Resolve issues

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: serosv
 Type: Package
 Title: Model Infectious Disease Parameters from Serosurveys
-Version: 1.2.0
+Version: 1.2.0.9000
 Authors@R: c(
     person("Anh", "Phan Truong Quynh", email = "anhptq@oucru.org", role = c("aut", "cre"), comment = c(ORCID = "0009-0000-2129-435X")),
     person("Nguyen", "Pham Nguyen The", email = "nguyenpnt@oucru.org", role = c("aut"), comment = c(ORCID = "0000-0002-0356-2776")),
@@ -40,6 +40,7 @@ Imports:
     rstan (>= 2.18.1),
     rstantools (>= 2.4.0),
     boot,
+    pROC,
     stats4
 Suggests: 
     covr,
@@ -49,16 +50,13 @@ Suggests:
     testthat (>= 3.0.0)
 Collate: 
     'data.R'
-    'mseir_model.R'
-    'sir_basic_model.R'
-    'sir_static_model.R'
-    'sir_subpops_model.R'
     'fractional_polynomial_models.R'
     'polynomial_models.R'
     'utils.R'
     'compare_models.R'
     'correct_prevalence.R'
     'weibull_model.R'
+    'farrington_model.R'
     'nonparametric.R'
     'semiparametric_models.R'
     'mixture_model.R'
@@ -69,6 +67,8 @@ Collate:
     'plots.R'
     'compute_ci.R'
     'age_time_model.R'
+    'predict.R'
+    'print.R'
 Config/testthat/edition: 3
 URL: https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv
 VignetteBuilder: knitr

NAMESPACE

@@ -1,40 +1,52 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(compute_ci,age_time_model)
+S3method(compute_ci,default)
+S3method(compute_ci,fp_model)
+S3method(compute_ci,hierarchical_bayesian_model)
+S3method(compute_ci,lp_model)
+S3method(compute_ci,mixture_model)
+S3method(compute_ci,penalized_spline_model)
+S3method(compute_ci,weibull_model)
 S3method(plot,age_time_model)
 S3method(plot,estimate_from_mixture)
 S3method(plot,farrington_model)
 S3method(plot,fp_model)
 S3method(plot,hierarchical_bayesian_model)
 S3method(plot,lp_model)
 S3method(plot,mixture_model)
-S3method(plot,mseir_model)
 S3method(plot,penalized_spline_model)
 S3method(plot,polynomial_model)
-S3method(plot,sir_basic_model)
-S3method(plot,sir_static_model)
-S3method(plot,sir_subpops_model)
 S3method(plot,weibull_model)
+S3method(predict,age_time_model)
+S3method(predict,farrington_model)
+S3method(predict,fp_model)
+S3method(predict,hierarchical_bayesian_model)
+S3method(predict,lp_model)
+S3method(predict,penalized_spline_model)
+S3method(predict,polynomial_model)
+S3method(predict,weibull_model)
+S3method(print,age_time_model)
+S3method(print,estimate_from_mixture)
+S3method(print,farrington_model)
+S3method(print,fp_model)
+S3method(print,hierarchical_bayesian_model)
+S3method(print,lp_model)
+S3method(print,mixture_model)
+S3method(print,penalized_spline_model)
+S3method(print,polynomial_model)
+S3method(print,weibull_model)
 export(add_thresholds)
 export(age_time_model)
 export(compare_models)
-export(compute_ci)
-export(compute_ci.age_time_model)
-export(compute_ci.fp_model)
-export(compute_ci.hierarchical_bayesian_model)
-export(compute_ci.lp_model)
-export(compute_ci.mixture_model)
-export(compute_ci.penalized_spline_model)
-export(compute_ci.weibull_model)
 export(correct_prevalence)
 export(est_foi)
 export(estimate_from_mixture)
 export(farrington_model)
-export(find_best_fp_powers)
 export(fp_model)
 export(hierarchical_bayesian_model)
 export(lp_model)
 export(mixture_model)
-export(mseir_model)
 export(pava)
 export(penalized_spline_model)
 export(plot_corrected_prev)
@@ -43,9 +55,6 @@ export(plot_standard_curve)
 export(plot_titer_qc)
 export(polynomial_model)
 export(set_plot_style)
-export(sir_basic_model)
-export(sir_static_model)
-export(sir_subpops_model)
 export(standardize_data)
 export(to_titer)
 export(transform_data)
@@ -55,15 +64,15 @@ import(assertthat)
 import(dplyr)
 import(ggplot2)
 import(graphics)
+import(locfit)
 import(methods)
+import(pROC)
 import(patchwork)
-import(purrr)
 import(scam)
 import(tidyr)
 importFrom(RcppParallel,RcppParallelLibs)
 importFrom(assertthat,assert_that)
 importFrom(boot,inv.logit)
-importFrom(deSolve,ode)
 importFrom(dplyr,group_by)
 importFrom(dplyr,mutate)
 importFrom(dplyr,n)
@@ -87,7 +96,17 @@ importFrom(mixdist,mix)
 importFrom(mixdist,mixgroup)
 importFrom(mixdist,mixparam)
 importFrom(mvtnorm,rmvnorm)
+importFrom(purrr,as_mapper)
+importFrom(purrr,compact)
 importFrom(purrr,imap_dfr)
+importFrom(purrr,map)
+importFrom(purrr,map2)
+importFrom(purrr,map_dbl)
+importFrom(purrr,map_dfc)
+importFrom(purrr,map_dfr)
+importFrom(purrr,pmap)
+importFrom(purrr,pmap_dfr)
+importFrom(purrr,walk)
 importFrom(rstan,sampling)
 importFrom(rstan,summary)
 importFrom(rstantools,rstan_config)
@@ -103,6 +122,9 @@ importFrom(stats,predict.glm)
 importFrom(stats,prop.test)
 importFrom(stats,qnorm)
 importFrom(stats,qt)
+importFrom(stats4,AIC)
+importFrom(stats4,BIC)
+importFrom(stats4,logLik)
 importFrom(stats4,mle)
 importFrom(stringr,str_detect)
 useDynLib(serosv, .registration=TRUE)

NEWS.md

@@ -1,4 +1,10 @@
+# serosv (development version)
+* clean up documentation
+
 # serosv 1.2.0
+* add to_titer() function to convert assay reading to titer
+* update the docs
+* update article for to_titer() on the website
 
 # serosv 1.1.0
 * add correct_prevalence() function to estimate real prevalence from imperfect test

R/age_time_model.R

@@ -3,15 +3,21 @@
 #'
 #' @description Fit age-stratified seroprevalence across multiple time points. Also try to monotonize age (or birth cohort) - specific seroprevalence.
 #'
-#' @param data - input data, must have`age`, `status`, time, group columns, where group column determines how data is aggregated
-#' @param time_col - name of the column for time (default to `date`)
-#' @param grouping_col - name of the column for time (default to `group`)
-#' @param age_correct - a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.
-#' @param le - number of bins to generate age grid, used when monotonizing data
-#' @param ci - confidence interval for smoothing
-#' @param monotonize_method - either "pava" or "scam"
+#' @param data input data, must have age, status, time, group columns, where group column determines how data is aggregated
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
+#' @param time_col name of the column for time (default to "date")
+#' @param grouping_col name of the column for time (default to "group")
+#' @param age_correct a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.
+#' @param le number of bins to generate age grid, used when monotonizing data
+#' @param ci confidence interval for smoothing
+#' @param monotonize_method either "pava" or "scam"
+#'
 #' @import scam assertthat
 #' @importFrom mgcv gam predict.gam betar
+#' @importFrom purrr map map_dbl map2
 #'
 #' @return a list of class time_age_model with 4 items
 #'   \item{out}{a data.frame with dimension n_group x 9, where columns `info`, `sp`, `foi` store output for non-monotonized
@@ -20,7 +26,10 @@
 #'   \item{age_correct}{a boolean indicating whether the data is monotonized across age or cohort}
 #'   \item{datatype}{whether the input data is aggregated or line-listing data}
 #' @export
-age_time_model <- function(data, time_col="date", grouping_col="group", age_correct=F, le=512, ci = 0.95, monotonize_method = "pava"){
+age_time_model <- function(data,
+                           age_col="age", status_col="status", pos_col="pos", tot_col="tot",
+                           time_col="date", grouping_col="group",
+                           age_correct=F, le=512, ci = 0.95, monotonize_method = "pava"){
   # work around to resolve no visible binding note NOTE during check()
   x <- label <- family <- fit <- se.fit <- ymin <- ymax <- y <- mean_time <- prevalence <- sim_data <- NULL
   age <- ys <- shift_no <- cohort <- col_time <- monotonized_mod <- df <- info <- sp <- monotonized_info <- monotonized_sp <- NULL
@@ -82,7 +91,8 @@ age_time_model <- function(data, time_col="date", grouping_col="group", age_corr
   model <- list()
 
   # --- preprocess data ------
-  check_input <- check_input(data)
+  check_input <- check_input(data,
+                             stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   age_range <- range(data$age)
   age_grid <- seq(age_range[1], age_range[2], length.out = le)
 
@@ -230,8 +240,10 @@ age_time_model <- function(data, time_col="date", grouping_col="group", age_corr
     )
 
   model$out <- out
+  model$monotonize_method <- monotonize_method
   model$grouping_col <- grouping_col
   model$age_correct <- age_correct
+  model$ci <- ci
 
 
   class(model) <- "age_time_model"