diff --git a/.Rbuildignore b/.Rbuildignore index 10ca984..a7ad1d3 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,3 +1,8 @@ +.*\.log +.*\.tex +.*_files +\.Rproj\.user +\.quarto ^.*\.Rproj$ ^CITATION\.cff$ ^LICENSE.md$ @@ -20,10 +25,10 @@ ^inst/*\.log$ ^inst/*css$ ^inst/*figure$ -^inst/*rds$ ^inst/intern_rapport$ ^inst/output$ ^inst/rapport$ +^inst/results.rds$ ^inst/website$ ^man-roxygen$ ^organisation.yml$ diff --git a/.github/workflows/check_on_branch.yml b/.github/workflows/check_on_branch.yml index 1ee5f63..bc635a0 100644 --- a/.github/workflows/check_on_branch.yml +++ b/.github/workflows/check_on_branch.yml @@ -18,5 +18,3 @@ jobs: contents: read steps: - uses: inbo/actions/check_pkg@main - with: - aptget: libpoppler-cpp-dev diff --git a/.github/workflows/check_on_different_r_os.yml b/.github/workflows/check_on_different_r_os.yml index 10c4a14..e769b5b 100644 --- a/.github/workflows/check_on_different_r_os.yml +++ b/.github/workflows/check_on_different_r_os.yml @@ -37,7 +37,7 @@ jobs: - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} - extra-repositories: https://inbo.r-universe.dev + extra-repositories: https://inbo.r-universe.dev https://inla.r-inla-download.org/R/stable - uses: r-lib/actions/setup-pandoc@v2 diff --git a/.github/workflows/check_on_main.yml b/.github/workflows/check_on_main.yml index 8c32ec6..6e8268d 100644 --- a/.github/workflows/check_on_main.yml +++ b/.github/workflows/check_on_main.yml @@ -17,5 +17,3 @@ jobs: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} steps: - uses: inbo/actions/check_pkg@main - with: - aptget: libpoppler-cpp-dev diff --git a/.zenodo.json b/.zenodo.json index 5d36bd3..01bf117 100644 --- a/.zenodo.json +++ b/.zenodo.json @@ -1,6 +1,6 @@ { "title": "abvanalysis: Analysis of the Common Breeding Bird Survey", - "version": "0.2.4", + "version": "0.2.5", "license": "GPL-3.0", "upload_type": "software", "description": "

Read in the raw data and prepare analysis datasets.<\/p>", diff --git a/CITATION.cff b/CITATION.cff index 9b3f914..1b06ab8 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -21,4 +21,4 @@ license: GPL-3.0 repository-code: https://github.com/inbo/abvanalysis/ type: software abstract: "Read in the raw data and prepare analysis datasets." -version: 0.2.4 +version: 0.2.5 diff --git a/DESCRIPTION b/DESCRIPTION index 3eb4843..9f85c74 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,17 +1,18 @@ Package: abvanalysis Title: Analysis of the Common Breeding Bird Survey -Version: 0.2.4 +Version: 0.2.5 Authors@R: c( person("Thierry", "Onkelinx", , "thierry.onkelinx@inbo.be", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8804-4216", affiliation = "Research Institute for Nature and Forest (INBO)")), - person("Research Institute for Nature and Forest (INBO)", , , "info@inbo.be", role = c("cph", "fnd")) + person("Research Institute for Nature and Forest (INBO)", , , "info@inbo.be", role = c("cph", "fnd"), + comment = c(ROR = "00j54wy13")) ) Description: Read in the raw data and prepare analysis datasets. License: GPL-3 URL: https://github.com/inbo/abvanalysis BugReports: https://github.com/inbo/abvanalysis/issues Depends: - R (>= 3.1.2) + R (>= 4.1.0) Imports: assertthat, DBI, @@ -25,17 +26,20 @@ Imports: stringr, tibble, tidyr -Suggests: +Suggests: + DT, effectclass, + gt, INBOmd, INBOtheme, knitr, pander, plotly, + quarto, rmarkdown, scales, tidyverse -VignetteBuilder: +VignetteBuilder: knitr Additional_repositories: https://inbo.r-universe.dev Config/checklist/communities: inbo @@ -44,4 +48,4 @@ Config/checklist/keywords: common breeding birds; monitoring; analysis; Encoding: UTF-8 Language: en-GB Roxygen: list(markdown = TRUE) -RoxygenNote: 7.3.2 +RoxygenNote: 7.3.3 diff --git a/LICENSE.md b/LICENSE.md index 2fb2e74..379c1b2 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,4 +1,4 @@ -### GNU GENERAL PUBLIC LICENSE +# GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 diff --git a/NAMESPACE b/NAMESPACE index ceb1e7a..f907217 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +export(copy_manifest_objects) export(get_field_id) export(get_linear_lincomb) export(get_nonlinear_lincomb) @@ -27,7 +28,6 @@ importFrom(assertthat,is.number) importFrom(assertthat,is.string) importFrom(assertthat,noNA) importFrom(digest,sha1) -importFrom(dplyr,"%>%") importFrom(dplyr,across) importFrom(dplyr,anti_join) importFrom(dplyr,arrange) @@ -35,6 +35,7 @@ importFrom(dplyr,bind_cols) importFrom(dplyr,bind_rows) importFrom(dplyr,count) importFrom(dplyr,distinct) +importFrom(dplyr,everything) importFrom(dplyr,filter) importFrom(dplyr,full_join) importFrom(dplyr,group_by) @@ -58,19 +59,24 @@ importFrom(git2rdata,push) importFrom(git2rdata,read_vc) importFrom(git2rdata,recent_commit) importFrom(git2rdata,rm_data) +importFrom(git2rdata,update_metadata) importFrom(git2rdata,verify_vc) importFrom(git2rdata,write_vc) importFrom(methods,slot) +importFrom(n2kanalysis,connect_inbo_s3) importFrom(n2kanalysis,display) importFrom(n2kanalysis,get_data) importFrom(n2kanalysis,get_file_fingerprint) importFrom(n2kanalysis,get_result) importFrom(n2kanalysis,get_status_fingerprint) +importFrom(n2kanalysis,manifest_yaml_to_bash) importFrom(n2kanalysis,n2k_composite) importFrom(n2kanalysis,n2k_inla) importFrom(n2kanalysis,n2k_manifest) +importFrom(n2kanalysis,read_manifest) importFrom(n2kanalysis,read_model) importFrom(n2kanalysis,status) +importFrom(n2kanalysis,store_manifest) importFrom(n2kanalysis,store_manifest_yaml) importFrom(n2kanalysis,store_model) importFrom(purrr,map) diff --git a/NEWS.md b/NEWS.md index 947a2dc..82de639 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,8 @@ +# abvanalysis 0.2.5 + +* Import only the observations from the target species. +* Provided more metadata about the imported data. + # abvanalysis 0.2.4 * Update checklist machinery diff --git a/R/copy_manifest_objects.R b/R/copy_manifest_objects.R new file mode 100644 index 0000000..e39992e --- /dev/null +++ b/R/copy_manifest_objects.R @@ -0,0 +1,44 @@ +#' Copy all object of the most recent manifest +#' @inheritParams n2kanalysis::store_model +#' @param target_folder Where to store the objects +#' @export +#' @importFrom dplyr filter +#' @importFrom n2kanalysis connect_inbo_s3 read_manifest read_model +#' store_manifest store_model +#' @importFrom rlang .data +copy_manifest_objects <- function( + base, + project, + target_folder = ".", + overwrite = FALSE +) { + manifest <- read_manifest(base = base, project = project) + store_manifest(manifest, base = target_folder, project = project) + manifest |> + slot("Manifest") |> + filter(is.na(.data$parent)) -> todo + file.path(target_folder, project) |> + list.files(recursive = TRUE) -> local_objects + if (overwrite) { + grepv("(converged|insufficient_data)", local_objects) -> local_objects + } + basename(local_objects) |> + gsub(pattern = "\\.rds$", replacement = "") -> done + for (hash in todo$fingerprint[!todo$fingerprint %in% done]) { + message(hash) + x <- try(read_model(hash, base = base, project = project)) + while (inherits(x, "try-error")) { + browser() + connect_inbo_s3() + x <- try(read_model(hash, base = base, project = project)) + } + store_model( + x, + base = target_folder, + project = project, + overwrite = overwrite + ) + rm(x) + gc() + } +} diff --git a/R/get_field_id.R b/R/get_field_id.R index 0a9164e..24d58aa 100644 --- a/R/get_field_id.R +++ b/R/get_field_id.R @@ -4,7 +4,7 @@ #' @param field_name The name of the field. #' @export #' @importFrom assertthat assert_that -#' @importFrom git2rdata read_vc write_vc +#' @importFrom git2rdata read_vc update_metadata write_vc get_field_id <- function(repo, table_name, field_name) { assert_that( inherits(repo, "git_repository"), is.string(table_name), @@ -19,6 +19,16 @@ get_field_id <- function(repo, table_name, field_name) { file = "database_id", root = repo, sorting = c("table", "field"), stage = TRUE ) + update_metadata( + file = "database_id", root = repo, stage = TRUE, name = "database_id", + title = "Database id for tables and fields", + description = + "This table describes to which table and field an id refers.", + field_description = c( + id = "Unique identifier", table = "Name of the table", + field = "Name of the field" + ) + ) return(1L) } @@ -40,5 +50,15 @@ get_field_id <- function(repo, table_name, field_name) { ) ) ) + update_metadata( + file = "database_id", root = repo, stage = TRUE, name = "database_id", + title = "Database id for tables and fields", + description = + "This table describes to which table and field an id refers.", + field_description = c( + id = "Unique identifier", table = "Name of the table", + field = "Name of the field" + ) + ) return(current_max + 1L) } diff --git a/R/get_linear_lincomb.R b/R/get_linear_lincomb.R index 3c6720e..14da332 100644 --- a/R/get_linear_lincomb.R +++ b/R/get_linear_lincomb.R @@ -3,8 +3,8 @@ #' @param formula the formula for the trend component #' @export #' @importFrom assertthat assert_that has_name is.string -#' @importFrom dplyr %>% across bind_cols bind_rows distinct group_by inner_join -#' mutate rename select summarise +#' @importFrom dplyr across bind_cols bind_rows distinct everything group_by +#' inner_join mutate rename select summarise #' @importFrom rlang := #' @importFrom stats model.matrix get_linear_lincomb <- function( @@ -20,16 +20,16 @@ get_linear_lincomb <- function( has_name(stratum_weights, "weight"), has_name(stratum_weights, stratum_var) ) - dataset %>% - select(c(stratum_var, time_var)) %>% - distinct() %>% + dataset |> + select(c(stratum_var, time_var)) |> + distinct() |> inner_join(stratum_weights, by = stratum_var) -> all_weight model.matrix(object = formula, all_weight) -> mm - (as.data.frame(mm) * all_weight$weight) %>% - mutate(ID = sprintf("linear_estimate_%02i", all_weight[[time_var]])) %>% - group_by(.data$ID) %>% - summarise(across(.fns = sum)) -> mm - stratum_weights %>% + (as.data.frame(mm) * all_weight$weight) |> + mutate(ID = sprintf("linear_estimate_%02i", all_weight[[time_var]])) |> + group_by(.data$ID) |> + summarise(across(.cols = everything(), .fns = sum)) -> mm + stratum_weights |> mutate("{time_var}" := 1) -> trend_weight # nolint: object_name_linter. mm_trend <- model.matrix(object = formula, trend_weight) mm_trend <- as.data.frame(mm_trend) * trend_weight$weight @@ -37,13 +37,13 @@ get_linear_lincomb <- function( if (has_name(mm_trend, "(Intercept)")) { mm_trend$`(Intercept)` <- 0 } - mm_trend %>% - summarise(across(.fns = sum)) %>% - mutate(ID = "linear_trend") %>% + mm_trend |> + summarise(across(.cols = everything(), .fns = sum)) |> + mutate(ID = "linear_trend") |> bind_rows(mm) -> weights w_names <- weights$ID - weights %>% - select(-"ID") %>% + weights |> + select(-"ID") |> as.list() -> weights names(weights[[1]]) <- w_names weights diff --git a/R/get_nonlinear_lincomb.R b/R/get_nonlinear_lincomb.R index 646e9c8..ab0916e 100644 --- a/R/get_nonlinear_lincomb.R +++ b/R/get_nonlinear_lincomb.R @@ -6,14 +6,14 @@ #' @param stratum_var variable containing the stratum information. #' @export #' @importFrom assertthat assert_that has_name is.string -#' @importFrom dplyr %>% arrange distinct filter inner_join mutate row_number +#' @importFrom dplyr arrange distinct filter inner_join mutate row_number #' select transmute #' @importFrom purrr map #' @importFrom rlang .data #' @importFrom stats setNames #' @importFrom tidyr expand_grid pivot_longer pivot_wider get_nonlinear_lincomb <- function( - dataset, stratum_weights, time_var, label_var, stratum_var + dataset, stratum_weights, time_var, label_var, stratum_var ) { assert_that(is.string(time_var), is.string(stratum_var), is.string(label_var)) assert_that( @@ -26,50 +26,50 @@ get_nonlinear_lincomb <- function( ) start <- min(dataset[[label_var]]) end <- max(dataset[[label_var]]) - seq(start, end, by = ifelse(time_var == "cycle", 3, 1)) %>% + seq(start, end, by = ifelse(time_var == "cycle", 3, 1)) |> length() -> n_total - dataset %>% - inner_join(stratum_weights, by = stratum_var) %>% - arrange(.data[[stratum_var]], .data[[time_var]]) %>% + dataset |> + inner_join(stratum_weights, by = stratum_var) |> + arrange(.data[[stratum_var]], .data[[time_var]]) |> mutate( - id = row_number() %>% + id = row_number() |> sprintf(fmt = "%04i") ) -> base_weights - base_weights %>% + base_weights |> pivot_wider( id_cols = label_var, names_from = .data$id, values_from = .data$weight, values_fill = 0 ) -> year_effect - dataset %>% - select(c(time_var, label_var)) %>% - distinct() %>% + dataset |> + select(c(time_var, label_var)) |> + distinct() |> arrange(.data[[time_var]]) -> time_steps strata <- sort(unique(dataset[[stratum_var]])) expand_grid( from = time_steps[[time_var]], to = time_steps[[time_var]], stratum = strata, to_s = strata - ) %>% - filter(.data$from < .data$to, .data$stratum == .data$to_s) %>% - select(-.data$to_s) %>% - inner_join(time_steps, by = c(from = time_var)) %>% - mutate(rowname = paste0("index_", .data$label, "_")) %>% - select(-.data$label) %>% - inner_join(time_steps, by = c(to = time_var)) %>% - mutate(rowname = paste0(.data$rowname, .data$label)) %>% - select(-.data$label) %>% + ) |> + filter(.data$from < .data$to, .data$stratum == .data$to_s) |> + select(-"to_s") |> + inner_join(time_steps, by = c(from = time_var)) |> + mutate(rowname = paste0("index_", .data$label, "_")) |> + select(-"label") |> + inner_join(time_steps, by = c(to = time_var)) |> + mutate(rowname = paste0(.data$rowname, .data$label)) |> + select(-"label") |> pivot_longer( c("from", "to"), names_to = "direction", values_to = time_var - ) %>% - inner_join(stratum_weights, by = stratum_var) %>% + ) |> + inner_join(stratum_weights, by = stratum_var) |> transmute( - id = interaction(.data[[time_var]], .data[[stratum_var]]) %>% - as.integer() %>% + id = interaction(.data[[time_var]], .data[[stratum_var]]) |> + as.integer() |> sprintf(fmt = "%04i"), .data$rowname, weight = ifelse(.data$direction == "to", 1, -1) * .data$weight - ) %>% - arrange(.data$id, .data$rowname) %>% + ) |> + arrange(.data$id, .data$rowname) |> pivot_wider( names_from = .data$id, values_from = .data$weight, values_fill = 0 ) -> indices @@ -83,18 +83,18 @@ get_nonlinear_lincomb <- function( n_window = ifelse(time_var == "cycle", 4, 12) ), moving_trend(n_total = n_total, n_window = n_total, start = start) - ) %>% - map(.x = stratum_weights$weight, .f = `*`) %>% + ) |> + map(.x = stratum_weights$weight, .f = `*`) |> do.call(what = "cbind") -> trend_coef - select(year_effect, -.data$label) %>% - bind_rows(select(indices, -.data$rowname)) %>% - as.matrix() %>% + select(year_effect, -"label") |> + bind_rows(select(indices, -"rowname")) |> + as.matrix() |> `row.names<-`( c(sprintf("estimate_%i", year_effect$label), indices$rowname) - ) %>% - rbind(trend_coef) %>% - list() %>% - setNames(time_var) %>% + ) |> + rbind(trend_coef) |> + list() |> + setNames(time_var) |> c(list( `(Intercept)` = c( rep(1, nrow(year_effect)), rep(0, nrow(indices) + nrow(trend_coef)) diff --git a/R/prepare_analysis.R b/R/prepare_analysis.R index 6c21ff0..06e71fb 100755 --- a/R/prepare_analysis.R +++ b/R/prepare_analysis.R @@ -16,47 +16,65 @@ #' left_join mutate pull select transmute #' @importFrom git2rdata verify_vc #' @importFrom methods slot -#' @importFrom n2kanalysis display get_file_fingerprint n2k_manifest -#' store_manifest_yaml +#' @importFrom n2kanalysis display get_file_fingerprint manifest_yaml_to_bash +#' n2k_manifest store_manifest_yaml #' @importFrom purrr map map_chr pmap_dfr #' @importFrom rlang .data #' @importFrom tidyr complete nest replace_na unnest prepare_analysis <- function( - repo, base, project, overwrite = FALSE, seed = 20070315, verbose = TRUE, - min_observation = 100, min_stratum = 3, min_cycle = 2, proportion = 0.15, - scheme_id = "ABV", volume, - docker = "inbobmk/rn2k:0.7", dependencies = c( - "inbo/n2khelper@v0.4.3", "inbo/n2kanalysis@v0.2.9" + repo, + base, + project, + overwrite = FALSE, + seed = 20070315, + verbose = TRUE, + min_observation = 100, + min_stratum = 3, + min_cycle = 2, + proportion = 0.15, + scheme_id = "ABV", + volume, + docker = "inbobmk/rn2k:0.7", + dependencies = c( + "inbo/n2khelper@v0.4.3", + "inbo/n2kanalysis@v0.2.9" ) ) { display(verbose, "Read data from repository") raw_visit <- verify_vc( - file = file.path("observation", "visit"), root = repo, + file = file.path("observation", "visit"), + root = repo, variables = c("id", "year", "period", "point_id", "datafield_id") ) raw_point <- verify_vc( - file = file.path("location", "point"), root = repo, + file = file.path("location", "point"), + root = repo, variables = c("id", "description", "square_id") ) raw_square <- verify_vc( - file = file.path("location", "square"), root = repo, + file = file.path("location", "square"), + root = repo, variables = c("id", "description", "stratum_id") ) raw_stratum <- verify_vc( - file = file.path("location", "stratum"), root = repo, + file = file.path("location", "stratum"), + root = repo, variables = c("id", "description", "n") ) raw_speciesgroup <- verify_vc( - file = file.path("species", "speciesgroup"), root = repo, + file = file.path("species", "speciesgroup"), + root = repo, variables = c("id", "description") ) raw_speciesgroup_species <- verify_vc( - file = file.path("species", "speciesgroup_species"), root = repo, + file = file.path("species", "speciesgroup_species"), + root = repo, variables = c("speciesgroup_id", "parent_id", "species") ) raw_distribution <- verify_vc( - file = file.path("distribution", "distribution"), root = repo, + file = file.path("distribution", "distribution"), + root = repo, variables = c("species_id", "family") ) @@ -64,12 +82,15 @@ prepare_analysis <- function( raw_square |> transmute( - square_id = .data$id, .data$stratum_id, square = factor(.data$description) + square_id = .data$id, + .data$stratum_id, + square = factor(.data$description) ) |> inner_join( raw_stratum |> transmute( - stratum_id = .data$id, stratum = factor(.data$description), + stratum_id = .data$id, + stratum = factor(.data$description), .data$n ), by = "stratum_id" @@ -77,22 +98,31 @@ prepare_analysis <- function( inner_join( raw_point |> transmute( - point_id = .data$id, point = factor(.data$description), + point_id = .data$id, + point = factor(.data$description), .data$square_id ), by = "square_id" ) |> inner_join(raw_visit, by = "point_id") |> - select(-"square_id", -"stratum_id", -"point_id", sample_id = "id") -> - observation + select( + -"square_id", + -"stratum_id", + -"point_id", + sample_id = "id" + ) -> observation + file.path(repo$path, "..", "observation") |> + list.files(pattern = "[0-9]+\\.tsv$") |> + gsub(pattern = "\\.tsv$", replacement = "") |> + as.integer() -> available_species raw_speciesgroup_species |> - filter(.data$species) |> + filter(.data$species, .data$parent_id %in% available_species) |> left_join(raw_distribution, by = c("parent_id" = "species_id")) |> inner_join(raw_speciesgroup, by = c("speciesgroup_id" = "id")) |> transmute( species_group_id = .data$speciesgroup_id, species = sprintf("%05i", .data$external_id), - family = as.character(.data$family) %>% + family = as.character(.data$family) |> replace_na("poisson"), ) |> arrange(.data$species_group_id) -> speciesgroup @@ -100,131 +130,85 @@ prepare_analysis <- function( display(verbose, "Prepare analysis per species") pmap_dfr( - as.list(speciesgroup), prepare_analysis_dataset, scheme_id = scheme_id, - location_group_id = "Vlaanderen", seed = seed, observation = observation, - repo = repo, base = base, project = project, overwrite = overwrite, - min_observation = min_observation, min_stratum = min_stratum, - min_cycle = min_cycle, proportion = proportion, verbose = verbose + as.list(speciesgroup), + prepare_analysis_dataset, + scheme_id = scheme_id, + location_group_id = "Vlaanderen", + seed = seed, + observation = observation, + repo = repo, + base = base, + project = project, + overwrite = overwrite, + min_observation = min_observation, + min_stratum = min_stratum, + min_cycle = min_cycle, + proportion = proportion, + verbose = verbose ) -> base_analysis display(verbose, "Prepare model composite") base_analysis |> - inner_join(raw_speciesgroup_species, by = "parent_id") |> + inner_join( + raw_speciesgroup_species, + by = "parent_id", + relationship = "many-to-many" + ) |> select(-"parent_id", -"species", species_group_id = "speciesgroup_id") |> mutate(species_group_id = as.character(.data$species_group_id)) |> nest( models = c( - .data$fingerprint, .data$status_fingerprint, .data$status, - .data$result_datasource, .data$first_imported_year, - .data$last_imported_year, .data$analysis_date + "fingerprint", + "status_fingerprint", + "status", + "result_datasource", + "first_imported_year", + "last_imported_year", + "analysis_date" ) ) |> arrange(.data$species_group_id, .data$frequency, .data$type) -> composite pmap_dfr( - as.list(composite), prepare_analysis_composite, scheme_id = scheme_id, - location_group_id = "Vlaanderen", seed = seed, verbose = verbose, - base = base, project = project, overwrite = overwrite + as.list(composite), + prepare_analysis_composite, + scheme_id = scheme_id, + location_group_id = "Vlaanderen", + seed = seed, + verbose = verbose, + base = base, + project = project, + overwrite = overwrite ) |> bind_cols(composite) -> composite display(verbose, "Prepare manifests") + composite |> transmute( - location_group = "Vlaanderen", species_group = .data$species_group_id, - .data$frequency, .data$type, .data$fingerprint, + location_group = "Vlaanderen", + species_group = .data$species_group_id, + .data$frequency, + .data$type, + .data$fingerprint, data = map(.data$models, select, parent = "fingerprint") - ) %>% + ) |> unnest(cols = "data") -> meta_composite - meta_composite |> - select(-"fingerprint", fingerprint = "parent") |> - bind_rows(meta_composite) |> - nest(data = c("fingerprint", "parent")) |> - mutate(manifest = map(.data$data, n2k_manifest)) -> manifest_composite - base_analysis |> - anti_join(meta_composite, by = c("fingerprint" = "parent")) |> - transmute(.data$parent_id, .data$fingerprint, parent = NA_character_) |> - nest(data = c("fingerprint", "parent")) |> - pull(.data$data) |> - map(n2k_manifest) |> - c(manifest_composite$manifest) |> - map( - store_manifest_yaml, base = base, project = project, docker = docker, - dependencies = dependencies - ) - - manifest_composite |> - transmute(manifest = map(.data$manifest, slot, "Manifest")) |> - unnest(cols = .data$manifest) |> - mutate(parent = !is.na(.data$parent)) |> - distinct(.data$fingerprint, .data$parent) |> - arrange(.data$parent, .data$fingerprint) |> - pull(.data$fingerprint) -> models - - manifests <- map_chr(manifest_composite$manifest, get_file_fingerprint) - - args <- ", dependencies = FALSE, upgrade = \\\"never\\\", keep_source = FALSE" - docker_hash <- sha1(manifests) - sprintf( - "Rscript -e 'remotes::install_github(\\\"%s\\\"%s)'", dependencies, args - ) -> deps - sprintf( - "#!/bin/bash -echo \"FROM %s -RUN %s\" > Dockerfile -docker build --pull --tag rn2k:%s . -rm Dockerfile", - docker, paste(deps, collapse = " \\\n&& "), docker_hash - ) -> init - - if (inherits(base, "s3_bucket")) { - sprintf( - "echo \"model %i of %i\" -docker run %s --name=%s rn2k:%s ./fit_model_aws.sh -b %s -p %s -m %s -date -docker stop --time 14400 %s -date", - seq_along(models), length(models), "--rm -d --env-file ./env.list", - models, docker_hash, attr(base, "Name"), project, models, models - ) -> model_scripts - - sprintf( - "echo \"manifest %i of %i\" -docker run %s --name=%s rn2k:%s ./fit_model_aws.sh -b %s -p %s -m %s -date -docker stop --time 14400 %s -date", - seq_along(manifests), length(manifests), "--rm -d --env-file ./env.list", - manifests, docker_hash, attr(base, "Name"), project, - paste0(manifests, ".manifest"), manifests - ) -> manifest_scripts - } else { - base <- normalizePath(base, winslash = "/") - if (missing(volume)) { - volume <- paste(base, base, "rw", sep = ":") - } - sprintf( - "echo \"model %i of %i\" -docker run %s --name=%s -v %s rn2k:%s ./fit_model_file.sh -b %s -p %s -m %s -date -docker stop --time 14400 %s -date", - seq_along(models), length(models), "--rm -d", models, volume, docker_hash, - base, project, models, models - ) -> model_scripts - - sprintf( - "echo \"manifest %i of %i\" -docker run %s --name=%s -v %s rn2k:%s ./fit_model_file.sh -b %s -p %s -m %s -date -docker stop --time 14400 %s -date", - seq_along(manifests), length(manifests), "--rm -d", manifests, volume, - docker_hash, base, project, paste0(manifests, ".manifest"), manifests - ) -> manifest_scripts - } - - return(list(init = init, models = model_scripts, manifest = manifest_scripts)) + select("fingerprint") |> + bind_rows(meta_composite) |> + n2k_manifest() |> + store_manifest_yaml( + base = base, + project = project, + docker = docker, + dependencies = dependencies, + overwrite = TRUE + ) -> yaml + manifest_yaml_to_bash( + base = base, + project = project, + hash = basename(yaml) + ) } diff --git a/R/prepare_analysis_composite.R b/R/prepare_analysis_composite.R index ca2e8a1..fc460db 100644 --- a/R/prepare_analysis_composite.R +++ b/R/prepare_analysis_composite.R @@ -8,7 +8,7 @@ #' @export #' @importFrom assertthat assert_that is.string has_name #' @importFrom n2kanalysis n2k_composite -#' @importFrom dplyr %>% select mutate arrange filter slice +#' @importFrom dplyr select mutate arrange filter slice #' @importFrom rlang .data #' @importFrom tibble rownames_to_column prepare_analysis_composite <- function( @@ -35,20 +35,20 @@ prepare_analysis_composite <- function( extractor <- function(model) { stopifnot(requireNamespace("tibble", quietly = TRUE)) stopifnot(requireNamespace("dplyr", quietly = TRUE)) - model$summary.lincomb.derived %>% - tibble::rownames_to_column("value") %>% - dplyr::select("value", estimate = "mean", variance = "sd") %>% + model$summary.lincomb.derived |> + tibble::rownames_to_column("value") |> + dplyr::select("value", estimate = "mean", variance = "sd") |> dplyr::mutate( variance = .data$variance ^ 2, number = suppressWarnings(as.integer(.data$value)) - ) %>% - dplyr::arrange(.data$number, .data$value) %>% + ) |> + dplyr::arrange(.data$number, .data$value) |> dplyr::select(-"number") } n2k_composite( result_datasource_id = models$result_datasource[1], - parent_status = models %>% + parent_status = models |> select( parent_analysis = "fingerprint", parent_status = "status", parentstatus_fingerprint = "status_fingerprint" @@ -60,6 +60,6 @@ prepare_analysis_composite <- function( model_type = paste("composite index:", frequency, type), formula = paste("~", frequency), extractor = extractor, analysis_date = max(models$analysis_date), status = "waiting" - ) %>% + ) |> storage(base = base, project = project, overwrite = overwrite) } diff --git a/R/prepare_analysis_dataset.R b/R/prepare_analysis_dataset.R index 4db9787..0fa85e6 100644 --- a/R/prepare_analysis_dataset.R +++ b/R/prepare_analysis_dataset.R @@ -125,8 +125,8 @@ prepare_analysis_dataset <- function( hyper = list(theta = list(prior = \"pc.prec\", param = c(1, 0.01))))", "f(point, model = \"iid\", hyper = list(theta = list(prior = \"pc.prec\", param = c(1, 0.01))))"[ - length(levels(dataset$point)) > 2 * length(levels(dataset$square)) - ] + length(levels(dataset$point)) > 2 * length(levels(dataset$square)) + ] ) # analysis on data by year @@ -178,8 +178,9 @@ prepare_analysis_dataset <- function( stratum_weights = stratum_weights, time_var = "cyear", label_var = "label", stratum_var = "stratum" ) -> lin_comb - c(base_effects, - "f(cyear, model = \"rw1\", replicate = as.integer(stratum), + c( + base_effects, + "f(cyear, model = \"rw1\", replicate = as.integer(stratum), hyper = list(theta = list(prior = \"pc.prec\", param = c(0.5, 0.01))))" ) |> paste(collapse = " + ") |> diff --git a/R/prepare_dataset_location.R b/R/prepare_dataset_location.R index 20fde2e..41d5b99 100755 --- a/R/prepare_dataset_location.R +++ b/R/prepare_dataset_location.R @@ -2,33 +2,33 @@ #' @inheritParams prepare_dataset #' @inheritParams git2rdata::write_vc #' @importFrom DBI dbGetQuery dbQuoteIdentifier dbQuoteString Id -#' @importFrom dplyr %>% arrange count filter full_join inner_join mutate -#' row_number transmute +#' @importFrom dplyr arrange count filter full_join inner_join mutate +#' row_number select transmute #' @importFrom rlang .data -#' @importFrom git2rdata prune_meta read_vc rm_data write_vc +#' @importFrom git2rdata prune_meta read_vc rm_data update_metadata write_vc #' @export prepare_dataset_location <- function( - origin, repo, end_date, strict = TRUE, db_scheme = "" + origin, repo, end_date, strict = TRUE, db_scheme = "" ) { + strata <- try( + read_vc(file.path("location", "stratum"), root = repo), silent = TRUE + ) rm_data(root = repo, path = "location", stage = TRUE) # import sampling framework sampling_frame <- read_vc("sampling_frame", repo) - sampling_frame %>% - count(description = .data$Stratum) %>% - filter(!is.na(.data$description)) %>% - arrange(.data$description) %>% + sampling_frame |> + count(description = .data$Stratum) |> + filter(!is.na(.data$description)) |> + arrange(.data$description) |> mutate(description = as.character(.data$description)) -> new_strata - strata <- try( - read_vc(file.path("location", "stratum"), root = repo), silent = TRUE - ) if (inherits(strata, "try-error")) { - new_strata %>% + new_strata |> mutate(id = row_number()) -> strata } else { - strata %>% - full_join(new_strata, by = "description") %>% - arrange(.data$id) %>% + strata |> + full_join(new_strata, by = "description") |> + arrange(.data$id) |> transmute( .data$description, n = ifelse(is.na(.data$id), .data$n.y, .data$n.x), @@ -43,17 +43,28 @@ prepare_dataset_location <- function( strata, file = file.path("location", "stratum"), root = repo, sorting = "description", stage = TRUE, strict = strict ) - strata %>% - select(stratum = .data$description, stratum_id = .data$id) %>% + update_metadata( + file = file.path("location", "stratum"), root = repo, name = "stratum", + title = "Strata using in the Common Breeding Bird Cencus in Flanders", + field_description = c( + description = "Name of the stratum", + n = "Total number of squares in the stratum", + id = "Unique identifier of the stratum" + ), + stage = TRUE + ) + + strata |> + select(stratum = "description", stratum_id = "id") |> inner_join( - sampling_frame %>% + sampling_frame |> transmute( description = sprintf("ABV_%s", .data$ExternalCode), stratum = .data$Stratum ), by = c("stratum") - ) %>% - select(-.data$stratum) -> strata + ) |> + select(-"stratum") -> strata # import UTM squares sprintf(" @@ -88,18 +99,29 @@ prepare_dataset_location <- function( dbQuoteIdentifier( origin, Id(scheme = db_scheme, table = "locations_location") ) - ) %>% - dbGetQuery(conn = origin) %>% - inner_join(strata, by = "description") %>% + ) |> + dbGetQuery(conn = origin) |> + inner_join(strata, by = "description") |> mutate( datafield_id = get_field_id( repo = repo, table_name = "locations_location", field_name = "id" ) - ) %>% + ) |> write_vc( file = file.path("location", "square"), root = repo, sorting = "description", stage = TRUE, strict = strict ) + update_metadata( + file = file.path("location", "square"), root = repo, stage = TRUE, + name = "square", + title = "UTM squares used in the Common Breeding Bird Cencus in Flanders", + field_description = c( + description = "Name of the UTM square", + id = "Unique identifier of the UTM square", + stratum_id = "Unique identifier of the stratum", + datafield_id = "Unique identifier of the datafield" + ) + ) # import points sprintf(" @@ -134,17 +156,28 @@ prepare_dataset_location <- function( dbQuoteIdentifier( origin, Id(scheme = db_scheme, table = "locations_location") ) - ) %>% - dbGetQuery(conn = origin) %>% + ) |> + dbGetQuery(conn = origin) |> mutate( datafield_id = get_field_id( repo = repo, table_name = "locations_location", field_name = "id" ) - ) %>% + ) |> write_vc( file = file.path("location", "point"), root = repo, stage = TRUE, sorting = "description", strict = strict ) + update_metadata( + file = file.path("location", "point"), root = repo, stage = TRUE, + name = "point", + title = "Points used in the Common Breeding Bird Cencus in Flanders", + field_description = c( + description = "Name of the point", + id = "Unique identifier of the point", + square_id = "Unique identifier of the UTM square", + datafield_id = "Unique identifier of the datafield" + ) + ) prune_meta(root = repo, path = "location", stage = TRUE) diff --git a/R/prepare_dataset_observation.R b/R/prepare_dataset_observation.R index 6d6ee11..fb86184 100755 --- a/R/prepare_dataset_observation.R +++ b/R/prepare_dataset_observation.R @@ -1,9 +1,9 @@ #' Read the observations and save them to git and the results database #' @inheritParams prepare_dataset #' @export -#' @importFrom git2rdata rm_data write_vc prune_meta +#' @importFrom git2rdata prune_meta rm_data update_metadata write_vc #' @importFrom DBI dbGetQuery dbQuoteIdentifier dbQuoteString Id -#' @importFrom dplyr %>% inner_join rename semi_join select group_by filter +#' @importFrom dplyr inner_join rename semi_join select group_by filter #' mutate pull #' @importFrom tidyr nest #' @importFrom purrr map2 map_int @@ -28,14 +28,14 @@ prepare_dataset_observation <- function( fv.start_date <= %s AND fs.not_counted = %s AND %s", - ifelse( - inherits(origin, "Microsoft SQL Server"), - "YEAR(fv.start_date)", - "EXTRACT(YEAR FROM fv.start_date)" - ), - ifelse( - inherits(origin, "Microsoft SQL Server"), - "CASE + ifelse( + inherits(origin, "Microsoft SQL Server"), + "YEAR(fv.start_date)", + "EXTRACT(YEAR FROM fv.start_date)" + ), + ifelse( + inherits(origin, "Microsoft SQL Server"), + "CASE WHEN fv.start_date < CAST( CAST(YEAR(fv.start_date) AS VARCHAR) + '-04-16' AS DATETIME @@ -48,40 +48,40 @@ prepare_dataset_observation <- function( THEN 2 ELSE 3 END", - "CASE + "CASE WHEN fv.start_date < CAST(EXTRACT(YEAR FROM fv.start_date) || '-04-16' AS TIMESTAMP) THEN 1 WHEN fv.start_date < CAST(EXTRACT(YEAR FROM fv.start_date) || '-06-01' AS TIMESTAMP) THEN 2 ELSE 3 END" - ), - dbQuoteIdentifier( - origin, Id(scheme = db_scheme, table = "projects_project") - ), - dbQuoteIdentifier( - origin, Id(scheme = db_scheme, table = "fieldwork_visit") - ), - dbQuoteIdentifier( - origin, Id(scheme = db_scheme, table = "fieldwork_sample") - ), - dbQuoteString(origin, as.character(end_date)), - ifelse(inherits(origin, "Microsoft SQL Server"), "0", "FALSE"), - ifelse( - inherits(origin, "Microsoft SQL Server"), - "CAST( + ), + dbQuoteIdentifier( + origin, Id(scheme = db_scheme, table = "projects_project") + ), + dbQuoteIdentifier( + origin, Id(scheme = db_scheme, table = "fieldwork_visit") + ), + dbQuoteIdentifier( + origin, Id(scheme = db_scheme, table = "fieldwork_sample") + ), + dbQuoteString(origin, as.character(end_date)), + ifelse(inherits(origin, "Microsoft SQL Server"), "0", "FALSE"), + ifelse( + inherits(origin, "Microsoft SQL Server"), + "CAST( CAST(YEAR(fv.start_date) AS VARCHAR) + '-3-1' AS DATETIME ) <= fv.start_date AND fv.start_date <= CAST( CAST(YEAR(fv.start_date) AS VARCHAR) + '-7-16' AS DATETIME )", - "CAST(EXTRACT(YEAR FROM fv.start_date) || '-3-1' AS TIMESTAMP) <= + "CAST(EXTRACT(YEAR FROM fv.start_date) || '-3-1' AS TIMESTAMP) <= fv.start_date AND fv.start_date <= CAST(EXTRACT(YEAR FROM fv.start_date) || '-7-16' AS TIMESTAMP)" - ) + ) ) |> - dbGetQuery(conn = origin) %>% + dbGetQuery(conn = origin) |> mutate( datafield_id = get_field_id( repo = repo, table_name = "fieldwork_sample", field_name = "id" @@ -92,6 +92,17 @@ END" sorting = c("year", "period", "point_id", "id"), strict = strict ) + update_metadata( + file = "observation/visit", root = repo, stage = TRUE, name = "visit", + title = "Visits in the Common Breeding Bird Census in Flanders", + field_description = c( + id = "Unique identifier of the visit", + point_id = "Unique identifier of the point", + year = "Year of the visit", + period = "Period of the visit", + datafield_id = "Unique identifier of the datafield" + ) + ) # counts per species sprintf(" @@ -135,26 +146,46 @@ END" fv.start_date <= CAST(EXTRACT(YEAR FROM fv.start_date) || '-7-16' AS TIMESTAMP)" ) - ) %>% + ) |> dbGetQuery(conn = origin) -> counts - counts %>% - semi_join(observations, by = c("sample_id" = "id")) %>% + counts |> + semi_join(observations, by = c("sample_id" = "id")) |> mutate( count = as.integer(count), datafield_id = get_field_id( repo = repo, table_name = "fieldwork_sample", field_name = "id" ) - ) %>% - group_by(.data$species_id) %>% - nest() %>% - mutate(n = map_int(.data$data, nrow)) %>% - filter(.data$n >= min_observation) %>% + ) |> + group_by(.data$species_id) |> + nest() |> + mutate(n = map_int(.data$data, nrow)) |> + filter(.data$n >= min_observation) |> + inner_join( + file.path("species", "species") |> + read_vc(root = repo) |> + select("id", "scientific_name"), + by = c("species_id" = "id") + ) |> mutate( filename = file.path("observation", sprintf("%05i", .data$species_id)), hash = map2( .data$data, .data$filename, write_vc, root = repo, stage = TRUE, sorting = "sample_id", strict = strict ) + ) |> + mutate( + metadata = map2( + .data$filename, .data$scientific_name, + ~update_metadata( + .x, root = repo, stage = TRUE, + name = .data$filename, title = paste("Observations of", .y), + field_description = c( + sample_id = "Unique identifier of the sample", + count = "Number of individuals", + datafield_id = "Identifier of the datafield" + ) + ) + ) ) prune_meta(root = repo, path = "observation", stage = TRUE) diff --git a/R/prepare_dataset_species.R b/R/prepare_dataset_species.R index 33b24e2..260a837 100755 --- a/R/prepare_dataset_species.R +++ b/R/prepare_dataset_species.R @@ -3,29 +3,29 @@ #' @inheritParams git2rdata::write_vc #' @importFrom DBI dbGetQuery dbQuoteLiteral dbQuoteString Id #' @importFrom digest sha1 -#' @importFrom dplyr %>% transmute select mutate bind_rows +#' @importFrom dplyr bind_rows mutate select transmute #' @importFrom purrr map_chr #' @importFrom rlang .data -#' @importFrom git2rdata prune_meta rm_data write_vc +#' @importFrom git2rdata prune_meta rm_data update_metadata write_vc #' @export prepare_dataset_species <- function( origin, repo, end_date, strict = FALSE, db_scheme = "" ) { - rm_data(root = repo, path = "location", stage = TRUE) + rm_data(root = repo, path = "species", stage = TRUE) sprintf(" WITH cte AS ( - SELECT fo.species_id + SELECT ps.species_id FROM %s AS pp + INNER JOIN %s AS ps ON pp.id = ps.project_id INNER JOIN %s AS fv ON pp.id = fv.project_id INNER JOIN %s AS fs ON fv.id = fs.visit_id INNER JOIN %s AS fo ON fs.id = fo.sample_id WHERE pp.name = 'Algemene Broedvogelmonitoring (ABV)' AND fv.validation_status != -1 AND - fv.start_date <= %s AND - fs.not_counted = %s - GROUP BY species_id + fv.start_date <= %s AND fs.not_counted = %s + GROUP BY ps.species_id ) SELECT @@ -36,6 +36,9 @@ prepare_dataset_species <- function( dbQuoteIdentifier( origin, Id(scheme = db_scheme, table = "projects_project") ), + dbQuoteIdentifier( + origin, Id(scheme = db_scheme, table = "projects_projectspecies") + ), dbQuoteIdentifier( origin, Id(scheme = db_scheme, table = "fieldwork_visit") ), @@ -50,8 +53,8 @@ prepare_dataset_species <- function( dbQuoteIdentifier( origin, Id(scheme = db_scheme, table = "species_species") ) - ) %>% - dbGetQuery(conn = origin) %>% + ) |> + dbGetQuery(conn = origin) |> mutate( datafield_id = get_field_id( repo = repo, table_name = "species_species", field_name = "id" @@ -59,7 +62,18 @@ prepare_dataset_species <- function( ) -> species write_vc( species, file = file.path("species", "species"), root = repo, - sorting = "euring", stage = TRUE + sorting = "euring", strict = FALSE + ) + update_metadata( + file = file.path("species", "species"), root = repo, stage = TRUE, + name = "species", title = "Species in the Common Breeding Bird Census", + field_description = c( + id = "Unique identifier of the species", + scientific_name = "Scientific name of the species", + nl = "Dutch name of the species", + euring = "Euring code of the species", + datafield_id = "Unique identifier of the datafield" + ) ) sprintf( "SELECT @@ -72,7 +86,7 @@ prepare_dataset_species <- function( origin, Id(scheme = db_scheme, table = "species_group") ) ) |> - dbGetQuery(conn = origin) %>% + dbGetQuery(conn = origin) |> mutate( description = gsub(" \\(.*\\)", "", .data$description), datafield_id = get_field_id( @@ -80,7 +94,7 @@ prepare_dataset_species <- function( ), id = map_chr(.data$description, sha1) ) -> speciesgroup - species %>% + species |> transmute( external_id = .data$id, id = map_chr(.data$nl, sha1), @@ -88,7 +102,7 @@ prepare_dataset_species <- function( .data$datafield_id ) -> speciesgroup2 sprintf( - "SELECT + "SELECT sg.id AS external_id, sgs.species_id AS species_id FROM %s AS sg @@ -101,44 +115,67 @@ WHERE sg.name LIKE '%% (ABV)'", origin, Id(scheme = db_scheme, table = "species_speciesgrouprelation") ) ) |> - dbGetQuery(conn = origin) |> + dbGetQuery(conn = origin) |> inner_join( - speciesgroup %>% - select(speciesgroup_id = .data$id, .data$external_id), + speciesgroup |> + select(speciesgroup_id = "id", "external_id"), by = "external_id" - ) %>% + ) |> inner_join( - speciesgroup2 %>% - select(species_id = .data$external_id, parent_id = .data$id), + speciesgroup2 |> + select(species_id = "external_id", parent_id = "id"), by = "species_id" - ) %>% - transmute(.data$speciesgroup_id, .data$parent_id, species = FALSE) %>% + ) |> + transmute(.data$speciesgroup_id, .data$parent_id, species = FALSE) |> bind_rows( - species %>% - select(external_id = .data$id, parent = .data$id) %>% + species |> + select(external_id = "id", parent = "id") |> inner_join( - speciesgroup2 %>% - select(speciesgroup_id = .data$id, .data$external_id), + speciesgroup2 |> + select(speciesgroup_id = "id", "external_id"), by = "external_id" - ) %>% + ) |> transmute( .data$speciesgroup_id, parent_id = as.character(.data$parent), species = TRUE ) - ) %>% + ) |> mutate( speciesgroup_id = factor(.data$speciesgroup_id), parent_id = factor(.data$parent_id) - ) %>% + ) |> write_vc( file = file.path("species", "speciesgroup_species"), root = repo, sorting = c("speciesgroup_id", "parent_id"), stage = TRUE, strict = strict ) - bind_rows(speciesgroup, speciesgroup2) %>% + update_metadata( + file = file.path("species", "speciesgroup_species"), root = repo, + stage = TRUE, name = "speciesgroup_species", + title = "Species per species groups", + description = + "This table describes which species belong to which species group.", + field_description = c( + speciesgroup_id = "Unique identifier of the species group", + parent_id = "Unique identifier of the parent species group", + species = "Is this a species or a species group?" + ) + ) + bind_rows(speciesgroup, speciesgroup2) |> write_vc( file = file.path("species", "speciesgroup"), root = repo, sorting = "id", stage = TRUE, strict = strict ) + update_metadata( + file = file.path("species", "speciesgroup"), root = repo, stage = TRUE, + name = "speciesgroup", + title = "Species groups in the Common Breeding Bird Census", + field_description = c( + id = "Unique identifier of the species group", + external_id = "External identifier of the species group", + description = "Name of the species group", + datafield_id = "Unique identifier of the datafield" + ) + ) prune_meta(root = repo, path = "species", stage = TRUE) diff --git a/R/read_relevant.R b/R/read_relevant.R new file mode 100644 index 0000000..b4f12e7 --- /dev/null +++ b/R/read_relevant.R @@ -0,0 +1,90 @@ +read_relevant <- function(base, project, verbose = TRUE) { + UseMethod("read_relevant", base) +} + +#' @importFrom dplyr mutate +#' @importFrom n2kanalysis display +#' @importFrom purrr map_dfr +#' @importFrom rlang .data +#' @importFrom stringr str_remove str_subset +read_relevant.character <- function(base, project, verbose = TRUE) { + file.path(base, project) |> + list.files(pattern = "rds$", recursive = TRUE, full.names = TRUE) |> + str_subset("converged") -> todo + map_dfr(todo, verbose = verbose, function(i, verbose) { + display(verbose, i) + readRDS(i) |> + extract_relevant() + }) |> + mutate(analysis = factor(.data$analysis)) +} + +#' @importFrom dplyr filter transmute +#' @importFrom n2kanalysis display read_manifest +#' @importFrom purrr map_dfr +#' @importFrom stringr str_subset +#' @importFrom rlang .data +read_relevant.s3_bucket <- function(base, project, verbose = TRUE) { + read_manifest(base = base, project = project) |> + slot("Manifest") |> + filter(is.na(.data$parent)) -> todo + map_dfr( + todo$fingerprint, verbose = verbose, + function(i, verbose) { + display(verbose, i) + read_model(x = i, base = base, project = project) |> + extract_relevant() + } + ) |> + mutate(analysis = factor(.data$analysis)) +} + +#' @importFrom dplyr count group_by inner_join mutate n select summarise +#' @importFrom n2kanalysis get_data get_file_fingerprint +#' @importFrom stringr str_remove +#' @importFrom rlang .data +#' @importFrom tidyr complete pivot_wider +extract_relevant <- function(x) { + if (!inherits(x, "n2kInla")) { + return(NULL) + } + if (!grepl("year:stratum", x@AnalysisMetadata$model_type)) { + return(NULL) + } + get_data(x) |> + mutate(tmp = sprintf("%s_%0.5f", .data$stratum, .data$weight)) |> + count( + .data$tmp, .data$square, .data$point, name = "visits" + ) |> + group_by(.data$tmp, .data$square) |> + summarise( + points = n(), visits = sum(.data$visits), .groups = "drop" + ) |> + group_by(.data$tmp, .data$points) |> + summarise( + squares = n(), visits = sum(.data$visits), .groups = "drop" + ) -> tmp + tmp |> + select(-"visits") |> + complete( + .data$tmp, points = 1:6, fill = list(squares = 0) + ) |> + mutate(points = sprintf("points_%i", .data$points)) |> + pivot_wider(names_from = "points", values_from = "squares") |> + inner_join( + tmp |> + group_by(.data$tmp) |> + summarise( + relevant = sum(.data$squares), visits = sum(.data$visits), + .groups = "drop" + ), + by = "tmp" + ) |> + mutate( + analysis = get_file_fingerprint(x), + stratum = str_remove(.data$tmp, "_.*"), + weight = str_remove(.data$tmp, ".*_") |> + as.numeric() + ) |> + select(-"tmp") +} diff --git a/R/retrieve_results.R b/R/retrieve_results.R index e5557bc..ee35919 100644 --- a/R/retrieve_results.R +++ b/R/retrieve_results.R @@ -6,61 +6,73 @@ #' @param target_repo A [git2rdata::repository()] object containing the package #' code. #' @export -#' @importFrom dplyr %>% count inner_join mutate select +#' @importFrom dplyr count inner_join mutate select #' @importFrom git2rdata read_vc write_vc -#' @importFrom n2kanalysis get_result +#' @importFrom n2kanalysis get_result read_manifest #' @importFrom rlang .data #' @importFrom stats qnorm #' @importFrom stringr str_detect str_replace #' @importFrom utils file_test retrieve_results <- function( - base, project, source_repo, target_repo, verbose = TRUE, strict = TRUE + base, project, source_repo, target_repo, verbose = TRUE, strict = TRUE ) { - target_rds <- file.path(base, paste(project, "results.rds", sep = "_")) - if (file_test("-f", target_rds)) { - results <- readRDS(target_rds) - } else { - results <- get_result(file.path(base, project), verbose = verbose) - saveRDS(results, target_rds) + file.path(target_repo$path, "..", "inst", "results.rds") |> + normalizePath() -> target_rds + if (!file_test("-f", target_rds)) { + read_manifest(base = base, project = project) |> + get_result(base = base, project = project, verbose = verbose) |> + saveRDS(file = target_rds) } + results <- readRDS(target_rds) - read_vc(file.path("location", "stratum"), root = source_repo) %>% + file.path("location", "stratum") |> + read_vc(root = source_repo) |> write_vc( file = file.path("inst", "results", "stratum"), root = target_repo, sorting = "description", strict = strict ) # overview of the effort - read_vc(file.path("observation", "visit"), root = source_repo) %>% + file.path("observation", "visit") |> + read_vc(root = source_repo) |> inner_join( read_vc(file.path("location", "point"), root = source_repo), by = c("point_id" = "id") - ) %>% + ) |> inner_join( read_vc(file.path("location", "square"), root = source_repo), by = c("square_id" = "id") - ) %>% + ) |> inner_join( read_vc(file.path("location", "stratum"), root = source_repo), by = c("stratum_id" = "id") - ) %>% + ) |> count( .data$year, location = .data$square_id, stratum = .data$description, name = "visits" - ) %>% - mutate(stratum = factor(.data$stratum)) %>% + ) |> + mutate(stratum = factor(.data$stratum)) |> write_vc( file = file.path("inst", "results", "effort"), root = target_repo, sorting = c("stratum", "location", "year"), strict = strict ) + file.path("inst", "results", "effort") |> + update_metadata( + root = target_repo, name = "effort", title = "Sampling effort", + field_description = c( + year = "year of the survey", location = "identifier of the location", + stratum = "name of the stratum", + visits = "number of visited point locations" + ) + ) # analysis metadata - results@AnalysisMetadata %>% + results@AnalysisMetadata |> inner_join( - read_vc(file.path("species", "speciesgroup"), source_repo) %>% + read_vc(file.path("species", "speciesgroup"), source_repo) |> select("id", speciesgroup = "description"), by = c("species_group_id" = "id") - ) %>% + ) |> transmute( analysis = .data$file_fingerprint, speciesgroup = factor(.data$speciesgroup), @@ -71,284 +83,360 @@ retrieve_results <- function( str_detect(.data$model_type, "composite index: .*"), NA, str_replace(.data$model_type, "inla (.*): .*", "\\1") - ) %>% + ) |> factor(), .data$status_fingerprint, status = factor(.data$status) - ) %>% + ) |> left_join( - results@Parameter %>% - filter(.data$description == "WAIC") %>% + results@Parameter |> + filter(.data$description == "WAIC") |> semi_join( x = results@ParameterEstimate, by = c("parameter" = "fingerprint") - ) %>% + ) |> select("analysis", waic = "estimate"), by = "analysis" - ) %>% + ) |> write_vc( file = file.path("inst", "results", "meta"), root = target_repo, sorting = "analysis", strict = strict ) - results@AnalysisRelation %>% - mutate( - analysis = factor(.data$analysis), parent = factor(.data$parent_analysis) - ) %>% + file.path("inst", "results", "meta") |> + update_metadata( + root = target_repo, name = "meta", title = "Metadata on the analyses", + field_description = c( + analysis = "Unique identifier of the analysis", + speciesgroup = "Name of the species or species group", + composite = "TRUE indicates a species group, FALSE a species", + cycle = "TRUE indicates a three year cycle, FALSE yearly cycle", + linear = "TRUE indicates a linear trend, FALSE a nonlinear trend", + family = "Statistical distribution used in the analysis", + status_fingerprint = "Status fingerprint of the analysis", + status = "Status of the analysis", + waic = "Wantabe-Akaike Information Criterion" + ) + ) + + results@AnalysisRelation |> + transmute( + analysis = factor(.data$analysis), parent = .data$parent_analysis + ) |> write_vc( file = file.path("inst", "results", "parent"), root = target_repo, sorting = c("analysis", "parent"), strict = strict ) + file.path("inst", "results", "parent") |> + update_metadata( + file = file.path("inst", "results", "parent"), root = target_repo, + name = "parent", title = "Parent-child links between analyses", + description = "Lists only analyses with at least one parent", + field_description = c( + analysis = "Identifier of the child analysis.", + parent = "Identifier of the parent analysis." + ) + ) + # differences among year or cycle - results@Contrast %>% - filter(str_detect(.data$description, "^index")) %>% + results@Contrast |> + filter(str_detect(.data$description, "^index")) |> mutate( - reference = str_replace(.data$description, "index_(.*)_.*", "\\1") %>% + reference = str_replace(.data$description, "index_(.*)_.*", "\\1") |> as.integer(), - alternative = str_replace(.data$description, "index_.*_(.*)", "\\1") %>% + alternative = str_replace(.data$description, "index_.*_(.*)", "\\1") |> as.integer() - ) %>% - inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) %>% + ) |> + inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) |> mutate( s = (.data$upper_confidence_limit - .data$lower_confidence_limit) / (2 * qnorm(0.975)), lower_confidence_limit = qnorm(0.05, mean = .data$estimate, sd = .data$s), upper_confidence_limit = qnorm(0.95, mean = .data$estimate, sd = .data$s) - ) %>% + ) |> transmute( analysis = factor(.data$analysis), .data$reference, .data$alternative, estimate = round(.data$estimate, 4), lower_confidence_limit = round(.data$lower_confidence_limit, 4), upper_confidence_limit = round(.data$upper_confidence_limit, 4) - ) %>% + ) |> write_vc( file = file.path("inst", "results", "index"), root = target_repo, sorting = c("analysis", "reference", "alternative"), strict = strict ) + file.path("inst", "results", "index") |> + update_metadata( + root = target_repo, name = "index", title = "Estimated indices", + field_description = c( + analysis = "Identifier of the analysis", + reference = "Year or period used as reference", + alternative = "Year of period compared with the reference", + estimate = "Estimate of the different on the log-scale", + lower_confidence_limit = "5% quantile of the distribution of the index", + upper_confidence_limit = "95% quantile of the distribution of the index" + ) + ) + # estimated average count per year or cycle - results@Contrast %>% - filter(str_detect(.data$description, "^estimate")) %>% + results@Contrast |> + filter(str_detect(.data$description, "^estimate")) |> mutate( - year = str_replace(.data$description, "estimate_(.*)", "\\1") %>% + year = str_replace(.data$description, "estimate_(.*)", "\\1") |> as.integer() - ) %>% - inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) %>% + ) |> + inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) |> mutate( s = (.data$upper_confidence_limit - .data$lower_confidence_limit) / (2 * qnorm(0.975)), lower_confidence_limit = qnorm(0.05, mean = .data$estimate, sd = .data$s), upper_confidence_limit = qnorm(0.95, mean = .data$estimate, sd = .data$s) - ) %>% + ) |> transmute( analysis = factor(.data$analysis), .data$year, estimate = round(.data$estimate, 4), lower_confidence_limit = round(.data$lower_confidence_limit, 4), upper_confidence_limit = round(.data$upper_confidence_limit, 4) - ) %>% + ) |> write_vc( file = file.path("inst", "results", "estimate"), root = target_repo, sorting = c("analysis", "year"), strict = strict ) + file.path("inst", "results", "estimate") |> + update_metadata( + root = target_repo, name = "estimate", + title = "Estimate average number of bird per point", + field_description = c( + analysis = "Identifier of the analysis", + year = "Year or period of the estimate", + estimate = "Estimate on the log-scale", + lower_confidence_limit = + "5% quantile of the distribution on the log-scale", + upper_confidence_limit = + "95% quantile of the distribution on the log-scale" + ) + ) + # trend in moving window - results@Contrast %>% - filter(str_detect(.data$description, "^trend")) %>% + results@Contrast |> + filter(str_detect(.data$description, "^trend")) |> mutate( - midpoint = str_replace(.data$description, "trend_(.*)_.*", "\\1") %>% + midpoint = str_replace(.data$description, "trend_(.*)_.*", "\\1") |> as.numeric(), - window = str_replace(.data$description, "trend_.*_(.*)", "\\1") %>% + window = str_replace(.data$description, "trend_.*_(.*)", "\\1") |> as.integer() - ) %>% - inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) %>% + ) |> + inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) |> mutate( s = (.data$upper_confidence_limit - .data$lower_confidence_limit) / (2 * qnorm(0.975)), lower_confidence_limit = qnorm(0.05, mean = .data$estimate, sd = .data$s), upper_confidence_limit = qnorm(0.95, mean = .data$estimate, sd = .data$s) - ) %>% + ) |> transmute( analysis = factor(.data$analysis), .data$midpoint, .data$window, estimate = round(.data$estimate, 4), lower_confidence_limit = round(.data$lower_confidence_limit, 4), upper_confidence_limit = round(.data$upper_confidence_limit, 4) - ) %>% + ) |> write_vc( file = file.path("inst", "results", "moving_trend"), root = target_repo, sorting = c("analysis", "window", "midpoint"), strict = strict ) + file.path("inst", "results", "moving_trend") |> + update_metadata( + root = target_repo, name = "moving_trend", + title = "Trend for different moving windows", + field_description = c( + analysis = "Identifier of the analysis", + midpoint = "Year in the middle of the interval", + window = "Width of the interval as a number of years", + estimate = "Estimate on the log-scale", + lower_confidence_limit = + "5% quantile of the distribution on the log-scale", + upper_confidence_limit = + "95% quantile of the distribution on the log-scale" + ) + ) + # linear trend - results@Contrast %>% - filter(str_detect(.data$description, "^linear_trend")) %>% - inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) %>% + results@Contrast |> + filter(str_detect(.data$description, "^linear_trend")) |> + inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) |> mutate( s = (.data$upper_confidence_limit - .data$lower_confidence_limit) / (2 * qnorm(0.975)), lower_confidence_limit = qnorm(0.05, mean = .data$estimate, sd = .data$s), upper_confidence_limit = qnorm(0.95, mean = .data$estimate, sd = .data$s) - ) %>% + ) |> transmute( analysis = factor(.data$analysis), estimate = round(.data$estimate, 4), lower_confidence_limit = round(.data$lower_confidence_limit, 4), upper_confidence_limit = round(.data$upper_confidence_limit, 4) - ) %>% + ) |> write_vc( file = file.path("inst", "results", "linear_trend"), root = target_repo, sorting = "analysis", strict = strict ) + file.path("inst", "results", "linear_trend") |> + update_metadata( + root = target_repo, name = "linear_trend", + title = "Slope of the linear trend", + field_description = c( + analysis = "Identifier of the analysis", + estimate = "Estimate on the log-scale", + lower_confidence_limit = + "5% quantile of the distribution on the log-scale", + upper_confidence_limit = + "95% quantile of the distribution on the log-scale" + ) + ) + # estimate along linear trend - results@Contrast %>% - filter(str_detect(.data$description, "^linear_estimate")) %>% + results@Contrast |> + filter(str_detect(.data$description, "^linear_estimate")) |> mutate( - year = str_replace(.data$description, "linear_estimate_(.*)", "\\1") %>% + year = str_replace(.data$description, "linear_estimate_(.*)", "\\1") |> as.integer() - ) %>% - inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) %>% + ) |> + inner_join(results@ContrastEstimate, by = c("fingerprint" = "contrast")) |> mutate( s = (.data$upper_confidence_limit - .data$lower_confidence_limit) / (2 * qnorm(0.975)), lower_confidence_limit = qnorm(0.05, mean = .data$estimate, sd = .data$s), upper_confidence_limit = qnorm(0.95, mean = .data$estimate, sd = .data$s) - ) %>% + ) |> transmute( analysis = factor(.data$analysis), .data$year, estimate = round(.data$estimate, 4), lower_confidence_limit = round(.data$lower_confidence_limit, 4), upper_confidence_limit = round(.data$upper_confidence_limit, 4) - ) %>% + ) |> write_vc( file = file.path("inst", "results", "linear_estimate"), root = target_repo, sorting = c("analysis", "year"), strict = strict ) + file.path("inst", "results", "linear_estimate") |> + update_metadata( + root = target_repo, name = "linear_estimate", + title = "Estimates along a linear trend", + field_description = c( + analysis = "Identifier of the analysis", + year = "Year of estimate, centered to the start of the analysis", + estimate = "Estimate on the log-scale", + lower_confidence_limit = + "5% quantile of the distribution on the log-scale", + upper_confidence_limit = + "95% quantile of the distribution on the log-scale" + ) + ) # composite index - results@AnalysisMetadata %>% - filter(str_detect(.data$model_type, "composite")) %>% + results@AnalysisMetadata |> + filter(str_detect(.data$model_type, "composite")) |> semi_join( x = results@ParameterEstimate, by = c("analysis" = "file_fingerprint") - ) %>% + ) |> inner_join( - results@Parameter %>% - filter(str_detect(.data$description, "^index")) %>% + results@Parameter |> + filter(str_detect(.data$description, "^index")) |> mutate( - reference = str_replace(.data$description, "index_(.*)_.*", "\\1") %>% + reference = str_replace(.data$description, "index_(.*)_.*", "\\1") |> as.integer(), alternative = str_replace( .data$description, "index_.*_(.*)", "\\1" - ) %>% + ) |> as.integer() ), by = c("parameter" = "fingerprint") - ) %>% + ) |> mutate( s = (.data$upper_confidence_limit - .data$lower_confidence_limit) / (2 * qnorm(0.975)), lower_confidence_limit = qnorm(0.05, mean = .data$estimate, sd = .data$s), upper_confidence_limit = qnorm(0.95, mean = .data$estimate, sd = .data$s) - ) %>% + ) |> transmute( analysis = factor(.data$analysis), .data$reference, .data$alternative, estimate = round(.data$estimate, 4), lower_confidence_limit = round(.data$lower_confidence_limit, 4), upper_confidence_limit = round(.data$upper_confidence_limit, 4) - ) %>% + ) |> write_vc( file = file.path("inst", "results", "composite_index"), strict = strict, root = target_repo, sorting = c("analysis", "reference", "alternative") ) + file.path("inst", "results", "composite_index") |> + update_metadata( + root = target_repo, name = "composite_index", + title = "Indices by species group", + field_description = c( + analysis = "Identifier of the analysis", + reference = "Year or period used as reference", + alternative = "year or period to compare with the reference", + estimate = "Estimate on the log-scale", + lower_confidence_limit = + "5% quantile of the distribution on the log-scale", + upper_confidence_limit = + "95% quantile of the distribution on the log-scale" + ) + ) + # trend in moving window - results@Parameter %>% - filter(str_detect(.data$description, "^trend")) %>% + results@Parameter |> + filter(str_detect(.data$description, "^trend")) |> mutate( - midpoint = str_replace(.data$description, "trend_(.*)_.*", "\\1") %>% + midpoint = str_replace(.data$description, "trend_(.*)_.*", "\\1") |> as.numeric(), - window = str_replace(.data$description, "trend_.*_(.*)", "\\1") %>% + window = str_replace(.data$description, "trend_.*_(.*)", "\\1") |> as.integer() - ) %>% + ) |> inner_join( results@ParameterEstimate, by = c("fingerprint" = "parameter") - ) %>% + ) |> semi_join( - results@AnalysisMetadata %>% + results@AnalysisMetadata |> filter(str_detect(.data$model_type, "composite")), by = c("analysis" = "file_fingerprint") - ) %>% + ) |> mutate( s = (.data$upper_confidence_limit - .data$lower_confidence_limit) / (2 * qnorm(0.975)), lower_confidence_limit = qnorm(0.05, mean = .data$estimate, sd = .data$s), upper_confidence_limit = qnorm(0.95, mean = .data$estimate, sd = .data$s) - ) %>% + ) |> transmute( .data$analysis, .data$midpoint, .data$window, estimate = round(.data$estimate, 4), lower_confidence_limit = round(.data$lower_confidence_limit, 4), upper_confidence_limit = round(.data$upper_confidence_limit, 4) - ) %>% + ) |> write_vc( file = file.path("inst", "results", "composite_moving_trend"), root = target_repo, sorting = c("analysis", "window", "midpoint"), strict = strict ) - # stratum weights - read_relevant(base = base, project = project, verbose = verbose) %>% + file.path("inst", "results", "composite_moving_trend") |> + update_metadata( + root = target_repo, name = "composite_moving_trend", + title = "Moving trend for the composite indices", + field_description = c( + analysis = "Identifier of the analysis", + midpoint = "Year in the middle of the interval", + window = "Width of the interval as a number of years", + estimate = "Estimate on the log-scale", + lower_confidence_limit = + "5% quantile of the distribution on the log-scale", + upper_confidence_limit = + "95% quantile of the distribution on the log-scale" + ) + ) + + read_relevant(base = base, project = project, verbose = verbose) |> write_vc( file = file.path("inst", "results", "stratum_weight"), root = target_repo, sorting = c("analysis", "stratum"), strict = strict ) + file.path("inst", "results", "stratum_weight") |> + update_metadata( + root = target_repo, name = "stratum_weight" + ) return(invisible(NULL)) } - -#' @importFrom dplyr %>% count group_by mutate n summarise -#' @importFrom n2kanalysis display get_data get_file_fingerprint -#' @importFrom purrr map_dfr -#' @importFrom rlang .data -#' @importFrom stringr str_remove str_subset -read_relevant <- function(base, project, verbose = TRUE) { - file.path(base, project) %>% - list.files(pattern = "rds$", recursive = TRUE, full.names = TRUE) %>% - str_subset("converged") -> todo - map_dfr(todo, verbose = verbose, function(i, verbose) { - display(verbose, i) - x <- readRDS(i) - if (!inherits(x, "n2kInla")) { - return(NULL) - } - if (!grepl("year:stratum", x@AnalysisMetadata$model_type)) { - return(NULL) - } - get_data(x) %>% - mutate(tmp = sprintf("%s_%0.5f", .data$stratum, .data$weight)) %>% - count( - .data$tmp, .data$square, .data$point, name = "visits" - ) %>% - group_by(.data$tmp, .data$square) %>% - summarise( - points = n(), visits = sum(.data$visits), .groups = "drop" - ) %>% - group_by(.data$tmp, .data$points) %>% - summarise( - squares = n(), visits = sum(.data$visits), .groups = "drop" - ) -> tmp - tmp %>% - select(-.data$visits) %>% - complete( - .data$tmp, points = 1:6, fill = list(squares = 0) - ) %>% - mutate(points = sprintf("points_%i", .data$points)) %>% - pivot_wider(names_from = "points", values_from = "squares") %>% - inner_join( - tmp %>% - group_by(.data$tmp) %>% - summarise( - relevant = sum(.data$squares), visits = sum(.data$visits), - .groups = "drop" - ), - by = "tmp" - ) %>% - mutate( - analysis = get_file_fingerprint(x), - stratum = str_remove(.data$tmp, "_.*"), - weight = str_remove(.data$tmp, ".*_") %>% - as.numeric() - ) %>% - select(-.data$tmp) - }) %>% - mutate(analysis = factor(.data$analysis)) -} diff --git a/R/select_relevant.R b/R/select_relevant.R index d9dcf9d..bef0ec6 100644 --- a/R/select_relevant.R +++ b/R/select_relevant.R @@ -18,7 +18,7 @@ #' @inheritParams prepare_dataset #' @export #' @importFrom assertthat assert_that has_name is.count is.number is.string -#' @importFrom dplyr %>% count distinct filter left_join mutate rename select +#' @importFrom dplyr count distinct filter left_join mutate rename select #' semi_join #' @importFrom git2rdata is_git2rdata read_vc #' @importFrom rlang .data @@ -47,9 +47,9 @@ select_relevant <- function( return(NULL) } - read_vc(file, root = repo) %>% - select(-.data$datafield_id) %>% - left_join(x = observation, by = "sample_id") %>% + read_vc(file, root = repo) |> + select(-"datafield_id") |> + left_join(x = observation, by = "sample_id") |> mutate( count = replace_na(.data$count, 0), cycle = 1 + (.data$year - 2007) %/% 3, @@ -60,28 +60,28 @@ select_relevant <- function( return(NULL) } # require at least one observation per point - rawdata %>% - filter(.data$count > 0) %>% - distinct(.data$point) %>% + rawdata |> + filter(.data$count > 0) |> + distinct(.data$point) |> semi_join(x = rawdata, by = "point") -> rawdata # require observation during at least min_cycle different cycles for each # square - rawdata %>% - filter(.data$count > 0) %>% - distinct(.data$square, .data$cycle) %>% - count(.data$square) %>% - filter(.data$n >= min_cycle) %>% + rawdata |> + filter(.data$count > 0) |> + distinct(.data$square, .data$cycle) |> + count(.data$square) |> + filter(.data$n >= min_cycle) |> semi_join(x = rawdata, by = "square") -> rawdata if (sum(rawdata$count > 0) < min_observation) { return(NULL) } # require at least min_stratum locations per stratum - rawdata %>% - filter(.data$count > 0) %>% - distinct(.data$stratum, .data$square) %>% - count(.data$stratum) %>% - filter(.data$n >= min_stratum) %>% + rawdata |> + filter(.data$count > 0) |> + distinct(.data$stratum, .data$square) |> + count(.data$stratum) |> + filter(.data$n >= min_stratum) |> semi_join(x = rawdata, by = "stratum") -> rawdata if (sum(rawdata$count > 0) < min_observation) { return(NULL) @@ -90,35 +90,35 @@ select_relevant <- function( model <- glm(count ~ 0 + period, data = rawdata, family = poisson) log_threshold <- max(coef(model)) + log(proportion) selection <- levels(rawdata$period)[coef(model) >= log_threshold] - rawdata %>% - filter(.data$period %in% selection) %>% + rawdata |> + filter(.data$period %in% selection) |> mutate(period = factor(.data$period)) -> rawdata if (sum(rawdata$count > 0) < min_observation) { return(NULL) } model <- glm(count ~ 0 + period, data = rawdata, family = poisson) - rawdata %>% + rawdata |> mutate( period = relevel(.data$period, which.max(coef(model))) ) -> rawdata # require observation during at least min_cycle different cycles for each # location - rawdata %>% - filter(.data$count > 0) %>% - distinct(.data$square, .data$cycle) %>% - count(.data$square) %>% - filter(.data$n >= min_cycle) %>% + rawdata |> + filter(.data$count > 0) |> + distinct(.data$square, .data$cycle) |> + count(.data$square) |> + filter(.data$n >= min_cycle) |> semi_join(x = rawdata, by = "square") -> rawdata if (sum(rawdata$count > 0) < min_observation) { return(NULL) } # require at least min_stratum locations per stratum - rawdata %>% - filter(.data$count > 0) %>% - distinct(.data$stratum, .data$square) %>% - count(.data$stratum) %>% - filter(.data$n >= min_stratum) %>% - semi_join(x = rawdata, by = "stratum") %>% + rawdata |> + filter(.data$count > 0) |> + distinct(.data$stratum, .data$square) |> + count(.data$stratum) |> + filter(.data$n >= min_stratum) |> + semi_join(x = rawdata, by = "stratum") |> rename(observation_id = .data$sample_id) -> rawdata if (sum(rawdata$count > 0) < min_observation) { return(NULL) diff --git a/_pkgdown.yml b/_pkgdown.yml index 485eb1c..744bc1d 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -19,4 +19,4 @@ navbar: authors: Research Institute for Nature and Forest (INBO): href: "https://www.vlaanderen.be/inbo/en-gb" - html: "" + html: "logo of the Research Institute for Nature and Forest (INBO)" diff --git a/abvanalysis.Rproj b/abvanalysis.Rproj index e4a061f..600e4af 100644 --- a/abvanalysis.Rproj +++ b/abvanalysis.Rproj @@ -1,4 +1,5 @@ Version: 1.0 +ProjectId: 90bc52d8-09d8-496a-b6ae-ae375a1199c8 RestoreWorkspace: Default SaveWorkspace: Default diff --git a/checklist.yml b/checklist.yml index e4a11f8..8d3e1d4 100644 --- a/checklist.yml +++ b/checklist.yml @@ -30,6 +30,7 @@ required: - folder conventions - license - lintr +- organisation - repository secret - spelling spelling: @@ -41,20 +42,18 @@ spelling: other: nl-BE: - inst/interactive/composite.qmd + - inst/interactive/composite_pdf.qmd - inst/interactive/index.md - inst/interactive/methodologie/index.qmd + - inst/interactive/methodologie/inzameling.qmd - inst/interactive/methodologie/reproduceerbaarheid.qmd - inst/interactive/methodologie/verwerking.qmd - inst/interactive/methodologie/voorstelling.qmd - inst/interactive/samenvatting.md - inst/interactive/species.qmd + - inst/interactive/species_pdf.qmd - inst/interactive/trends.qmd - - inst/website/0_methodologie.Rmd - - inst/website/1_samengesteld.Rmd - - inst/website/2_trend_per_soort.Rmd - - inst/website/_samengesteld.Rmd - - inst/website/_soort.Rmd - - inst/website/download_html.Rmd - - inst/website/download_non_html.Rmd - - inst/website/index.Rmd - - inst/website/zzz_bibliografietitel.Rmd + - inst/interactive/trends_pdf.qmd + - inst/interactive/vbp_species.qmd +pak: +- pdftools diff --git a/inst/CITATION b/inst/CITATION index 2150e00..1d3d6f4 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -2,12 +2,12 @@ citHeader("To cite `abvanalysis` in publications please use:") # begin checklist entry bibentry( bibtype = "Manual", - title = "abvanalysis: Analysis of the Common Breeding Bird Survey. Version 0.2.4", - author = c( author = c(person(given = "Thierry", family = "Onkelinx"))), - year = 2024, + title = "abvanalysis: Analysis of the Common Breeding Bird Survey. Version 0.2.5", + author = c(person(given = "Thierry", family = "Onkelinx")), + year = 2026, url = "https://github.com/inbo/abvanalysis/", abstract = "Read in the raw data and prepare analysis datasets.", - textVersion = "Onkelinx, Thierry (2024) abvanalysis: Analysis of the Common Breeding Bird Survey. Version 0.2.4. https://github.com/inbo/abvanalysis/", + textVersion = "Onkelinx, Thierry (2026) abvanalysis: Analysis of the Common Breeding Bird Survey. Version 0.2.5. https://github.com/inbo/abvanalysis/", keywords = "common breeding birds; monitoring; analysis; reproducible science", ) # end checklist entry diff --git a/inst/WORDLIST b/inst/WORDLIST deleted file mode 100644 index 3f27f91..0000000 --- a/inst/WORDLIST +++ /dev/null @@ -1,8 +0,0 @@ -ABV -Codecov -INBO -INLA -Lifecycle -Natuurpunt -Studie -Vogelnieuws diff --git a/inst/WORDLIST_nl_BE b/inst/WORDLIST_nl_BE deleted file mode 100644 index 4b557c8..0000000 --- a/inst/WORDLIST_nl_BE +++ /dev/null @@ -1,46 +0,0 @@ -ABV -Bayesiaanse -Bosonderzoek -Data-hashes -GBIF -Gaussiaanse -INBO -INLA -Poisson -R-script -SHA-1 -WAIC -Waarderingkaart -aantalsontwikkelingen -data-hash -exponentiëren -geaggregreerd -geparametriseerd -gestratificeerde -habitats -hash -hashes -hashfunctie -herbezoeken -kommagescheiden -kwantielen -linkeras -log-schaal -log-transformatie -nulwaarnemingen -overbemonsterd -overdisperieparameter -overdispersie -package -packages -rechteras -referentiejaartal -rekenbladprogramma -strata -stratum -stratumeffecten -stratumgewicht -stratumgewichten -t.e.m. -varianties -zestientallig diff --git a/inst/analysis_local.R b/inst/analysis_local.R index d9fc51e..43657c4 100644 --- a/inst/analysis_local.R +++ b/inst/analysis_local.R @@ -1,38 +1,66 @@ +# import data library(abvanalysis) repo <- git2rdata::repository(file.path("~", "n2k", "abv")) -origin <- RPostgreSQL::dbConnect( - RPostgreSQL::PostgreSQL(), host = "127.0.0.1", user = "readonly", - dbname = "meetnetten", port = "65432", - password = keyring::key_get("meetnetten", username = "readonly") -) # postgres server origin <- odbc::dbConnect( - odbc::odbc(), driver = "ODBC Driver 17 for SQL Server", + odbc::odbc(), + driver = "ODBC Driver 18 for SQL Server", server = "inbo-sql08-prd.inbo.be", - database = "S0008_00_Meetnetten", port = 1433, uid = "bmkreader", + Encrypt = "no", + database = "S0008_00_Meetnetten", + port = 1433, + uid = "bmkreader", pwd = keyring::key_get("meetnetten", username = "bmkreader") ) # sql server - +project <- "abv" prepare_dataset( - origin = origin, repo = repo, end_date = Sys.time(), verbose = TRUE, - push = FALSE, strict = TRUE + origin = origin, + repo = repo, + end_date = Sys.time(), + verbose = TRUE, + push = FALSE, + strict = FALSE, + db_scheme = "staging_Meetnetten" ) -base <- file.path("~", "n2kanalysis") +# prepare analysis +library(abvanalysis) +library(n2kanalysis) +repo <- git2rdata::repository(file.path("~", "n2k", "abv")) project <- "abv" -dir.create(file.path(base, project), recursive = TRUE, showWarnings = FALSE) -script <- prepare_analysis( - repo = repo, base = base, project = project, docker = "inbobmk/rn2k:0.9", - dependencies = c("inbo/n2khelper@v0.5.0", "inbo/n2kanalysis@v0.3.2") -) +connect_inbo_s3() +base <- aws.s3::get_bucket(Sys.getenv("N2KBUCKET"), prefix = project, max = 1) +prepare_analysis( + repo = repo, + base = base, + project = project, + docker = "inbobmk/rn2k:dev-0.10", + dependencies = c("inbo/n2khelper@v0.5.0", "inbo/n2kanalysis@0.4.1") +) -> bash_script +sprintf( + "aws s3 cp s3://%s/%s ~/abv.sh\nchmod +x ~/abv.sh", + Sys.getenv("N2KBUCKET"), + bash_script +) |> + cat() -writeLines( - c(script$init, script$model), file.path(base, project, "abv_model.sh") -) -writeLines( - c(script$init, script$manifest), file.path(base, project, "abv.sh") -) +# fit models +library(n2kanalysis) +connect_inbo_s3() +base <- aws.s3::get_bucket(Sys.getenv("N2KBUCKET"), prefix = project, max = 1) +read_manifest(base = base, project = project) |> + fit_model(base = base, project = project) +# get results +library(abvanalysis) +library(n2kanalysis) +connect_inbo_s3() +project <- "abv" +base <- aws.s3::get_bucket(Sys.getenv("N2KBUCKET"), prefix = project, max = 1) +repo <- git2rdata::repository(file.path("~", "n2k", "abv")) retrieve_results( - base = base, project = project, source_repo = repo, - target_repo = git2rdata::repository(), strict = FALSE + base = base, + project = project, + source_repo = repo, + target_repo = git2rdata::repository(), + strict = FALSE ) diff --git a/inst/extensions/flandersqmd-report-website/_extension.yml b/inst/extensions/flandersqmd-report-website/_extension.yml deleted file mode 100644 index f182c6f..0000000 --- a/inst/extensions/flandersqmd-report-website/_extension.yml +++ /dev/null @@ -1,23 +0,0 @@ -title: Corporate identity of the Flemish government quarto websites -author: Thierry Onkelinx -version: 0.1.0 -quarto-required: ">=1.4.0" -contributes: - project: - project: - type: website - website: - sidebar: - search: true - style: "floating" - format: - html: - css: [css/agency.css, css/flanders_generic.css] - template: "partials/template.html" - number-sections: true - toc: true - html-math-method: katex - filters: - - filters/translations.lua - shortcodes: - - shortcodes/colophon.lua diff --git a/inst/extensions/flandersqmd-report-website/filters/translations.lua b/inst/extensions/flandersqmd-report-website/filters/translations.lua deleted file mode 100644 index 8fce5ea..0000000 --- a/inst/extensions/flandersqmd-report-website/filters/translations.lua +++ /dev/null @@ -1,130 +0,0 @@ -function is_empty(s) - return s == nil or s == '' -end - -function translation(lang) - local result - if (lang == "nl-BE") then - result = { - author = "Geschreven door", - address = "Vlaamse overheid, Herman Teirlinckgebouw, Havenlaan 88, 1000 Brussel", - citation = "Wijze van citeren", - city = "Brussel", - country = "België", - depotnr = "Depotnummer", - export = "Exporteer referentie als", - location = "Vestiging", - mission = "Hier komt de missie", - name = "Vlaamse overheid", - ordernr = "Opdrachtnummer", - reviewer = "Nagelezen door", - series = "Rapporten van het", - url = "https://www.vlaanderen.be", - url_text = "vlaanderen.be", - vu = "Verantwoordelijke uitgever", - year = "Gepubliceerd in" - } - elseif (lang == "fr-FR") then - result = { - author = "Écrit par", - address = "Autorité flamande, Herman Teirlinckgebouw, Avenu du Port 88, 1000 Bruxelles", - citation = "Citation recommandée", - city = "Bruxelles", - country = "Belgique", - depotnr = "Numéro de dépôt", - export = "Exporter la référence à", - location = "Adresse", - mission = "Mission statement", - name = "l'Autorité flamande ", - ordernr = "Numéro de commande", - reviewer = "Examiné par", - series = "Rapports de", - url = "https://www.vlaanderen.be/fr", - url_text = "vlaanderen.be", - vu = "Éditeur responsable", - year = "Publié en" - } - else - result = { - author = "Written by", - address = "Government of Flanders, Herman Teirlinck, Havenlaan 88, 1000 Brussel", - citation = "Way of quoting", - city = "Brussels", - country = "Belgium", - depotnr = "Deposit number", - export = "Export reference to", - location = "Location:", - mission = "Misson statement", - name = "Government of Flanders", - ordernr = "Order number", - reviewer = "Reviewed by", - series = "Reports of the", - url = "https://www.vlaanderen.be/en", - url_text = "vlaanderen.be", - vu = "Responsible publisher", - year = "Published during" - } - end - return result -end - -function abbreviate_person(person, i, type, n) - if i > 2 then - return '' - end - if i == 2 and n > 2 then - return ' et al.' - end - if (i > 1) then - res = ' & ' - else - res = '' - end - if is_empty(person.name) then - res = res .. '

!!! flandersqmd.' .. type ..' element ' .. i .. ' has no name element!!!

' - else - if is_empty(person.name.family) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no family element!!!

' - else - res = res .. ' ' .. pandoc.utils.stringify(person.name.family) - end - if is_empty(person.name.given) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no given element!!!

' - else - local x = pandoc.utils.stringify(person.name.given) - res = res .. ', ' .. string.gsub(x, '([A-Z])[a-zàçéèïîôö]*', '%1.') - end - end - return res -end - -function shortauthor(author) - n = 0 - for i, person in pairs(author) do - n = i - end - z = '' - for i, person in pairs(author) do - z = z .. abbreviate_person(person, i, 'author', n) - end - return z -end - -return { - { - Meta = function(meta) - meta.translation = translation(pandoc.utils.stringify(meta.lang)) - meta.issn = "ISSN nummer" - meta.vu = "Naam verantwoordelijke uitgever" - if is_empty(meta.flandersqmd.author) then - meta.shortauthor = pandoc.RawInline( - "html", - '

!!! Missing flandersqmd.author !!!

' - ) - else - meta.shortauthor = shortauthor(meta.flandersqmd.author) - end - return meta - end - } -} diff --git a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Bold.ttf b/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Bold.ttf deleted file mode 100644 index ade4266..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Bold.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Light.ttf b/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Light.ttf deleted file mode 100644 index 577a2a0..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Light.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Medium.ttf b/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Medium.ttf deleted file mode 100644 index 1deb449..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Medium.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Regular.ttf b/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Regular.ttf deleted file mode 100644 index a7b8d23..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/FlandersArtSans-Regular.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Bold.ttf b/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Bold.ttf deleted file mode 100644 index 9f92725..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Bold.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Light.ttf b/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Light.ttf deleted file mode 100644 index 2ce1842..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Light.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Medium.ttf b/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Medium.ttf deleted file mode 100644 index 3643283..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Medium.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Regular.ttf b/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Regular.ttf deleted file mode 100644 index 457d262..0000000 Binary files a/inst/extensions/flandersqmd-report-website/fonts/Inconsolata-Regular.ttf and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/img/background-pattern.png b/inst/extensions/flandersqmd-report-website/img/background-pattern.png deleted file mode 100644 index a2d9426..0000000 Binary files a/inst/extensions/flandersqmd-report-website/img/background-pattern.png and /dev/null differ diff --git a/inst/extensions/flandersqmd-report-website/img/cc-by.svg b/inst/extensions/flandersqmd-report-website/img/cc-by.svg deleted file mode 100644 index e44c25f..0000000 --- a/inst/extensions/flandersqmd-report-website/img/cc-by.svg +++ /dev/null @@ -1,155 +0,0 @@ - - - - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/inst/extensions/flandersqmd-report-website/partials/nav-container.html b/inst/extensions/flandersqmd-report-website/partials/nav-container.html deleted file mode 100644 index 96b024c..0000000 --- a/inst/extensions/flandersqmd-report-website/partials/nav-container.html +++ /dev/null @@ -1,9 +0,0 @@ - -
blabla -$if(logo)$ $endif$ -
- $if(title)$
Junk
$endif$ - $if(subtitle)$
$subtitle$
$endif$ - $if(author)$
$author$
$endif$ -
-
diff --git a/inst/extensions/flandersqmd-report-website/partials/template.html b/inst/extensions/flandersqmd-report-website/partials/template.html deleted file mode 100644 index 4429a83..0000000 --- a/inst/extensions/flandersqmd-report-website/partials/template.html +++ /dev/null @@ -1,84 +0,0 @@ - - - - - - -$for(author-meta)$ - -$endfor$ -$if(date-meta)$ - -$endif$ -$if(keywords)$ - -$endif$ -$if(description-meta)$ - -$endif$ - $if(title-prefix)$$title-prefix$ – $endif$$pagetitle$ - -$for(css)$ - -$endfor$ -$for(header-includes)$ - $header-includes$ -$endfor$ -$if(math)$ -$if(mathjax)$ - -$endif$ - $math$ -$endif$ - - -$for(include-before)$ -$include-before$ -$endfor$ -$if(title)$ -
-

$title$

-$if(subtitle)$ -

$subtitle$

-$endif$ -$for(author)$ -

$author$

-$endfor$ -$if(date)$ -

$date$

-$endif$ -$if(abstract)$ -
-
$abstract-title$
-$abstract$ -
-$endif$ -
-$endif$ -$if(toc)$ - -$endif$ -$body$ - -

 

-

- - Creative Commons-License - $shortauthor$ - $publicationyear$ - $if(flandersqmd.doi)$$flandersqmd.doi$$else$

!!! Missing flandersqmd.doi !!!

$endif$ - - -$for(include-after)$ -$include-after$ -$endfor$ - - - diff --git a/inst/extensions/flandersqmd-report-website/shortcodes/colophon.lua b/inst/extensions/flandersqmd-report-website/shortcodes/colophon.lua deleted file mode 100644 index 810139a..0000000 --- a/inst/extensions/flandersqmd-report-website/shortcodes/colophon.lua +++ /dev/null @@ -1,424 +0,0 @@ -return { - ['colophon'] = function(args, kwargs, meta) ---[[check if variable has content]] - local function is_empty(s) - return s == nil or s == '' - end - ---[[format person]] - local function display_person(person, i, type) - local res = '' - if i > 1 then - res = res .. '
' - end - if not is_empty(person.email) then - res = res .. '' - end - if is_empty(person.name) then - res = res .. '

!!! flandersqmd.' .. type ..' element ' .. i .. ' has no name element!!!

' - else - if is_empty(person.name.given) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no given element!!!

' - else - res = res .. pandoc.utils.stringify(person.name.given) - end - if is_empty(person.name.family) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no family element!!!

' - else - res = res .. ' ' .. pandoc.utils.stringify(person.name.family) - end - if not is_empty(person.email) then - res = res .. '' - end - end - if not is_empty(person.orcid) then - local x = pandoc.utils.stringify(person.orcid) - res = res .. ' ' - end - if not is_empty(person.affiliation) then - for i, instititute in pairs(person.affiliation) do - res = res .. ', ' .. pandoc.utils.stringify(instititute) - end - end - return res - end - ---[[format person for bibtex]] - local function bibtex_person(person, i, type) - local res = '' - if i > 1 then - res = res .. ' and ' - end - if is_empty(person.name) then - res = res .. '

!!! flandersqmd.' .. type ..' element ' .. i .. ' has no name element!!!

' - else - if is_empty(person.name.family) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no family element!!!

' - else - res = res .. pandoc.utils.stringify(person.name.family) .. ', ' - end - if is_empty(person.name.given) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no given element!!!

' - else - res = res .. pandoc.utils.stringify(person.name.given) - end - end - return res - end - ---[[format person for ris]] - local function ris_person(person, i, type) - local res = 'AU - ' - if is_empty(person.name) then - res = res .. '

!!! flandersqmd.' .. type ..' element ' .. i .. ' has no name element!!!

' - else - if is_empty(person.name.family) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no family element!!!

' - else - res = res .. pandoc.utils.stringify(person.name.family) .. ', ' - end - if is_empty(person.name.given) then - res = res .. '

!!! flandersqmd.' .. type .. ' element ' .. i .. ' name element has no given element!!!

' - else - res = res .. pandoc.utils.stringify(person.name.given) .. '\n' - end - end - return res - end - ---[[format person for json]] - local function json_person(person, i, type) - local res = ' {\n' - if not is_empty(person.name) then - if not is_empty(person.name.family) then - res = res .. ' "family":"' .. pandoc.utils.stringify(person.name.family) .. '",\n' - end - if not is_empty(person.name.given) then - res = res .. ' "given":"' .. pandoc.utils.stringify(person.name.given) .. '"\n }\n' - end - end - return res - end - ---[[initialise]] - if is_empty(meta.flandersqmd.cover) then - z = '' - else - y = pandoc.utils.stringify(meta.flandersqmd.cover) - y = y:match("(.+)%..*") - z = '' - end - z = z .. '
' - ---[[add title]] - z = z .. '
' - if is_empty(meta.flandersqmd.title) then - z = z .. '

!!! Missing flandersqmd.title !!!

' - else - z = z .. '

' .. pandoc.utils.stringify(meta.flandersqmd.title) .. '

' - end - ---[[add subtitle]] - if not is_empty(meta.flandersqmd.subtitle) then - z = z .. '

' .. pandoc.utils.stringify(meta.flandersqmd.subtitle) .. '

' - end - z = z .. '
' - ---[[add authors]] - z = z .. '
' .. '
' .. - '
' .. meta.translation.author .. - '
' .. '
' - if is_empty(meta.flandersqmd.author) then - z = z .. '

!!! Missing flandersqmd.author !!!

' - else - for i, person in pairs(meta.flandersqmd.author) do - z = z .. display_person(person, i, 'author') - end - end - z = z .. '
' .. '
' - ---[[add reviewer]] - if not is_empty(meta.flandersqmd.reviewer) then - z = z .. '
' .. '
' .. - meta.translation.reviewer .. '
' .. - '
' - for i, person in pairs(meta.flandersqmd.reviewer) do - z = z .. display_person(person, i, 'reviewer') - end - z = z .. '
' .. '
' - end - ---[ insert DOI] - z = z .. '
' .. '
DOI
' .. - '
' - if is_empty(meta.flandersqmd.doi) then - z = z .. '

!!! Missing flandersqmd.doi !!!

' - else - local x = pandoc.utils.stringify(meta.flandersqmd.doi) - z = z .. '' .. x .. '' - end - z = z .. '
'.. '
' - ---[ insert year] - if not is_empty(meta.flandersqmd.publicationdate) then - z = z .. '
' .. '
' .. - meta.translation.year .. '
' .. - '
' .. '

' .. - pandoc.utils.stringify(meta.publicationyear) .. '

' .. '
' - end - ---[ insert ordernumber] - if not is_empty(meta.flandersqmd.ordernr) then - z = z .. '
' .. '
' .. - meta.translation.ordernr .. '
' .. - '
' .. '

' .. - pandoc.utils.stringify(meta.flandersqmd.ordernr) .. '

'.. '
' - end - ---[ insert depotnumber] - if not is_empty(meta.flandersqmd.depotnr) then - z = z .. '
' .. '
' .. - meta.translation.depotnr .. '
' .. - '
' .. '

' .. - pandoc.utils.stringify(meta.flandersqmd.depotnr) .. '

'.. '
' - end - ---[ insert issn] - if not is_empty(meta.translation.issn_nr) then - z = z .. '
' .. '
ISSN
' .. - '
' .. - meta.translation.issn_nr .. '
'.. '
' - end - ---[ insert vu] - if not is_empty(meta.translation.vu_name) then - z = z .. '
' .. '
' .. - meta.translation.vu .. '
' .. - '
' .. '

' .. - meta.translation.vu_name .. '

'.. '
' - end - ---[[end meta]] - z = z .. '
' - ---[ insert mission] - if not is_empty(meta.translation.mission) then - z = z .. '
' .. - meta.translation.mission .. '
' - end - ---[ insert address] - if not is_empty(meta.translation.address) then - z = z .. '
' .. '
' .. - meta.translation.location .. '
' .. - '
' .. meta.translation.address - if not is_empty(meta.translation.url) then - z = z .. ' ' - if is_empty(meta.translation.url_text) then - z = z .. meta.translation.url .. '' - else - z = z .. meta.translation.url_text .. '' - end - end - z = z .. '
' - end - ---[ insert citation] - z = z .. '
' .. - meta.translation.citation .. '
' .. - '
' - if is_empty(meta.flandersqmd.author) then - z = z .. '

!!! Missing flandersqmd.author !!!

' - else - z = z .. meta.shortauthor - end - if is_empty(meta.flandersqmd.publicationdate) then - z = z .. '

!!! Missing flandersqmd.publicationdate !!!

' - else - z = z .. ' (' .. meta.publicationyear .. ') ' - end - if is_empty(meta.flandersqmd.title) then - z = z .. '

!!! Missing flandersqmd.title !!!

' - else - z = z .. pandoc.utils.stringify(meta.flandersqmd.title) .. '. ' - if not is_empty(meta.flandersqmd.subtitle) then - z = z .. pandoc.utils.stringify(meta.flandersqmd.subtitle) .. '. ' - end - end - if not is_empty(meta.translation.name) then - if not is_empty(meta.translation.series) then - z = z .. meta.translation.series .. ' ' - end - z = z .. meta.translation.name .. ' ' - end - if is_empty(meta.flandersqmd.publicationdate) then - z = z .. '

!!! Missing flandersqmd.publicationdate !!!

' - else - z = z .. meta.publicationyear - end - if is_empty(meta.flandersqmd.reportnr) then - z = z .. '

!!! Missing flandersqmd.reportnr !!!

' - else - z = z .. '(' .. pandoc.utils.stringify(meta.flandersqmd.reportnr) .. '). ' - end - z = z .. meta.translation.name .. ', ' .. meta.translation.city .. '. DOI: ' - if is_empty(meta.flandersqmd.doi) then - z = z .. '

!!! Missing flandersqmd.doi !!!

' - else - local x = pandoc.utils.stringify(meta.flandersqmd.doi) - z = z .. '' .. x .. '' - end - z = z .. '
' - - z = z .. '
' .. meta.translation.export .. - '' .. - '' .. - '' .. - '' - z = z .. '\n' - z = z .. '' - - z = z .. '' .. '
' - if not is_empty(meta.flandersqmd.cover) then - z = z .. '
' .. '
' .. - meta.translation.coverdescription .. '
' - if is_empty(meta.flandersqmd.coverdescription) then - z = z .. '

!!! Missing flandersqmd.coverdescription !!!

' - else - y = pandoc.utils.stringify(meta.flandersqmd.coverdescription) - z = z .. '
' .. y .. '
' - end - z = z .. '
' - end - if not is_empty(meta.flandersqmd.client) then - z = z .. '
' .. '
' .. - meta.translation.client .. '
' .. - '
' - for i, client in pairs(meta.flandersqmd.client) do - z = z .. pandoc.utils.stringify(client) .. '
' - end - if not is_empty(meta.flandersqmd.clientlogo) then - z = z .. '' - end - z = z .. '
' .. '
' - end - if not is_empty(meta.flandersqmd.cooperation) then - z = z .. '
' .. '
' .. - meta.translation.cooperation .. '
' .. - '
' - for i, cooperation in pairs(meta.flandersqmd.cooperation) do - z = z .. pandoc.utils.stringify(cooperation) .. '
' - end - if not is_empty(meta.flandersqmd.cooperationlogo) then - z = z .. '' - end - z = z .. '
' .. '
' - end - ---[[finalise]] - z = z .. '
' - - return pandoc.RawInline("html", z) - end -} diff --git a/inst/extensions/inboqmd-report-website/_extension.yml b/inst/extensions/inboqmd-report-website/_extension.yml deleted file mode 100644 index 259e524..0000000 --- a/inst/extensions/inboqmd-report-website/_extension.yml +++ /dev/null @@ -1,23 +0,0 @@ -title: Corporate identity of the Research Institute for Nature and Forest (INBO) quarto report websites -author: Thierry Onkelinx -version: 0.1.0 -quarto-required: ">=1.4.0" -contributes: - project: - project: - type: website - website: - sidebar: - search: true - style: "floating" - format: - html: - css: [css/agency.css, _extensions/flandersqmd-report-website/css/flanders_generic.css] - template: "_extensions/flandersqmd-report-website/partials/template.html" - number-sections: true - toc: true - html-math-method: katex - filters: - - filters/translations.lua - shortcodes: - - _extensions/flandersqmd-report-website/shortcodes/colophon.lua diff --git a/inst/extensions/inboqmd-report-website/filters/translations.lua b/inst/extensions/inboqmd-report-website/filters/translations.lua deleted file mode 100644 index 48a2fab..0000000 --- a/inst/extensions/inboqmd-report-website/filters/translations.lua +++ /dev/null @@ -1,222 +0,0 @@ -function is_empty(s) - return s == nil or s == '' -end - -function translation_entity(result, lang, entity) - if (entity == "INBO") then - if (lang == "nl-BE") then - result.address = "INBO Brussel, Herman Teirlinckgebouw, Havenlaan 88 bus 73, 1000 Brussel" - result.city = "Brussel" - result.mission = "Het INBO is het onafhankelijk onderzoeksinstituut van de Vlaamse overheid dat via toegepast wetenschappelijk onderzoek, data- en kennisontsluiting het biodiversiteitsbeleid en -beheer onderbouwt en evalueert." - result.name = "Instituut voor Natuur- en Bosonderzoek" - result.series = "Rapporten van het" - result.tagline = "vlaanderen-wetenschap.pdf" - result.url = "https://www.vlaanderen.be/inbo" - result.url_text = "vlaanderen.be/inbo" - elseif (lang == "fr-FR") then - result.address = "INBO Bruxelles, Herman Teirlinckgebouw, Avenu du Port 88 boîte 73, 1000 Bruxelles" - result.city = "Bruxelles" - result.mission = "l'Institut de Recherche sur la Nature et les Forêts ('Instituut voor Natuur- en Bosonderzoek', INBO) est un institut de recherche indépendant du gouvernement flamand, qui étaye et évalue la politique et la gestion en matière de biodiversité par la recherche scientifique appliquée, l'intégration et la dissémination publique de données et de connaissances." - result.name = "l'Institut de Recherche sur la Nature et les Forêts" - result.series = "Rapports de" - result.tagline = "flanders-state-art.pdf" - result.url = "https://www.vlaanderen.be/inbo/en-gb/homepage/" - result.url_text = "vlaanderen.be/inbo" - else - result.address = "INBO Brussels, Herman Teirlinckgebouw, Havenlaan 88 bus 73, 1000 Brussels" - result.city = "Brussels" - result.mission = "The Research Institute for Nature and Forest (INBO) is an independent research institute of the Flemish government. Through applied scientific research, open data and knowledge, integration and disclosure, it underpins and evaluates biodiversity policy and management." - result.name = "Research Instute for Nature and Forest" - result.tagline = "flanders-state-art.pdf" - result.url = "https://www.vlaanderen.be/inbo/en-gb/homepage/" - result.url_text = "vlaanderen.be/inbo" - end - result.issn_nr = "1782-9054" - result.vu_name = "Hilde Eggermont" - else - result.address = metadata_warning('flandersqmd.entity must be equal to INBO. Please contact the maintainer if you need a different entity') - result.city = metadata_warning('flandersqmd.entity must be equal to INBO. Please contact the maintainer if you need a different entity') - result.mission = metadata_warning('flandersqmd.entity must be equal to INBO. Please contact the maintainer if you need a different entity') - result.name = metadata_warning('flandersqmd.entity must be equal to INBO. Please contact the maintainer if you need a different entity') - result.tagline = "flanders-state-art.pdf" - result.url = '' - result.url_text = metadata_warning('flandersqmd.entity must be equal to INBO. Please contact the maintainer if you need a different entity') - result.issn_nr = metadata_warning("flandersqmd.entity must be equal to INBO. Please contact the maintainer if you need a different entity") - result.vu_name = metadata_warning("flandersqmd.entity must be equal to INBO. Please contact the maintainer if you need a different entity") - end - return result -end - -function translation(lang, entity) - local result - if (lang == "nl-BE") then - result = { - author = "Geschreven door", - author_pdf = "Auteurs", - ccby = "Dit werk valt onder een \\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Naamsvermelding 4.0 Internationaal-licentie}.", - citation = "Wijze van citeren", - city = "Brussel", - client = "Dit onderzoek werd uitgevoerd in opdracht van", - cooperation = "Dit onderzoek werd uitgevoerd in samenwerking met", - country = "België", - coverdescription = "Foto cover", - depotnr = "Depotnummer", - export = "Exporteer referentie als", - location = "Vestiging", - mission = "Hier komt de missie", - ordernr = "Opdrachtnummer", - reviewer = "Nagelezen door", - reviewer_pdf = "Reviewers", - series = "Rapporten van het", - vu = "Verantwoordelijke uitgever", - year = "Gepubliceerd in" - } - elseif (lang == "fr-FR") then - result = { - author = "Écrit par", - author_pdf = "Auteurs", - ccby = "Ce rapport est sous licence \\href{https://creativecommons.org/licenses/by/4.0/deed.fr}{Creative Commons Attribution 4.0 International}.", - citation = "Citation recommandée", - city = "Bruxelles", - client = "Cette étude a été commandée par", - cooperation = "Cette étude a été menée en collaboration avec", - country = "Belgique", - coverdescription = "Photo de couverture", - depotnr = "Numéro de dépôt", - export = "Exporter la référence à", - location = "Adresse", - mission = "Mission statement", - ordernr = "Numéro de commande", - reviewer = "Examiné par", - reviewer_pdf = "Reviewers", - series = "Rapports de", - vu = "Éditeur responsable", - year = "Publié en" - } - else - result = { - author = "Written by", - author_pdf = "Authors", - ccby = "This work is licensed under a \\href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 Generic License}.", - citation = "Way of quoting", - city = "Brussels", - client = "This study was commissioned by", - cooperation = "This study was conducted in collaboration with", - country = "Belgium", - coverdescription = "Cover photo", - depotnr = "Deposit number", - export = "Export reference to", - location = "Location", - mission = "Misson statement", - ordernr = "Order number", - reviewer = "Reviewed by", - reviewer_pdf = "Reviewers", - series = "Reports of the", - vu = "Responsible publisher", - year = "Published during" - } - end - return translation_entity(result, lang, entity) -end - -function abbreviate_person(person, i, type, n) - if i > 2 then - return '' - end - if i == 2 and n > 2 then - return ' et al.' - end - if (i > 1) then - res = ' & ' - else - res = '' - end - if is_empty(person.name) then - res = res .. metadata_warning('flandersqmd.' .. type ..' element ' .. i .. ' has no name element') - else - if is_empty(person.name.family) then - res = res .. metadata_warning('flandersqmd.' .. type .. ' element ' .. i .. ' name element has no family element') - else - res = res .. ' ' .. pandoc.utils.stringify(person.name.family) - end - if is_empty(person.name.given) then - res = res .. metadata_warning('flandersqmd.' .. type .. ' element ' .. i .. ' name element has no given element') - else - local x = pandoc.utils.stringify(person.name.given) - res = res .. ', ' .. string.gsub(x, '([A-Z])[a-zàçéèïîôö]*', '%1.') - end - end - return res -end - -function shortauthor(author) - n = 0 - for i, person in pairs(author) do - n = i - end - z = '' - for i, person in pairs(author) do - z = z .. abbreviate_person(person, i, 'author', n) - end - return z -end - -function metadata_warning(warningmessage) - if FORMAT:match 'latex' then - return '{\\color{red}!!! ' .. warningmessage .. ' !!!}' - else - return '

!!! ' .. warningmessage .. ' !!!

' - end -end - -function conditional_format(txt) - if FORMAT:match 'latex' then - return pandoc.RawInline('latex', txt) - else - return pandoc.RawInline('html', txt) - end -end - -function publicationyear(pubdate) - local pattern = "^(%d%d%d%d)-(%d%d)-(%d%d+)$" - local runyear, runmonth, runday = pubdate:match(pattern) - if runyear == nil then - z = metadata_warning("flandersqmd.publicationdate not in YYYY-MM-DD format") - else - local ts = os.time({year = runyear, month = runmonth, day = runday}) - z = os.date("%Y", ts) - end - return z -end - -return { - { - Meta = function(meta) - if is_empty(meta.flandersqmd.entity) then - meta.translation = translation( - pandoc.utils.stringify(meta.lang), "INBO" - ) - else - meta.translation = translation( - pandoc.utils.stringify(meta.lang), - pandoc.utils.stringify(meta.flandersqmd.entity) - ) - end - if is_empty(meta.flandersqmd.author) then - meta.shortauthor = conditional_format( - metadata_warning('Missing flandersqmd.author') - ) - else - meta.shortauthor = shortauthor(meta.flandersqmd.author) - meta.ccby = pandoc.RawInline("latex", meta.translation.ccby) - end - if not is_empty(meta.flandersqmd.publicationdate) then - meta.publicationyear = publicationyear( - pandoc.utils.stringify(meta.flandersqmd.publicationdate) - ) - meta.fpublicationyear = conditional_format(meta.publicationyear) - end - return meta - end - } -} diff --git a/inst/interactive/.gitignore b/inst/interactive/.gitignore new file mode 100644 index 0000000..8e664d9 --- /dev/null +++ b/inst/interactive/.gitignore @@ -0,0 +1 @@ +.quarto diff --git a/inst/interactive/_quarto.yml b/inst/interactive/_quarto.yml index ca04ced..00c95d4 100644 --- a/inst/interactive/_quarto.yml +++ b/inst/interactive/_quarto.yml @@ -1,7 +1,6 @@ project: type: website output-dir: ../website - lib-dir: libs knitr: opts_chunk: @@ -13,73 +12,93 @@ execute: echo: false freeze: auto +bibliography: references.bib + +lang: nl-BE + format: - inboqmd-report-website-html: - bibliography: references.bib + flandersqmd-website-html: default flandersqmd: title: "Trends op basis van de Algemene Broedvogelmonitoring Vlaanderen (ABV)" - subtitle: "Technisch achtergrondrapport voor de periode 2007-2023" + subtitle: "Technisch achtergrondrapport voor de periode 2007-2025" + shorttitle: "abv-trends-2007-2025" author: - name: - given : Thierry + given: Thierry family: Onkelinx email: thierry.onkelinx@inbo.be orcid: 0000-0001-8804-4216 - affiliation: Research Institute for Nature and Forest (INBO) + affiliation: + - Instituut voor Natuur- en Bosonderzoek (INBO) corresponding: true - name: given: Olivier family: Dochy email: olivier.dochy@inbo.be orcid: 0009-0002-2822-5634 - affiliation: Research Institute for Nature and Forest (INBO) + affiliation: + - Instituut voor Natuur- en Bosonderzoek (INBO) - name: given: Glenn family: Vermeersch email: glenn.vermeersch@inbo.be orcid: 0000-0002-9176-8917 - affiliation: Research Institute for Nature and Forest (INBO) + affiliation: + - Instituut voor Natuur- en Bosonderzoek (INBO) - name: given: Koen family: Devos email: koen.devos@inbo.be orcid: 0000-0001-7265-6349 - affiliation: Research Institute for Nature and Forest (INBO) + affiliation: + - Instituut voor Natuur- en Bosonderzoek (INBO) reviewer: - name: given: Hans family: Van Calster email: hans.vancalster@inbo.be orcid: 0000-0001-8595-8426 - affiliation: Research Institute for Nature and Forest (INBO) - lang: nl-BE - style: INBO - funder: Research Institute for Nature and Forest (INBO) - rightsholder: Research Institute for Nature and Forest (INBO) + affiliation: + - Instituut voor Natuur- en Bosonderzoek (INBO) + entity: INBO + level: 2 + funder: + - name: + given: Instituut voor Natuur- en Bosonderzoek (INBO) + email: info@inbo.be + ror: 00j54wy13 + rightsholder: + - name: + given: Instituut voor Natuur- en Bosonderzoek (INBO) + email: info@inbo.be + ror: 00j54wy13 + publisher: Instituut voor Natuur- en Bosonderzoek (INBO) + license: CC-BY-4.0 community: inbo keywords: natuurindicator; trend; broedvogel; citizen science - publication_type: report - reportnr: 15 - year: 2024 - doi: 10.21436/inbor.102669823 - depotnr: D/2024/3241/046 + publication_type: publication-report + reportnr: 14 + year: 2026 + doi: 10.21436/inbor.141312967 + depotnr: D/2026/3241/066 bibliography: references.bib link-citations: TRUE site: bookdown::bookdown_site cooperation: - Natuurpunt Studie vzw - Coxiestraat 11, 2800 Mechelen - - https://www.natuurpunt.be - cooperation_logo: natuurpunt.png - coverphoto: https://img2.vildaphoto.net/asset/l/86103.jpg - coverdescription: Putter (_Carduelis carduelis_) eet zaden uit een kaardebol. (© Lars Soerink, Vildaphoto) - cover: cover.png - embargo: 2024-03-25 + cooperationurl: https://www.natuurpunt.be + cooperationlogo: natuurpunt.png + coverphoto: https://img3.vildaphoto.net/asset/l/186414.jpg + coverdescription: Halsbandparkiet _Psittacula krameri_ zittend op een tak. (© Yves Adams, Vildaphoto) + cover: cover.pdf + embargo: 2026-03-20 floatbarrier: section + hyphenation: + - do-cu-men-teert website: - title: Algemene Broedvogelmonitoring Vlaanderen (ABV) 2007-2023 navbar: left: - text: Cover @@ -87,16 +106,16 @@ website: - text: Samenvatting file: samenvatting.md - text: Methodologie - file: methodologie/index.qmd + file: methodologie/inzameling.qmd - text: Samengestelde indices file: index/bos.qmd - text: Trends per soort - file: soort/index.qmd + file: soort/trends.qmd tools: + - icon: download + href: onkelinx_etal_2025_abv.pdf - icon: mastodon href: https://mastodon.online/@inbo - - icon: twitter - href: https://twitter.com/inbovlaanderen - icon: facebook href: https://www.facebook.com/INBOVlaanderen/ - icon: github @@ -115,10 +134,9 @@ website: - samenvatting.md - abstract.md - section: "Methodologie" - file: methodologie/index.qmd contents: - text: "Inzameling van de tellingen in het veld" - file: methodologie/index.qmd + file: methodologie/inzameling.qmd - text: "Verwerking van de gegevens" file: methodologie/verwerking.qmd - text: "Voorstelling van de gegevens" diff --git a/inst/interactive/abstract.md b/inst/interactive/abstract.md index 7c6e390..a7b401b 100644 --- a/inst/interactive/abstract.md +++ b/inst/interactive/abstract.md @@ -1,3 +1,5 @@ +::: {lang=en-GB} + --- title: English abstract --- @@ -17,3 +19,14 @@ the second part presents a number of indicators based on trends of several types the third part describes the time series of individual species. In parts two and three, we limit ourselves to a standardised representation of the most relevant analysis results without an ecological interpretation. For an ecological interpretation of the results we refer to other publications such as [INBO Vogelnieuws](https://www.vlaanderen.be/inbo/publicaties/vogelnieuws). +::: + + +::: {.content-visible when-format="pdf"} +\clearpage +\phantomsection +\addcontentsline{toc}{chapter}{\contentsname} +\setcounter{tocdepth}{2} +\tableofcontents +::: + diff --git a/inst/interactive/composite.qmd b/inst/interactive/composite.qmd index 67238bc..6b612a8 100644 --- a/inst/interactive/composite.qmd +++ b/inst/interactive/composite.qmd @@ -2,7 +2,7 @@ title: "{{species}}" --- -```{r setup-{{label}}} +```{r setup} library(crosstalk) library(effectclass) library(git2rdata) @@ -11,31 +11,31 @@ library(gt) library(plotly) library(tidyverse) conflicted::conflicts_prefer(dplyr::filter, dplyr::pull, plotly::layout) +root <- system.file("results", package = "abvanalysis") if (interactive()) { - root <- file.path("..", "..", "..", "abv", "results") read_vc("meta", root) |> filter(.data$composite, .data$status == "converged") |> slice_sample(n = 1) -> this this_species <- this$speciesgroup } else { - root <- file.path("..", "..", "results") this_species <- "{{species}}" } threshold <- log(0.75) ``` -```{r load-data-{{label}}} +```{r load-data} read_vc("meta", root) |> - filter(.data$speciesgroup == this_species, , !.data$linear) |> + filter(.data$speciesgroup == this_species, !.data$linear) |> select("analysis", "cycle", "status_fingerprint") -> meta paste0( "Wijzigingen tussen jaren voor %s. ", "De band geeft het [90%% geloofwaardigheidsinterval]", - "(../methodologie/voorstelling.html#sec-onzekerheid). ", + "(../methodologie/voorstelling.html#sec-onzekerheid) in stappen van 30%%. ", "De [referentielijnen]", "(../methodologie/voorstelling.html#sec-trendklasse) ", "zorgen voor de opdeling van de wijzigingen in [klassen]", - "(../methodologie/voorstelling.html#sec-trendklasse)." + "(../methodologie/voorstelling.html#sec-trendklasse). ", + "Beweeg met de muis over de grafiek om de indeling zichtbaar te maken." ) |> sprintf(as.character(this_species)) -> caption_year_index paste0( @@ -45,14 +45,16 @@ paste0( "De [referentielijnen]", "(../methodologie/voorstelling.html#sec-trendklasse) ", "zorgen voor de opdeling van de wijzigingen in [klassen]", - "(../methodologie/voorstelling.html#sec-trendklasse)." + "(../methodologie/voorstelling.html#sec-trendklasse) in stappen van 30%%. ", + "Beweeg met de muis over de grafiek om de indeling zichtbaar te maken." ) |> sprintf(as.character(this_species)) -> caption_cycle_index meta |> filter(!.data$cycle) |> semi_join(x = read_vc("parent", root), by = "analysis") |> semi_join( - x = read_vc("meta", root), by = c("analysis" = "parent_analysis") + x = read_vc("meta", root), + by = c("analysis" = "parent") ) |> pull(.data$speciesgroup) |> as.character() |> @@ -61,6 +63,60 @@ str_replace_all(specieslist, " ", "-") |> tolower() |> sprintf(fmt = "[%2$s](../soort/%1$s.html)", specieslist) -> specieslist threshold <- threshold / sqrt(length(specieslist)) +meta |> + inner_join(x = read_vc("composite_index", root), by = "analysis") |> + group_by(.data$cycle) |> + slice_max(.data$alternative, n = 1, with_ties = TRUE) |> + filter(.data$reference %in% range(.data$reference)) |> + mutate( + estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, + lcl = ifelse(.data$cycle, 1 / 3, 1) * .data$lower_confidence_limit, + ucl = ifelse(.data$cycle, 1 / 3, 1) * .data$upper_confidence_limit, + klasse = classification(.data$lcl, .data$ucl, threshold), + wijziging = format_ci( + exp(.data$estimate) - 1, + lcl = exp(.data$lcl) - 1, + ucl = exp(.data$ucl) - 1, + percent = TRUE, + sign = TRUE + ) + ) |> + inner_join( + tribble( + ~tekst, ~y, "stabiel", 0, "toename", 1, "afname", -1, + "sterke toename", 1.5, "sterke afname", -1.5, "matige toename", 0.5, + "matige afname", -0.5, "mogelijke toename", 0.5, + "mogelijke afname", -0.5, "onduidelijk", 0 + ) |> + mutate( + s = c(rep(0.25, 7), 0.5, 0.5, 1), + lcl = qnorm(0.05, .data$y, .data$s), + ucl = qnorm(0.95, .data$y, .data$s) + ) |> + transmute(.data$tekst, klasse = classification(.data$lcl, .data$ucl, 1)), + by = "klasse" + ) |> + mutate( + .data$cycle, + verschil = sprintf( + "De toestand in %s verschilt met %s t.o.v. %s (%s).", + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$alternative, .data$alternative + 2), + .data$alternative + ), + .data$wijziging, + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$reference, .data$reference + 2), + .data$reference + ), + .data$tekst + ) + ) |> + summarise( + verschil = paste(.data$verschil, collapse = "\n"), .groups = "drop" + ) -> dit_verschil ``` ## Samenstelling van de index @@ -70,7 +126,9 @@ De aangepaste grenswaarden zijn `r sprintf("%+.1f%%", 100 * exp(threshold) - 100 ## Analyse per jaar -```{r fig-year-change-{{label}}} +`r dit_verschil$verschil[!dit_verschil$cycle]` + +```{r fig-year-change} #| warning: false #| fig-cap: !expr caption_year_index meta |> @@ -78,44 +136,70 @@ meta |> semi_join(x = read_vc("composite_index", root), by = "analysis") |> mutate( interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) ) -> nadien nadien |> mutate( - old = .data$reference, reference = .data$alternative, - alternative = .data$old, estimate = -.data$estimate, + old = .data$reference, + reference = .data$alternative, + alternative = .data$old, + estimate = -.data$estimate, old = .data$lower_confidence_limit, lower_confidence_limit = -.data$upper_confidence_limit, upper_confidence_limit = -.data$old, interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) ) -> voordien voordien |> @@ -124,11 +208,15 @@ voordien |> nadien |> distinct(.data$reference) |> mutate( - alternative = .data$reference, estimate = 0, lcl = 0, ucl = 0, - wijziging = "", interpretatie = "referentie" + alternative = .data$reference, + estimate = 0, + lcl = 0, + ucl = 0, + wijziging = "", + interpretatie = "referentie" ) ) -> jaarlijks -range(jaarlijks$alternative) + c(-1, 1) -> bereik_x +range(jaarlijks$reference) + c(-1, 1) -> bereik_x jaarlijks |> mutate(alternative = .data$alternative - 0.49999) |> bind_rows( @@ -138,13 +226,19 @@ jaarlijks |> ) |> arrange(.data$reference, .data$alternative) |> transmute( - .data$reference, .data$alternative, .data$estimate, - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$reference, + .data$alternative, + .data$estimate, + .data$lower_confidence_limit, + .data$upper_confidence_limit, sd = .data$upper_confidence_limit - .data$lower_confidence_limit, sd = .data$sd / 2 / qnorm(0.95), wijziging = sprintf( - "%.0f t.o.v. %i\n%s\n%s", round(.data$alternative), .data$reference, - .data$wijziging, .data$interpretatie + "%.0f t.o.v. %i\n%s\n%s", + round(.data$alternative), + .data$reference, + .data$wijziging, + .data$interpretatie ) |> str_replace_all(".*\n\nreferentie", "referentie"), key = sprintf("%.0f-%i", round(.data$alternative), .data$reference) @@ -156,7 +250,9 @@ jaarlijks |> ref_y = c(0, threshold, -threshold) |> c(rep(NA, n() - 3)), ref_text = c( - "toestand in", "belangrijke afname t.o.v.", "belangrijke toename t.o.v." + "toestand in", + "belangrijke afname t.o.v.", + "belangrijke toename t.o.v." ) |> paste(.data$reference), textposition = c("top left", "bottom left") |> @@ -165,8 +261,10 @@ jaarlijks |> ) |> SharedData$new(key = ~key) -> index_year c( - index_year$data(withFilter = FALSE)$lower_confidence_limit, threshold, - index_year$data(withFilter = FALSE)$upper_confidence_limit, -threshold + index_year$data(withFilter = FALSE)$lower_confidence_limit, + threshold, + index_year$data(withFilter = FALSE)$upper_confidence_limit, + -threshold ) |> range(na.rm = TRUE) -> bereik change_breaks(n = 5, extra = c(-threshold, threshold))( @@ -175,33 +273,60 @@ change_breaks(n = 5, extra = c(-threshold, threshold))( bscols( widths = c(10, 2), plot_ly(index_year, x = ~alternative, y = ~estimate) |> - add_fan(sd = ~sd, text = ~wijziging) |> + add_fan( + sd = ~sd, + text = ~wijziging, + hoverinfo = "text", + step = 0.3, + name = "index", + showlegend = FALSE + ) |> add_text( - x = ~ref_x, y = ~ref_y, text = ~ref_text, textposition = ~textposition, - color = I(inbo_hoofd), hoverinfo = "none" + x = ~ref_x, + y = ~ref_y, + text = ~ref_text, + textposition = ~textposition, + color = I(inbo_hoofd), + hoverinfo = "none", + showlegend = FALSE ) |> config( displaylogo = FALSE, modeBarButtonsToRemove = c( - "autoScale", "hoverCompareCartesian", "lasso2d", "select2d" + "autoScale", + "hoverCompareCartesian", + "lasso2d", + "select2d" ) ) |> layout( title = this_species, xaxis = list(title = list(font = list(size = 0)), range = bereik_x), yaxis = list( - title = list(font = list(size = 0)), zerolinewidth = 1, - tickvals = deze_breaks, ticktext = change_labels(deze_breaks) + title = list(font = list(size = 0)), + zerolinewidth = 1, + tickvals = deze_breaks, + ticktext = change_labels(deze_breaks) ), shapes = list( - type = "rect", x0 = 0, x1 = 1, xref = "paper", y0 = threshold, - y1 = -threshold, line = list(width = 0), opacity = 0.1, - fillcolor = "black", layer = "below" + type = "rect", + x0 = 0, + x1 = 1, + xref = "paper", + y0 = threshold, + y1 = -threshold, + line = list(width = 0), + opacity = 0.1, + fillcolor = "black", + layer = "below" ) ), filter_select( - id = "ref_year", label = "Referentiejaar", sharedData = index_year, - group = ~reference, multiple = FALSE + id = "ref_year", + label = "Referentiejaar", + sharedData = index_year, + group = ~reference, + multiple = FALSE ) ) %>% `class<-`(c(class(.), "htmlwidget")) @@ -209,7 +334,9 @@ bscols( ## Analyse per driejarige cyclus -```{r fig-cycle-change-{{label}}} +`r dit_verschil$verschil[dit_verschil$cycle]` + +```{r fig-cycle-change} #| warning: false #| fig-cap: !expr caption_cycle_index meta |> @@ -217,44 +344,70 @@ meta |> semi_join(x = read_vc("composite_index", root), by = "analysis") |> mutate( interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) ) -> nadien nadien |> mutate( - old = .data$reference, reference = .data$alternative, - alternative = .data$old, estimate = -.data$estimate, + old = .data$reference, + reference = .data$alternative, + alternative = .data$old, + estimate = -.data$estimate, old = .data$lower_confidence_limit, lower_confidence_limit = -.data$upper_confidence_limit, upper_confidence_limit = -.data$old, interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) ) -> voordien voordien |> @@ -263,8 +416,12 @@ voordien |> nadien |> distinct(.data$reference) |> mutate( - alternative = .data$reference, estimate = 0, lcl = 0, ucl = 0, - wijziging = "", interpretatie = "referentie" + alternative = .data$reference, + estimate = 0, + lcl = 0, + ucl = 0, + wijziging = "", + interpretatie = "referentie" ) ) |> mutate( @@ -272,8 +429,12 @@ voordien |> sd = .data$upper_confidence_limit - .data$lower_confidence_limit, sd = .data$sd / 2 / qnorm(0.95), wijziging = sprintf( - "%i-%i t.o.v. %s\n%s\n%s", .data$alternative, .data$alternative + 2, - .data$reference, .data$wijziging, .data$interpretatie + "%i-%i t.o.v. %s\n%s\n%s", + .data$alternative, + .data$alternative + 2, + .data$reference, + .data$wijziging, + .data$interpretatie ) |> str_replace_all(".*\n\nreferentie", "referentie") ) -> jaarlijks @@ -287,8 +448,13 @@ jaarlijks |> ) |> arrange(.data$reference, .data$alternative) |> select( - "reference", "alternative", "estimate", "lower_confidence_limit", - "upper_confidence_limit", "sd", "wijziging" + "reference", + "alternative", + "estimate", + "lower_confidence_limit", + "upper_confidence_limit", + "sd", + "wijziging" ) |> group_by(.data$reference) |> mutate( @@ -297,7 +463,9 @@ jaarlijks |> ref_y = c(0, threshold, -threshold) |> c(rep(NA, n() - 3)), ref_text = c( - "toestand in", "belangrijke afname t.o.v.", "belangrijke toename t.o.v." + "toestand in", + "belangrijke afname t.o.v.", + "belangrijke toename t.o.v." ) |> paste(.data$reference), textposition = c("top left", "bottom left") |> @@ -307,8 +475,10 @@ jaarlijks |> ) |> SharedData$new(key = ~key) -> index_cycle c( - index_cycle$data(withFilter = FALSE)$lower_confidence_limit, threshold, - index_cycle$data(withFilter = FALSE)$upper_confidence_limit, -threshold + index_cycle$data(withFilter = FALSE)$lower_confidence_limit, + threshold, + index_cycle$data(withFilter = FALSE)$upper_confidence_limit, + -threshold ) |> range(na.rm = TRUE) -> bereik change_breaks(n = 5, extra = c(-threshold, threshold))( @@ -317,33 +487,60 @@ change_breaks(n = 5, extra = c(-threshold, threshold))( bscols( widths = c(10, 2), plot_ly(index_cycle, x = ~alternative, y = ~estimate) |> - add_fan(sd = ~sd, text = ~wijziging) |> + add_fan( + sd = ~sd, + text = ~wijziging, + hoverinfo = "text", + step = 0.3, + name = "index", + showlegend = FALSE + ) |> add_text( - x = ~ref_x, y = ~ref_y, text = ~ref_text, textposition = ~textposition, - color = I(inbo_hoofd), hoverinfo = "none" + x = ~ref_x, + y = ~ref_y, + text = ~ref_text, + textposition = ~textposition, + color = I(inbo_hoofd), + hoverinfo = "none", + showlegend = FALSE ) |> config( displaylogo = FALSE, modeBarButtonsToRemove = c( - "autoScale", "hoverCompareCartesian", "lasso2d", "select2d" + "autoScale", + "hoverCompareCartesian", + "lasso2d", + "select2d" ) ) |> layout( title = this_species, xaxis = list(title = list(font = list(size = 0)), range = bereik_x), yaxis = list( - title = list(font = list(size = 0)), zerolinewidth = 1, - tickvals = deze_breaks, ticktext = change_labels(deze_breaks) + title = list(font = list(size = 0)), + zerolinewidth = 1, + tickvals = deze_breaks, + ticktext = change_labels(deze_breaks) ), shapes = list( - type = "rect", x0 = 0, x1 = 1, xref = "paper", y0 = threshold, - y1 = -threshold, line = list(width = 0), opacity = 0.1, - fillcolor = "black", layer = "below" + type = "rect", + x0 = 0, + x1 = 1, + xref = "paper", + y0 = threshold, + y1 = -threshold, + line = list(width = 0), + opacity = 0.1, + fillcolor = "black", + layer = "below" ) ), filter_select( - id = "ref_cycle", label = "Referentieperiode", sharedData = index_cycle, - group = ~reference, multiple = FALSE + id = "ref_cycle", + label = "Referentieperiode", + sharedData = index_cycle, + group = ~reference, + multiple = FALSE ) ) %>% `class<-`(c(class(.), "htmlwidget")) @@ -351,14 +548,17 @@ bscols( ## Kenmerken van de analyses -```{r tbl-hash-{{label}}} +Het cijfermateriaal om de figuren te reproduceren is beschikbaar via de [download pagina](../methodologie/reproduceerbaarheid.html#downloadbaar-cijfermateriaal). + +```{r tbl-hash} #| tbl-cap: Data-hashes van de analyses in het kader van [traceerbaarheid]( #| ../methodologie/reproduceerbaarheid.html#sec-traceerbaarheid). meta |> arrange(.data$cycle) |> transmute( frequentie = factor( - .data$cycle, levels = c(TRUE, FALSE), + .data$cycle, + levels = c(TRUE, FALSE), labels = c("driejaarlijks", "jaarlijks") ), analyse = str_replace(.data$analysis, "(.{20})", "\\1\n"), @@ -368,13 +568,13 @@ meta |> cols_width(analyse ~ px(20), status ~ px(20)) ``` -```{js change-js-{{label}}} +```{js change-js} function filter_default() { document.getElementById("ref_year").getElementsByClassName("selectized")[0].selectize.removeOption(""); - document.getElementById("ref_year").getElementsByClassName("selectized") + document.getElementById("ref_year").getElementsByClassName("selectized") [0].selectize.setValue("2007", false); document.getElementById("ref_cycle").getElementsByClassName("selectized")[0].selectize.removeOption(""); - document.getElementById("ref_cycle").getElementsByClassName("selectized") + document.getElementById("ref_cycle").getElementsByClassName("selectized") [0].selectize.setValue("2007-2009", false); } $(document).ready(filter_default); diff --git a/inst/interactive/composite_pdf.qmd b/inst/interactive/composite_pdf.qmd new file mode 100644 index 0000000..c415869 --- /dev/null +++ b/inst/interactive/composite_pdf.qmd @@ -0,0 +1,374 @@ +--- +title: "{{species}}" +--- + +```{r} +#| label: setup-{{label}} +library(effectclass) +library(git2rdata) +library(INBOtheme) +library(kableExtra) +library(knitr) +library(tidyverse) +conflicted::conflicts_prefer(dplyr::filter, dplyr::pull) +update_geom_defaults("point", list(size = 2)) +update_geom_defaults("text", list(size = 3)) +theme_set(theme_inbo(base_size = 8)) +root <- system.file("results", package = "abvanalysis") +if (interactive()) { + read_vc("meta", root) |> + filter(.data$composite, .data$status == "converged") |> + slice_sample(n = 1) -> this + this_species <- this$speciesgroup +} else { + this_species <- "{{species}}" +} +threshold <- log(0.75) +c(rev(traffic_palette(7)), "grey65", "grey35", "grey50") |> + setNames( + c("++", "+", "+~", "~", "-~", "-", "--", "?+", "?-", "?") + ) -> kleurgradient +kleurgradient[4] <- inbo_steun_blauw +periode_labels <- function(x) { + sprintf("%i - %i", x + 2007, x + 2009) +} +cycle_breaks <- function(x) { + round(min(x)):round(max(x)) +} +``` + +```{r} +#| label: load-data-{{label}} +read_vc("meta", root) |> + filter(.data$speciesgroup == this_species, !.data$linear) |> + select("analysis", "cycle", "status_fingerprint") -> meta +meta |> + filter(!.data$cycle) |> + semi_join(x = read_vc("parent", root), by = "analysis") |> + semi_join( + x = read_vc("meta", root), + by = c("analysis" = "parent") + ) |> + pull(.data$speciesgroup) |> + as.character() |> + sort() -> specieslist +str_replace_all(specieslist, " ", "-") |> + tolower() |> + sprintf(fmt = "[%2$s](../soort/%1$s.html)", specieslist) -> specieslist +threshold <- threshold / sqrt(length(specieslist)) +meta |> + inner_join(x = read_vc("composite_index", root), by = "analysis") |> + group_by(.data$cycle) |> + slice_max(.data$alternative, n = 1, with_ties = TRUE) |> + filter(.data$reference %in% range(.data$reference)) |> + mutate( + estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, + lcl = ifelse(.data$cycle, 1 / 3, 1) * .data$lower_confidence_limit, + ucl = ifelse(.data$cycle, 1 / 3, 1) * .data$upper_confidence_limit, + klasse = classification(.data$lcl, .data$ucl, threshold), + wijziging = format_ci( + exp(.data$estimate) - 1, + lcl = exp(.data$lcl) - 1, + ucl = exp(.data$ucl) - 1, + percent = TRUE, + sign = TRUE + ) + ) |> + inner_join( + tribble( + ~tekst, ~y, "stabiel", 0, "toename", 1, "afname", -1, + "sterke toename", 1.5, "sterke afname", -1.5, "matige toename", 0.5, + "matige afname", -0.5, "mogelijke toename", 0.5, + "mogelijke afname", -0.5, "onduidelijk", 0 + ) |> + mutate( + s = c(rep(0.25, 7), 0.5, 0.5, 1), + lcl = qnorm(0.05, .data$y, .data$s), + ucl = qnorm(0.95, .data$y, .data$s) + ) |> + transmute( + .data$tekst, klasse = classification(.data$lcl, .data$ucl, 1) + ), + by = "klasse" + ) |> + mutate( + .data$cycle, + verschil = sprintf( + "De toestand in %s verschilt met %s t.o.v. %s (%s).", + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$alternative, .data$alternative + 2), + .data$alternative + ), + .data$wijziging, + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$reference, .data$reference + 2), + .data$reference + ), + .data$tekst + ) + ) |> + summarise( + verschil = paste(.data$verschil, collapse = "\n"), .groups = "drop" + ) -> dit_verschil +``` + +## Samenstelling van de index + +Deze index is gebaseerd op volgende `r length(specieslist)` soorten: `r paste(specieslist, collapse = ", ")`. +De aangepaste grenswaarden zijn `r sprintf("%+.1f%%", 100 * exp(threshold) - 100)` en `r sprintf("%+.1f%%", 100 * exp(-threshold) - 100)`. + +## Analyse per jaar {#sec-jaar-{{label}}} + +`r dit_verschil$verschil[!dit_verschil$cycle]` + +```{r} +#| label: fig-year-change-{{label}} +#| warning: false +#| fig-cap: Wijzigingen t.o.v. het referentiejaar 2007 voor {{species}}. +#| De band geeft het 90% geloofwaardigheidsinterval (§[-@sec-onzekerheid]) in +#| stappen van 5%. +#| De referentielijnen (§[-@sec-trendklasse]) zorgen voor de opdeling van de +#| wijzigingen in klassen. +#| Gebruik deze figuur om de wijzigingen t.o.v. het referentiejaar 2007 in te +#| schatten. +meta |> + filter(!.data$cycle) |> + semi_join(x = read_vc("composite_index", root), by = "analysis") -> nadien +nadien |> + mutate( + old = .data$reference, + reference = .data$alternative, + alternative = .data$old, + estimate = -.data$estimate, + old = .data$lower_confidence_limit, + lower_confidence_limit = -.data$upper_confidence_limit, + upper_confidence_limit = -.data$old + ) |> + bind_rows(nadien) |> + mutate( + interpretatie = classification( + .data$lower_confidence_limit, + .data$upper_confidence_limit, + threshold = threshold + ) + ) -> jaarlijks +nadien |> + filter(.data$reference == min(.data$reference)) -> oldest_ref +change_breaks(n = 4, extra = c(threshold, -threshold))( + c(oldest_ref$lower_confidence_limit, oldest_ref$upper_confidence_limit) +) -> deze_breaks +oldest_ref |> + mutate(sd = (.data$upper_confidence_limit - .data$estimate) / qnorm(0.975)) |> + ggplot(aes(x = alternative, y = estimate)) + + annotate( + geom = "rect", + xmin = -Inf, + xmax = Inf, + ymin = threshold, + ymax = -threshold, + alpha = 0.2, + fill = inbo_grijs + ) + + geom_hline(yintercept = 0) + + stat_fan(aes(link_sd = sd), geom = "rect") + + annotate( + geom = "text", + x = Inf, + y = 0, + hjust = 1, + vjust = -0.1, + label = "toestand in 2007", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = threshold, + hjust = 1, + vjust = 1, + label = "belangrijke afname t.o.v. 2007", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = -threshold, + hjust = 1, + vjust = 0, + label = "belangrijke toename t.o.v. 2007", + colour = inbo_hoofd + ) + + theme(axis.title = element_blank()) + + scale_y_continuous(breaks = deze_breaks, labels = change_labels) +``` + +```{r} +#| label: fig-year-matrix-{{label}} +#| fig.cap: Interpretatie van het verschil van een bepaald jaar (kolom) t.o.v. +#| een bepaald referentiejaar (rij) voor {{species}}. +#| Uitleg van de symbolen in tabel @tbl-regels. +#| Gebruik deze figuur om het verschil tussen twee jaren in te schatten. +#| Zie §[-@sec-paarsgewijs] voor meer uitleg over deze figuur. +ggplot(jaarlijks, aes(x = alternative, y = reference, label = interpretatie)) + + geom_point(aes(colour = interpretatie), size = 4, show.legend = FALSE) + + scale_y_continuous("referentie") + + scale_colour_manual(values = kleurgradient) + + geom_text( + aes(label = interpretatie), + hjust = 0.5, + vjust = 0.5, + colour = "white", + size = 2 + ) + + coord_fixed() + + theme( + axis.title.x = element_blank(), + axis.text.x = element_text(angle = 90, vjust = 0.5) + ) +``` + +## Analyse per driejarige cyclus {#sec-cyclus-{{label}}} + +`r dit_verschil$verschil[dit_verschil$cycle]` + +```{r} +#| label: fig-cycle-change-{{label}} +#| warning: false +#| fig-cap: Wijzigingen t.o.v. de referentieperiode 2007-2009 voor {{species}}. +#| De band geeft het 90% geloofwaardigheidsinterval (§[-@sec-onzekerheid]) in +#| stappen van 5%. +#| De referentielijnen (§[-@sec-trendklasse]) zorgen voor de opdeling van de +#| wijzigingen in klassen. +#| Gebruik deze figuur om de wijzigingen t.o.v. het referentiejaar 2007 in te +#| schatten. +meta |> + filter(.data$cycle) |> + semi_join(x = read_vc("composite_index", root), by = "analysis") |> + mutate(across(c("reference", "alternative"), function(x) { + (x - 2007) %/% 3 + })) -> nadien +nadien |> + mutate( + old = .data$reference, + reference = .data$alternative, + alternative = .data$old, + estimate = -.data$estimate, + old = .data$lower_confidence_limit, + lower_confidence_limit = -.data$upper_confidence_limit, + upper_confidence_limit = -.data$old + ) |> + bind_rows(nadien) |> + mutate( + interpretatie = classification( + .data$lower_confidence_limit, + .data$upper_confidence_limit, + threshold = threshold + ) + ) -> jaarlijks +nadien |> + filter(.data$reference == min(.data$reference)) -> oldest_ref +change_breaks(n = 4, extra = c(threshold, -threshold))( + c(oldest_ref$lower_confidence_limit, oldest_ref$upper_confidence_limit) +) -> deze_breaks +oldest_ref |> + mutate(sd = (.data$upper_confidence_limit - .data$estimate) / qnorm(0.975)) |> + ggplot(aes(x = alternative, y = estimate)) + + annotate( + geom = "rect", + xmin = -Inf, + xmax = Inf, + ymin = threshold, + ymax = -threshold, + alpha = 0.2, + fill = inbo_grijs + ) + + geom_hline(yintercept = 0) + + stat_fan(aes(link_sd = sd), geom = "rect") + + annotate( + geom = "text", + x = Inf, + y = 0, + hjust = 1, + vjust = -0.1, + label = "toestand in 2007-2009", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = threshold, + hjust = 1, + vjust = 1, + label = "belangrijke afname t.o.v. 2007-2009", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = -threshold, + hjust = 1, + vjust = 0, + label = "belangrijke toename t.o.v. 2007-2009", + colour = inbo_hoofd + ) + + theme(axis.title = element_blank()) + + scale_x_continuous(breaks = cycle_breaks, labels = periode_labels) + + scale_y_continuous(breaks = deze_breaks, labels = change_labels) +``` + +```{r} +#| label: fig-cycle-matrix-{{label}} +#| fig.cap: Interpretatie van het verschil van een bepaalde periode (kolom) +#| t.o.v. een bepaald referentiejaar (rij) voor {{species}}. +#| Uitleg van de symbolen in tabel @tbl-regels. +#| Gebruik deze figuur om het verschil tussen twee periodes in te schatten. +#| Zie §[-@sec-paarsgewijs] voor meer uitleg over deze figuur. +ggplot(jaarlijks, aes(x = alternative, y = reference, label = interpretatie)) + + geom_point(aes(colour = interpretatie), size = 4, show.legend = FALSE) + + scale_x_continuous(breaks = cycle_breaks, labels = periode_labels) + + scale_y_continuous( + "referentie", + breaks = cycle_breaks, + labels = periode_labels + ) + + scale_colour_manual(values = kleurgradient) + + geom_text( + aes(label = interpretatie), + hjust = 0.5, + vjust = 0.5, + colour = "white", + size = 2 + ) + + coord_fixed() + + theme( + axis.title.x = element_blank(), + axis.text.x = element_text(angle = 90, vjust = 0.5) + ) +``` + +## Kenmerken van de analyses + +Het cijfermateriaal om de figuren te reproduceren is beschikbaar via de [download pagina](../methodologie/reproduceerbaarheid.html#downloadbaar-cijfermateriaal). + +```{r} +#| label: tbl-hash-{{label}} +#| tbl-cap: Data-hashes van de analyses in het kader van traceerbaarheid +#| (§[-@sec-traceerbaarheid]). +meta |> + arrange(.data$cycle) |> + transmute( + frequentie = factor( + .data$cycle, + levels = c(TRUE, FALSE), + labels = c("driejaarlijks", "jaarlijks") + ), + analyse = str_replace(.data$analysis, "(.{20})", "\\1 "), + status = str_replace(.data$status_fingerprint, "(.{20})", "\\1 ") + ) |> + kable(format = "pipe") |> + column_spec(1, width = "15mm") |> + column_spec(2, width = "35mm") |> + column_spec(3, width = "35mm") +``` diff --git a/inst/interactive/cover.pdf b/inst/interactive/cover.pdf new file mode 100644 index 0000000..27d2422 Binary files /dev/null and b/inst/interactive/cover.pdf differ diff --git a/inst/interactive/cover.png b/inst/interactive/cover.png deleted file mode 100644 index 6bbc514..0000000 Binary files a/inst/interactive/cover.png and /dev/null differ diff --git a/inst/interactive/index.md b/inst/interactive/index.md index 62a21c2..624b502 100644 --- a/inst/interactive/index.md +++ b/inst/interactive/index.md @@ -1,61 +1 @@ ---- - title: "Trends op basis van de Algemene Broedvogelmonitoring Vlaanderen (ABV)" -subtitle: "Technisch achtergrondrapport voor de periode 2007-2023" -author: - - name: - given : Thierry -family: Onkelinx -email: thierry.onkelinx@inbo.be -orcid: 0000-0001-8804-4216 -affiliation: Research Institute for Nature and Forest (INBO) -corresponding: true -- name: - given: Olivier -family: Dochy -email: olivier.dochy@inbo.be -orcid: 0009-0002-2822-5634 -affiliation: Research Institute for Nature and Forest (INBO) -- name: - given: Glenn -family: Vermeersch -email: glenn.vermeersch@inbo.be -orcid: 0000-0002-9176-8917 -affiliation: Research Institute for Nature and Forest (INBO) -- name: - given: Koen -family: Devos -email: koen.devos@inbo.be -orcid: 0000-0001-7265-6349 -affiliation: Research Institute for Nature and Forest (INBO) -reviewer: - - name: - given: Hans -family: Van Calster -email: hans.vancalster@inbo.be -orcid: 0000-0001-8595-8426 -affiliation: Research Institute for Nature and Forest (INBO) -lang: nl -style: INBO -funder: Research Institute for Nature and Forest (INBO) -rightsholder: Research Institute for Nature and Forest (INBO) -community: inbo -keywords: natuurindicator; trend; broedvogel; citizen science -publication_type: report -reportnr: 15 -year: 2024 -doi: 10.21436/inbor.102669823 -depotnr: D/2024/3241/046 -bibliography: references.bib -link-citations: TRUE -site: bookdown::bookdown_site -cooperation: - - Natuurpunt Studie vzw -- Coxiestraat 11, 2800 Mechelen -- https://www.natuurpunt.be -cooperation_logo: natuurpunt.png -cover_photo: https://img2.vildaphoto.net/asset/l/86103.jpg -cover_description: Putter (_Carduelis carduelis_) eet zaden uit een kaardebol. (© Lars Soerink, Vildaphoto) -cover: cover.pdf -embargo: 2024-03-25 -floatbarrier: section ---- +{{< colophon >}} diff --git a/inst/interactive/interactive.Rproj b/inst/interactive/interactive.Rproj index cde2e35..1930a6e 100644 --- a/inst/interactive/interactive.Rproj +++ b/inst/interactive/interactive.Rproj @@ -1,4 +1,5 @@ Version: 1.0 +ProjectId: 2e8148e0-2851-43a6-8b1a-1911ea441837 RestoreWorkspace: Default SaveWorkspace: Default diff --git a/inst/interactive/methodologie/index.qmd b/inst/interactive/methodologie/inzameling.qmd similarity index 84% rename from inst/interactive/methodologie/index.qmd rename to inst/interactive/methodologie/inzameling.qmd index 46ebd90..88abc1d 100644 --- a/inst/interactive/methodologie/index.qmd +++ b/inst/interactive/methodologie/inzameling.qmd @@ -7,11 +7,12 @@ bibliography: ../references.bib library(git2rdata) library(INBOtheme) library(tidyverse) -if (interactive()) { - root <- file.path("..", "..", "..", "..", "abv", "results") -} else { - root <- file.path("..", "..", "results") -} +update_geom_defaults( + "point", + list(size = ifelse(knitr::is_html_output(), 4, 2)) +) +theme_set(theme_inbo(base_size = 8)) +root <- system.file("results", package = "abvanalysis") strata <- read_vc("stratum", root) effort <- read_vc("effort", root) ``` @@ -53,39 +54,52 @@ Voor het onderscheid tussen mogelijk bruikbaar en voorlopig niet bruikbaar verwi #| Elk punt is een hok dat onderzocht is in een bepaald jaar. #| De horizontale lijnen verbinden het eerste en meest recente bezoek aan #| eenzelfde hok. -#| fig.height: 10 +#| fig.height: 9 effort |> arrange(.data$year) |> group_by(.data$location) |> transmute( - .data$year, end = max(.data$year), year2 = lead(.data$year), - year3 = lead(.data$year2), year4 = lead(.data$year3), - year5 = lead(.data$year4), year6 = lead(.data$year5), + .data$year, + end = max(.data$year), + year2 = lead(.data$year), + year3 = lead(.data$year2), + year4 = lead(.data$year3), + year5 = lead(.data$year4), + year6 = lead(.data$year5), year7 = lead(.data$year6) ) |> slice(1) |> ungroup() |> arrange( - .data$year, .data$end, .data$year2, .data$year3, .data$year4, .data$year5, - .data$year6, .data$year7 + .data$year, + .data$end, + .data$year2, + .data$year3, + .data$year4, + .data$year5, + .data$year6, + .data$year7 ) |> rowid_to_column("hok") |> transmute( - .data$location, hok = factor(.data$hok), + .data$location, + hok = factor(.data$hok), bruikbaar = ((.data$year - 2007) %/% 3) < ((end - 2007) %/% 3), bruikbaar = factor( - .data$bruikbaar, levels = c(TRUE, FALSE), + .data$bruikbaar, + levels = c(TRUE, FALSE), labels = c("mogelijk", "voorlopig\nniet") ) ) |> inner_join(effort, by = "location") -> inspanning ggplot(inspanning, aes(x = year, y = hok, group = hok, colour = bruikbaar)) + geom_line(alpha = 0.2) + - geom_point(alpha = 0.5, size = 4) + + geom_point(alpha = 0.5) + facet_wrap(~stratum, scales = "free_y") + scale_x_continuous(breaks = pretty) + theme( - axis.title.x = element_blank(), axis.text.x = element_text(angle = 90), + axis.title.x = element_blank(), + axis.text.x = element_text(angle = 90), axis.text.y = element_blank() ) ``` @@ -99,7 +113,8 @@ inspanning |> geom_histogram(binwidth = 1) + facet_wrap(~stratum) + scale_x_continuous( - "Aantal jaren waarin een hok onderzocht is", breaks = pretty, + "Aantal jaren waarin een hok onderzocht is", + breaks = pretty, limits = c(0, NA) ) + ylab("Aantal hokken") @@ -122,9 +137,12 @@ expand.grid( ) |> ggplot(aes(x = x, y = y)) + geom_rect( - data = tribble(~x, ~y, ~xend, ~yend, 0, 0, 1, 1), + data = tribble( + ~x, ~y, ~xend, ~yend, 0, 0, 1, 1 + ), aes(xmin = x, ymin = y, xmax = xend, ymax = yend), - fill = NA, colour = inbo_steun_blauw + fill = NA, + colour = inbo_steun_blauw ) + geom_point() + coord_fixed() + diff --git a/inst/interactive/methodologie/reproduceerbaarheid.qmd b/inst/interactive/methodologie/reproduceerbaarheid.qmd index 2c2df45..ce76afb 100644 --- a/inst/interactive/methodologie/reproduceerbaarheid.qmd +++ b/inst/interactive/methodologie/reproduceerbaarheid.qmd @@ -7,14 +7,15 @@ bibliography: ../references.bib library(effectclass) library(git2rdata) library(tidyverse) +root <- system.file("results", package = "abvanalysis") if (interactive()) { output_dir <- file.path("..", "..", "..", "..", "abv", "web") } else { - root <- file.path("..", "..", "results") output_dir <- "../data" dir.create(output_dir, showWarnings = FALSE) } threshold <- log(0.75) +format <- knitr::opts_knit$get("rmarkdown.pandoc.to") ``` ## Versiebeheer @@ -43,7 +44,7 @@ Deze laatste bevat de nodige links naar de tekstbestanden en hun versie. De `n2kanalysis` objecten hebben elk twee data hashes. Elk data hash is een reeks van 40 hexadecimale^[Hexadecimaal betekent letterlijk zestientallig. Het is een talstelsel waarbij niet, zoals gebruikelijk, met tien cijfers wordt gewerkt, maar met zestien cijfers. -De cijfers 0 t.e.m. 9 worden daarom uitgebreid met `a` (=10) t.e.m. `f` (=15).] cijfers die het resultaat zijn van de cryptografische [hashfunctie](https://nl.wikipedia.org/wiki/Hashfunctie) [SHA-1](https://nl.wikipedia.org/wiki/SHA-familie). +De cijfers 0 t.e.m. 9 worden daarom uitgebreid met `a` (=10) t.e.m. `f` (=15).] cijfers die het resultaat zijn van de cryptografische [hashfunctie](https://nl.wikipedia.org/wiki/Hashfunctie) [`SHA-1`](https://nl.wikipedia.org/wiki/SHA-familie). Deze hashfunctie heeft een aantal belangrijke eigenschappen: 1. Ze zetten elke invoer om naar een uitvoer met vaste lengte (40 hexadecimale cijfers). @@ -84,6 +85,7 @@ Elke dataset bestaat uit twee bestanden: ```{r samengesteld} #| results: hide +#| eval: !expr format == "html" read_vc("meta", root) |> filter(.data$composite, .data$status == "converged") -> indices indices |> @@ -91,13 +93,13 @@ indices |> select(indicator = "speciesgroup", "analysis") |> inner_join( read_vc("parent", root) |> - select("analysis", "parent_analysis"), + select("analysis", "parent"), by = "analysis" ) |> inner_join( read_vc("meta", root) |> select("analysis", soort = "speciesgroup"), - by = c("parent_analysis" = "analysis") + by = c("parent" = "analysis") ) |> add_count(.data$indicator) |> transmute( @@ -109,9 +111,18 @@ indices |> ) -> soorten write_vc( soorten, "samengesteld_soorten", root = "../data", optimize = FALSE, - sorting = c("indicator", "soort") + sorting = c("indicator", "soort"), strict = FALSE ) read_vc("composite_index", root) |> + rename(old = "reference", reference = "alternative") |> + rename(alternative = "old") |> + mutate( + estimate = -.data$estimate, old = -.data$lower_confidence_limit, + lower_confidence_limit = -.data$upper_confidence_limit, + upper_confidence_limit = .data$old + ) |> + select(-"old") |> + bind_rows(read_vc("composite_index", root)) |> inner_join(indices, by = "analysis") |> inner_join( soorten |> @@ -135,35 +146,55 @@ read_vc("composite_index", root) |> percent = TRUE, sign = TRUE, change = TRUE ), across(c("schatting", "lcl", "ucl"), exp), - across(c("schatting", "lcl", "ucl"), round, 4), analyse = factor(.data$analysis), status = factor(.data$status_fingerprint) ) |> write_vc( - file = "samengesteld", root = "../data", optimize = FALSE, - sorting = c("indicator", "frequentie", "referentie", "naar") + file = "samengesteld", root = "../data", optimize = FALSE, digits = 4, + sorting = c("indicator", "frequentie", "referentie", "naar"), strict = FALSE ) ``` -- **samengestelde indices:** [`samengesteld.csv`](../data/samengesteld.csv) en [`samengesteld.yml`](../data/samengesteld.yml). - - `indicator`: naam van de indicator; - - `frequentie`: index op basis van jaarlijkse of driejaarlijkse gegevens; - - `referentie`: het referentiejaartal; - - `naar`: het jaartal waarvoor we verhouding het referentiejaar berekenen; - - `schatting`: de geschatte verhouding van dit jaar t.o.v. het referentiejaar; - - `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; - - `klasse`: [interpretatie](voorstelling.html#sec-trendklasse) van de wijziging; - - `wijziging`: tekstuele beschrijving van de evolutie; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **soortenlijst samengestelde indices:** [`samengesteld_soorten.csv`](../data/samengesteld_soorten.csv) en [`samengesteld_soorten.yml`](../data/samengesteld_soorten.yml). - - `soort`: naam van de soort; - - `indicator`: naam van de samengestelde indicator; - - `ondergrens` en `bovengrens`: grenzen voor de opdeling van de effecten in [klassen](voorstelling.html#sec-trendklasse) + +```{r} +#| label: link-html +#| eval: !expr format == "html" +lineair <- "[jaarlijkse wijziging](verwerking.html#sec-lineairetrend)" +interpretatie <- "[interpretatie](voorstelling.html#sec-trendklasse)" +stratum <- "[gewicht van het stratum](verwerking.html#sec-stratumgewicht)" +``` +```{r} +#| label: link-pdf +#| eval: !expr format != "html" +lineair <- "jaarlijkse wijziging (§[@-sec-lineairetrend])" +interpretatie <- "interpretatie (§[@-sec-trendklasse])" +stratum <- "gewicht van het stratum (§[@-sec-stratumgewicht])" +``` + + +#### Samengestelde indices + +- `indicator`: naam van de indicator; +- `frequentie`: index op basis van jaarlijkse of driejaarlijkse gegevens; +- `referentie`: het referentiejaartal; +- `naar`: het jaartal waarvoor we verhouding het referentiejaar berekenen; +- `schatting`: de geschatte verhouding van dit jaar t.o.v. het referentiejaar; +- `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; +- `klasse`: `r interpretatie` van de wijziging; +- `wijziging`: tekstuele beschrijving van de evolutie; +- `analyse`: data-hash van de analyse; +- `status`: data-hash van de status van de analyse + +#### Soortenlijst samengestelde indices + +- `soort`: naam van de soort; +- `indicator`: naam van de samengestelde indicator; +- `ondergrens` en `bovengrens`: grenzen voor de `r interpretatie` van de effecten in klassen. ### Individuele soorten ```{r soorten} #| results: hide +#| eval: !expr format == "html" read_vc("estimate", root) |> inner_join(read_vc("meta", root), by = "analysis") |> transmute( @@ -171,17 +202,17 @@ read_vc("estimate", root) |> frequentie = ifelse(.data$cycle, "driejaarlijks", "jaarlijks") |> factor(), jaar = .data$year, schatting = .data$estimate, - lcl = .data$lower_confidence_limit, ucl = .data$upper_confidence_limit, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, wijziging = format_ci( .data$schatting, lcl = .data$lcl, ucl = .data$ucl, link = "log" ), across(c("schatting", "lcl", "ucl"), exp), - across(c("schatting", "lcl", "ucl"), round, 4), analyse = factor(.data$analysis), status = factor(.data$status_fingerprint) ) |> write_vc( - file = "aantallen", root = "../data", optimize = FALSE, - sorting = c("soort", "frequentie", "jaar") + file = "aantallen", root = "../data", optimize = FALSE, digits = 4, + sorting = c("soort", "frequentie", "jaar"), strict = FALSE ) read_vc("linear_trend", root) |> inner_join(read_vc("meta", root), by = "analysis") |> @@ -191,6 +222,9 @@ read_vc("linear_trend", root) |> summarise(looptijd = max(.data$year) - 1), by = "analysis" ) |> + group_by(.data$speciesgroup) |> + mutate(looptijd = max(.data$looptijd)) |> + ungroup() |> inner_join( read_vc("meta", root) |> select("speciesgroup", "cycle", "linear", "waic") |> @@ -205,29 +239,39 @@ read_vc("linear_trend", root) |> ), by = c("speciesgroup", "cycle") ) |> + mutate(period = ifelse(.data$cycle, 3, 1)) |> transmute( soort = .data$speciesgroup, frequentie = ifelse(.data$cycle, "driejaarlijks", "jaarlijks") |> factor(), jaarlijks = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, percent = TRUE, - ucl = .data$upper_confidence_limit, link = "log", change = TRUE, - sign = TRUE + .data$estimate / .data$period, link = "log", change = TRUE, sign = TRUE, + lcl = .data$lower_confidence_limit / .data$period, percent = TRUE, + ucl = .data$upper_confidence_limit / .data$period ), - looptijd = .data$looptijd * ifelse(.data$cycle, 3, 1), looptijd = format_ci( - .data$looptijd * .data$estimate, change = TRUE, sign = TRUE, - lcl = .data$looptijd * .data$lower_confidence_limit, percent = TRUE, - ucl = .data$looptijd * .data$upper_confidence_limit, link = "log" + .data$looptijd * .data$estimate / .data$period, change = TRUE, + lcl = .data$looptijd * .data$lower_confidence_limit / .data$period, + ucl = .data$looptijd * .data$upper_confidence_limit / .data$period, + sign = TRUE, link = "log", percent = TRUE ), .data$verloop, analyse = factor(.data$analysis), status = factor(.data$status_fingerprint), ) |> write_vc( - file = "lineare_trend", root = "../data", optimize = FALSE, - sorting = c("soort", "frequentie") + file = "lineare_trend", root = "../data", optimize = FALSE, digits = 4, + sorting = c("soort", "frequentie"), strict = FALSE ) read_vc("index", root) |> + rename(old = "reference", reference = "alternative") |> + rename(alternative = "old") |> + mutate( + estimate = -.data$estimate, old = -.data$lower_confidence_limit, + lower_confidence_limit = -.data$upper_confidence_limit, + upper_confidence_limit = .data$old + ) |> + select(-"old") |> + bind_rows(read_vc("index", root)) |> inner_join(read_vc("meta", root), by = "analysis") |> transmute( soort = factor(.data$speciesgroup), @@ -245,12 +289,11 @@ read_vc("index", root) |> percent = TRUE, sign = TRUE, change = TRUE ), across(c("schatting", "lcl", "ucl"), exp), - across(c("schatting", "lcl", "ucl"), round, 4), analyse = factor(.data$analysis), status = factor(.data$status_fingerprint) ) |> write_vc( - file = "index", root = "../data", optimize = FALSE, - sorting = c("soort", "frequentie", "referentie", "naar") + file = "index", root = "../data", optimize = FALSE, digits = 4, + sorting = c("soort", "frequentie", "referentie", "naar"), strict = FALSE ) read_vc("effort", root) |> distinct(.data$stratum, .data$location) |> @@ -279,44 +322,89 @@ read_vc("effort", root) |> ) |> write_vc( file = "stratum_gewicht", root = "../data", optimize = FALSE, - sorting = c("soort", "stratum") + sorting = c("soort", "stratum"), strict = FALSE ) ``` -- **gemiddeld aantal op een meetpunt:** [`aantallen.csv`](../data/aantallen.csv) en [`aantallen.yml`](../data/aantallen.yml). - - `soort`: Nederlandse soortnaam; - - `frequentie`: trend op basis van jaarlijkse of driejaarlijkse gegevens; - - `jaar`: jaartal of startjaar van de driejaarlijkse periode; - - `schatting`: schatting van het gemiddeld aantal dieren op een meetpunt; - - `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **lineaire trends:** [`lineaire_trend.csv`](../data/lineaire_trend.csv) en [`lineaire_trend.yml`](../data/lineaire_trend.yml). - - `soort`: Nederlandse soortnaam; - - `frequentie`: trend op basis van jaarlijkse of driejaarlijkse gegevens; - - `jaarlijks`: gemiddelde [jaarlijkse wijziging](verwerking.html#sec-lineairetrend); - - `looptijd`: gemiddelde wijziging over de looptijd van de tijdsreeks; - - `klasse`: [interpretatie](voorstelling.html#sec-trendklasse) van de wijziging; - - `verloop`: geeft aan of de trend lineair verloopt; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **indices:** [`index.csv`](../data/index.csv) en [`index.yml`](../data/index.yml). - - `soort`: naam van de indicator; - - `frequentie`: index op basis van jaarlijkse of driejaarlijkse gegevens; - - `referentie`: het referentiejaartal; - - `naar`: het jaartal waarvoor we verhouding het referentiejaar berekenen; - - `schatting`: de geschatte verhouding van dit jaar t.o.v. het referentiejaar; - - `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; - - `klasse`: [interpretatie](voorstelling.html#sec-trendklasse) van de wijziging; - - `wijziging`: tekstuele beschrijving van de evolutie; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **stratumgewichten:** [`stratum_gewicht.csv`](../data/stratum_gewicht.csv) en [`stratum_gewicht.yml`](../data/stratum_gewicht.yml). - - `soort`: Nederlandse soortnaam; - - `stratum`: naam van het stratum; - - `gewicht`: [gewicht van het stratum](verwerking.html#sec-stratumgewicht) voor deze soort; - - `aanwezig`: geschatte aantal hokken van het stratum waar de soort aanwezig is; - - `relevant`: aantal relevante hokken voor deze soort in het stratum; - - `onderzocht`: aantal onderzochte hokken in het stratum; - - `totaal`: totaal aantal hokken in het stratum; - - `bezoeken`: totaal aantal bezoeken aan de meetpunten in het stratum; +#### Gemiddeld aantal op een meetpunt + +- `soort`: Nederlandse soortnaam; +- `frequentie`: trend op basis van jaarlijkse of driejaarlijkse gegevens; +- `jaar`: jaartal of startjaar van de driejaarlijkse periode; +- `schatting`: schatting van het gemiddeld aantal dieren op een meetpunt; +- `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; +- `analyse`: data-hash van de analyse; +- `status`: data-hash van de status van de analyse + +#### Lineaire trends + +- `soort`: Nederlandse soortnaam; +- `frequentie`: trend op basis van jaarlijkse of driejaarlijkse gegevens; +- `jaarlijks`: gemiddelde `r lineair`; +- `looptijd`: gemiddelde wijziging over de looptijd van de tijdsreeks; +- `klasse`: `r interpretatie` van de wijziging; +- `verloop`: geeft aan of de trend lineair verloopt; +- `analyse`: data-hash van de analyse; +- `status`: data-hash van de status van de analyse + +#### Indices + +- `soort`: naam van de indicator; +- `frequentie`: index op basis van jaarlijkse of driejaarlijkse gegevens; +- `referentie`: het referentiejaartal; +- `naar`: het jaartal waarvoor we verhouding het referentiejaar berekenen; +- `schatting`: de geschatte verhouding van dit jaar t.o.v. het referentiejaar; +- `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; +- `klasse`: `r interpretatie` van de wijziging; +- `wijziging`: tekstuele beschrijving van de evolutie; +- `analyse`: data-hash van de analyse; +- `status`: data-hash van de status van de analyse + +#### Stratumgewichten + +- `soort`: Nederlandse soortnaam; +- `stratum`: naam van het stratum; +- `gewicht`: `r stratum` voor deze soort; +- `aanwezig`: geschatte aantal hokken van het stratum waar de soort aanwezig is; +- `relevant`: aantal relevante hokken voor deze soort in het stratum; +- `onderzocht`: aantal onderzochte hokken in het stratum; +- `totaal`: totaal aantal hokken in het stratum; +- `bezoeken`: totaal aantal bezoeken aan de meetpunten in het stratum; + +### Download bestanden + +::: {.content-visible when-format="html"} + +< + +| informatie | gegevens | metadata | +| ---------- | -------- | -------- | +| samengestelde indices | [`samengesteld.csv`](../data/samengesteld.csv) | [`samengesteld.yml`](../data/samengesteld.yml) | +| soortenlijst samengestelde indices | [`samengesteld_soorten.csv`](../data/samengesteld_soorten.csv) | [`samengesteld_soorten.yml`](../data/samengesteld_soorten.yml) | + +: Downloadbare bestanden met resultaten per samengestelde index {#tbl-download-composite} + +| informatie | gegevens | metadata | +| ---------- | -------- | -------- | +| gemiddeld aantal op een meetpunt| [`aantallen.csv`](../data/aantallen.csv) | [`aantallen.yml`](../data/aantallen.yml) | +| lineaire trends| [`lineaire_trend.csv`](../data/lineaire_trend.csv) | [`lineaire_trend.yml`](../data/lineaire_trend.yml) | +| indices | [`index.csv`](../data/index.csv) | [`index.yml`](../data/index.yml) | +| stratumgewichten| [`stratum_gewicht.csv`](../data/stratum_gewicht.csv) | [`stratum_gewicht.yml`](../data/stratum_gewicht.yml) | + +: Downloadbare bestanden met resultaten per soort {#tbl-download-species} + +< + +::: + +::: {.content-visible when-format="pdf"} + +```{r} +readLines("../_quarto.yml") |> + grepv(pattern = "doi:") |> + gsub(pattern = ".*doi:\\s*", replacement = "https://doi.org/") -> doi +``` + +De downloadbare bestanden zijn beschikbaar in de online versie. +Deze kan u bereiken via [`r doi`](`r doi`). +::: diff --git a/inst/interactive/methodologie/verwerking.qmd b/inst/interactive/methodologie/verwerking.qmd index 141dfaf..7fc9a37 100644 --- a/inst/interactive/methodologie/verwerking.qmd +++ b/inst/interactive/methodologie/verwerking.qmd @@ -7,11 +7,10 @@ library(git2rdata) library(INBOtheme) library(tidyverse) conflicted::conflicts_prefer(dplyr::filter) -if (interactive()) { - root <- file.path("..", "..", "..", "..", "abv", "results") -} else { - root <- file.path("..", "..", "results") -} +update_geom_defaults("point", list(size = 2)) +update_geom_defaults("text", list(size = 2)) +theme_set(theme_inbo(base_size = 8)) +root <- system.file("results", package = "abvanalysis") ``` @@ -54,23 +53,35 @@ effort |> arrange(.data$year) |> group_by(.data$location) |> transmute( - .data$year, end = max(.data$year), year2 = lead(.data$year), - year3 = lead(.data$year2), year4 = lead(.data$year3), - year5 = lead(.data$year4), year6 = lead(.data$year5), + .data$year, + end = max(.data$year), + year2 = lead(.data$year), + year3 = lead(.data$year2), + year4 = lead(.data$year3), + year5 = lead(.data$year4), + year6 = lead(.data$year5), year7 = lead(.data$year6) ) |> slice(1) |> ungroup() |> arrange( - .data$year, .data$end, .data$year2, .data$year3, .data$year4, .data$year5, - .data$year6, .data$year7 + .data$year, + .data$end, + .data$year2, + .data$year3, + .data$year4, + .data$year5, + .data$year6, + .data$year7 ) |> rowid_to_column("hok") |> transmute( - .data$location, hok = factor(.data$hok), + .data$location, + hok = factor(.data$hok), bruikbaar = ((.data$year - 2007) %/% 3) < ((end - 2007) %/% 3), bruikbaar = factor( - .data$bruikbaar, levels = c(TRUE, FALSE), + .data$bruikbaar, + levels = c(TRUE, FALSE), labels = c("mogelijk", "voorlopig\nniet") ) ) |> @@ -100,7 +111,7 @@ Dit wil niet zeggen dat we deze soort permanent afschrijven! Wanneer we deze regels in de toekomst opnieuw toepassen hebben we mogelijk wel voldoende waarnemingen. Ook hokken of strata in de toekomst gekoloniseerd worden, worden dan opgepikt. -## Trendberekening {#s:trendberekening} +## Trendberekening {#sec-trendberekening} ### Niet-lineaire trends diff --git a/inst/interactive/methodologie/voorstelling.qmd b/inst/interactive/methodologie/voorstelling.qmd index 19fab61..9670aa4 100644 --- a/inst/interactive/methodologie/voorstelling.qmd +++ b/inst/interactive/methodologie/voorstelling.qmd @@ -6,9 +6,7 @@ title: Voorstelling van de gegevens library(effectclass) library(gt) library(INBOtheme) -library(plotly) library(tidyverse) -conflicted::conflicts_prefer(plotly::layout) ``` @@ -63,17 +61,17 @@ De afkortingen zelf zijn te fijn om als symbool te gebruiken. #| 90% interval, $b$: bovengrens van het 90% interval. $L < R < B$ en $l < b$. #| " tribble( - ~ benaming, ~afkorting, ~regels, - "sterke toename", "`++`", "$B < l$", - "toename", "`+`", "$R < l < B$ en $B < b$", - "matige toename", "`+~`", "$R < l < B$ en $b < B$", - "stabiel", "`~`", "$L < l < R$ en $R < b < B$", - "matige afname", "`-~`", "$L < l < R$ en $b < R$", - "afname", "`-`", "$l < L$ en $L < b < R$", - "sterke afname", "`--`", "$l < L$", + ~benaming, ~afkorting, ~regels, + "sterke toename", "`++`", "$B < l$", + "toename", "`+`", "$R < l < B$ en $B < b$", + "matige toename", "`+~`", "$R < l < B$ en $b < B$", + "stabiel", "`~`", "$L < l < R$ en $R < b < B$", + "matige afname", "`-~`", "$L < l < R$ en $b < R$", + "afname", "`-`", "$l < L$ en $L < b < R$", + "sterke afname", "`--`", "$l < L$", "mogelijke toename", "`?+`", "$L < l < R$ en $B < b$", - "mogelijke afname", "`?-`", "$l < L$ en $R < b < B$", - "onduidelijk", "`?`", "$l < L$ en $B < b$" + "mogelijke afname", "`?-`", "$l < L$ en $R < b < B$", + "onduidelijk", "`?`", "$l < L$ en $B < b$" ) |> gt() |> fmt_markdown() @@ -84,18 +82,20 @@ tribble( #| het 90% interval te vergelijken met een referentie, ondergrens en #| bovengrens. tribble( - ~x, ~y, "stabiel", 0, "toename", 1, "afname", -1, "sterke toename", 1.5, + ~x, ~y, "stabiel", 0, "toename", 1, "afname", -1, "sterke toename", 1.5, "sterke afname", -1.5, "matige toename", 0.5, "matige afname", -0.5, - "mogelijke toename", 0.5, "mogelijke afname", -0.5, "onduidelijk", 0 + "mogelijke toename", 0.5, "mogelijke afname", -0.5, "onduidelijk", 0 ) |> mutate( - s = c(rep(0.25, 7), 0.5, 0.5, 1.1), lcl = qnorm(0.05, .data$y, .data$s), + s = c(rep(0.25, 7), 0.5, 0.5, 1.1), + lcl = qnorm(0.05, .data$y, .data$s), ucl = qnorm(0.95, .data$y, .data$s), klasse = classification(.data$lcl, .data$ucl, 1) ) |> arrange(.data$klasse) |> mutate( - x = factor(.data$x, levels = rev(.data$x)), x_int = as.integer(.data$x), + x = factor(.data$x, levels = rev(.data$x)), + x_int = as.integer(.data$x), tekst = format_ci(.data$y, lcl = .data$lcl, ucl = .data$ucl) |> sprintf(fmt = "%s\n%s (%s)", .data$x, .data$klasse) ) -> effect @@ -106,31 +106,97 @@ effect |> effect |> mutate(x_int = .data$x_int + 0.499) ) |> - arrange(.data$x_int) |> - plot_ly(x = ~x_int, y = ~y) |> - add_fan(sd = ~s, text = ~tekst) |> - config( - displaylogo = FALSE, - modeBarButtonsToRemove = c( - "autoScale", "hoverCompareCartesian", "lasso2d", "select2d" + arrange(.data$x_int) -> plot_effect +if (knitr::is_html_output()) { + library(plotly) + conflicted::conflicts_prefer(plotly::layout) + plot_ly(plot_effect, x = ~x_int, y = ~y) |> + add_fan(sd = ~s, text = ~tekst) |> + config( + displaylogo = FALSE, + modeBarButtonsToRemove = c( + "autoScale", + "hoverCompareCartesian", + "lasso2d", + "select2d" + ) + ) |> + layout( + xaxis = list( + title = list(font = list(size = 0)), + tickvals = effect$x_int, + ticktext = effect$x + ), + yaxis = list( + title = list(font = list(size = 0)), + zerolinewidth = 1, + tickvals = c(-1, 0, 1), + ticktext = c("ondergrens", "referentie", "bovengrens") + ), + shapes = list( + type = "rect", + x0 = 0, + x1 = 1, + xref = "paper", + y0 = -1, + y1 = 1, + line = list(width = 0), + opacity = 0.1, + fillcolor = "black", + layer = "below" + ) ) - ) |> - layout( - xaxis = list( - title = list(font = list(size = 0)), tickvals = effect$x_int, - ticktext = effect$x - ), - yaxis = list( - title = list(font = list(size = 0)), zerolinewidth = 1, - tickvals = c(-1, 0, 1), - ticktext = c("ondergrens", "referentie", "bovengrens") - ), - shapes = list( - type = "rect", x0 = 0, x1 = 1, xref = "paper", y0 = -1, y1 = 1, - line = list(width = 0), opacity = 0.1, fillcolor = "black", - layer = "below" +} else { + theme_set(theme_inbo(base_size = 8)) + ggplot(plot_effect, aes(x = x, y = y)) + + annotate( + geom = "rect", + xmin = -Inf, + xmax = Inf, + ymin = -1, + ymax = 1, + alpha = 0.2, + fill = inbo_grijs + ) + + geom_hline(yintercept = 0) + + stat_fan(aes(link_sd = s), geom = "rect") + + annotate( + geom = "text", + x = Inf, + y = 0, + hjust = 1, + vjust = -0.1, + label = "referentie", + colour = inbo_hoofd, + angle = 90 + ) + + annotate( + geom = "text", + x = Inf, + y = -1, + hjust = 1, + vjust = 1, + label = "belangrijke afname", + colour = inbo_hoofd, + angle = 90 + ) + + annotate( + geom = "text", + x = Inf, + y = 1, + hjust = 1, + vjust = 0, + label = "belangrijke toename", + colour = inbo_hoofd, + angle = 90 + ) + + coord_flip() + + theme( + axis.title = element_blank(), + axis.ticks = element_blank(), + axis.text.x = element_blank() ) - ) +} ``` Uiteraard hangt de opdeling sterk af van de keuze van de boven- en ondergrens. @@ -171,21 +237,17 @@ Rond de lijn tonen we het 30%, 60% en 90% interval (zie §@sec-onzekerheid). In de online versie van het rapport is de figuur interactief. Wanneer de gebruiker met de muis over de figuur gaat verschijnt er in de buurt van de lijn een pop-up met de exacte schatting van dat jaar inclusief het 90% betrouwbaarheidsinterval en het jaartal. -## Paarsgewijze vergelijking van jaren +## Paarsgewijze vergelijking van jaren {#sec-paarsgewijs} + +::: {.content-visible when-format="pdf"} Deze informatie hebben we telkens in twee figuren samengevat. De eerste figuur geeft het verschil van elk jaar t.o.v. 2007, het jaar waarin de metingen gestart zijn. Voor 2007 geven we geen cijfer omdat het per definitie 1 is en geen informatie bevat. -Het symbool geeft de puntschatting van het relatieve verschil t.o.v. 2007 weer. -De vorm van het symbool geeft de opdeling van de sterkte van het effect weer (zie @fig-trendklasse). -Rond elke puntschatting tonen we het 30%, 60% en 90% interval (zie §@sec-onzekerheid). +Rond elke puntschatting tonen we het 90% interval in stappen van 5% (zie §@sec-onzekerheid). De horizontale streepjeslijn geeft de referentie van 0% verschil weer. De horizontale puntlijnen geven de bovengrens (+33%) en ondergrens (-25%) weer. Deze laten toe om de vlot zelf de betrouwbaarheidsintervallen te vergelijken met de referentie, boven- en ondergrens. -De figuur heeft twee y-assen. -Beide assen geven dezelfde informatie weer, enkel de formattering van de labels is anders. -De linkeras toont procentuele verschillen terwijl de rechteras de relatieve verschillen toont. -Een procentueel verschil van +50% is equivalent met een relatieve verschil (verhouding) met een factor 1,5. We stellen vast dat veel gebruikers ook andere jaren met elkaar willen vergelijken. Een correcte vergelijking is enkel mogelijk indien we een van deze jaren als referentie gebruiken. @@ -200,5 +262,20 @@ De vorm van de symbolen geeft zicht op de sterkte en onzekerheid van het effect Wanneer een rij volledig rood (blauw) is, zijn alle verschillen met dit referentiejaar positief (negatief) m.a.w. dit is het referentiejaar met de laagste (hoogste) aantallen. Wanneer een kolom volledig rood (blauw) is, zijn alle verschillen van dit jaar t.o.v. alle referentiejaren negatief (positief) m.a.w. dit is het jaar met de hoogste (laagste) aantallen. Clusters van punten met een gelijkaardige kleur geven periodes aan waarin de aantallen geleidelijk wijzigen. -In de online versie van dit rapport is deze figuur interactief. +::: + +::: {.content-visible when-format="html"} +De figuur geeft het verschil van elk jaar t.o.v. een referentiejaar. +Standaard gebruiken we hiervoor 2007, het jaar waarin de metingen gestart zijn. +Voor het referentiejaar geven we geen cijfer omdat het per definitie 1 is en geen informatie bevat. +Rond elke puntschatting tonen we het 90% interval in stappen van 5% (zie §@sec-onzekerheid). +De horizontale streepjeslijn geeft de referentie van 0% verschil weer. +De horizontale puntlijnen geven de bovengrens (+33%) en ondergrens (-25%) weer. +Deze laten toe om de vlot zelf de betrouwbaarheidsintervallen te vergelijken met de referentie, boven- en ondergrens. + +We stellen vast dat veel gebruikers ook andere jaren met elkaar willen vergelijken. +Een correcte vergelijking is enkel mogelijk indien we een van deze jaren als referentie gebruiken. +Hiertoe selecteer je het gewenste referentiejaar in het keuzemenu naast de figuur. + Wanneer de gebruiker met de muis over de figuur gaat verschijnt er in de buurt van de lijn een pop-up met de exacte schatting van dat punt inclusief het 90% betrouwbaarheidsinterval en het jaartal en referentiejaar. +::: diff --git a/inst/website/natuurpunt.png b/inst/interactive/natuurpunt.png similarity index 100% rename from inst/website/natuurpunt.png rename to inst/interactive/natuurpunt.png diff --git a/inst/interactive/references.bib b/inst/interactive/references.bib index 4ff2572..e74a8d2 100755 --- a/inst/interactive/references.bib +++ b/inst/interactive/references.bib @@ -1,36 +1,3 @@ -@Manual{R, - title = {R: A Language and Environment for Statistical Computing}, - author = {{R Core Team}}, - organization = {R Foundation for Statistical Computing}, - address = {Vienna, Austria}, - year = {2023}, - url = {https://www.R-project.org/}, -} -@Manual{git2rdata, - title = {git2rdata: Store and Retrieve Data.frames in a Git Repository. Version 0.4.0}, - author = {Thierry Onkelinx}, - year = {2022}, - url = {https://ropensci.github.io/git2rdata/}, - abstract = {The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette("plain_text", package = "git2rdata"). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette("version_control", package = "git2rdata"). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette("workflow", package = "git2rdata") gives a toy example. 4) vignette("efficiency", package = "git2rdata") provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.}, - keywords = {R package, reproducible research, version control}, -} -@Manual{abvanalysis, - title = {abvanalysis: Analysis of the Common Breeding Bird Survey. Version 0.2.4}, - author = {Thierry Onkelinx}, - year = {2024}, - url = {https://github.com/inbo/abvanalysis/}, - abstract = {Read in the raw data and prepare analysis datasets.}, - keywords = {common breeding birds; monitoring; analysis; reproducible science}, -} -@Manual{n2kanalysis, - title = {n2kanalysis: Generic Functions to Analyse Data from the 'Natura 2000' Monitoring. Version 0.3.2}, - author = {Thierry Onkelinx}, - year = {2024}, - url = {https://github.com/inbo/n2kanalysis/}, - abstract = {All generic functions and classes for the analysis for the 'Natura 2000' monitoring. The classes contain all required data and definitions to fit the model without the need to access other sources. Potentially they might need access to one or more parent objects. An aggregation object might for example need the result of an imputation object. The actual definition of the analysis, using these generic function and classes, is defined in dedictated analysis R packages for every monitoring scheme. For example 'abvanalysis' and 'watervogelanalysis'.}, - keywords = {analysis, reproducible research, natura 2000, monitoring}, - doi = {10.5281/zenodo.3576047}, -} @TechReport{ABVmethode, address = {{Brussels, Belgium}}, title = {Methodehandleiding Bij Het Project '{{Algemene Broedvogelmonitoring Vlaanderen}} ({{ABV}})'}, diff --git a/inst/interactive/render.R b/inst/interactive/render.R index d0db404..ee62fd7 100644 --- a/inst/interactive/render.R +++ b/inst/interactive/render.R @@ -1,56 +1,84 @@ library(abvanalysis) library(git2rdata) +library(keyring) library(knitr) library(quarto) library(tidyverse) conflicted::conflicts_prefer(dplyr::filter, dplyr::pull) n_head <- Inf -results_folder <- "../../../abv/results" -target_folder <- "../../../abv/quarto" -system.file("extensions", package = "abvanalysis") |> - dirname() -> source_folder -file.path( - target_folder, "_extensions", "inboqmd-report-website", "_extensions", - "flandersqmd-report-website" -) |> - dir.create(showWarnings = FALSE, recursive = TRUE) +results_folder <- system.file("results", package = "abvanalysis") +source_folder <- system.file("interactive", package = "abvanalysis") +target_folder <- key_get("quarto_folder", keyring = "abv") +target_pdf <- paste0(target_folder, "_pdf") -# styling -file.path(source_folder, "extensions", "inboqmd-report-website") |> - list.files(recursive = TRUE) -> to_do -file.path(source_folder, "extensions", "inboqmd-report-website", to_do) |> - file.copy( - to = file.path( - target_folder, "_extensions", "inboqmd-report-website", to_do - ), - overwrite = TRUE - ) -file.path(source_folder, "extensions", "flandersqmd-report-website") |> - list.files(recursive = TRUE) -> to_do -file.path(source_folder, "extensions", "flandersqmd-report-website", to_do) |> - file.copy( - to = file.path( - target_folder, "_extensions", "inboqmd-report-website", "_extensions", - "flandersqmd-report-website", to_do - ), - overwrite = TRUE - ) +dir.create(target_folder, showWarnings = FALSE, recursive = TRUE) +dir.create(target_pdf, showWarnings = FALSE, recursive = TRUE) + +# logo +system.file("interactive/natuurpunt.png", package = "abvanalysis") |> + file.copy(to = target_folder, overwrite = TRUE) +system.file("interactive/natuurpunt.png", package = "abvanalysis") |> + file.copy(to = target_pdf, overwrite = TRUE) # methodology +## html file.path(target_folder, "methodologie") |> dir.create(showWarnings = FALSE, recursive = TRUE) -list.files(pattern = "\\.(bib|md)", full.names = TRUE) |> +list.files(source_folder, pattern = "\\.md", full.names = TRUE) |> file.copy(to = target_folder, overwrite = TRUE) -list.files("methodologie", pattern = "\\.qmd", full.names = TRUE) |> + +cite_package <- function(x) { + citation(ifelse(x == "R", "base", x)) |> + toBibtex() |> + str_replace("Manual\\{,", paste0("Manual\\{", x, ",")) +} +c("R", "git2rdata", "abvanalysis", "n2kanalysis") |> + map(cite_package) |> + c( + file.path(source_folder, "references.bib") |> + readLines() + ) |> + do.call(what = c) -> refs +file.path(target_folder, "references.bib") |> + writeLines(text = refs) +file.path(source_folder, "methodologie") |> + list.files(pattern = "\\.qmd", full.names = TRUE) |> file.copy(to = file.path(target_folder, "methodologie"), overwrite = TRUE) +## pdf +file.path(target_pdf, "methodologie") |> + dir.create(showWarnings = FALSE, recursive = TRUE) +file.path(target_pdf, "references.bib") |> + writeLines(text = refs) +file.path(source_folder, "abstract.md") |> + readLines() -> abstract +c(head(abstract, 2), "# Abstract {-}", tail(abstract, -5)) |> + writeLines(file.path(target_pdf, "abstract.md")) +c("# Bibliografie {-}", "", "::: {#refs}", ":::") |> + writeLines(file.path(target_pdf, "references.md")) + +c( + "{{< colophon >}}", + "# Samenvatting {-}", + "", + file.path(source_folder, "samenvatting.md") |> + readLines() |> + tail(-5) +) |> + writeLines(file.path(target_pdf, "index.md")) +file.path(source_folder, "methodologie") |> + list.files(pattern = "\\.qmd", full.names = TRUE) |> + file.copy(to = file.path(target_pdf, "methodologie"), overwrite = TRUE) # species file.path(target_folder, "soort") |> dir.create(showWarnings = FALSE, recursive = TRUE) -list.files(pattern = "trends.qmd", full.names = TRUE) |> +list.files(source_folder, pattern = "trends.qmd", full.names = TRUE) |> file.copy( - to = file.path(target_folder, "soort", "index.qmd"), overwrite = TRUE + to = file.path(target_folder, "soort", "trends.qmd"), + overwrite = TRUE ) +file.path(source_folder, "trends_pdf.qmd") |> + file.copy(to = file.path(target_pdf, "soort", "trends.qmd"), overwrite = TRUE) read_vc("meta", results_folder) |> filter(.data$status == "converged", !.data$composite) |> distinct(.data$speciesgroup) |> @@ -66,10 +94,15 @@ for (i in seq_len(nrow(species))) { file.path(target_folder, species$output_file[i]) |> dirname() |> dir.create(showWarnings = FALSE) - knit_expand( - "species.qmd", species = species$species[i], label = species$label[i] - ) |> + file.path(source_folder, "species.qmd") |> + knit_expand(species = species$species[i], label = species$label[i]) |> writeLines(file.path(target_folder, species$output_file[i])) + file.path(target_pdf, species$output_file[i]) |> + dirname() |> + dir.create(showWarnings = FALSE) + file.path(source_folder, "species_pdf.qmd") |> + knit_expand(species = species$species[i], label = species$label[i]) |> + writeLines(file.path(target_pdf, species$output_file[i])) } # composite index @@ -88,39 +121,96 @@ for (i in seq_len(nrow(composite))) { file.path(target_folder, composite$output_file[i]) |> dirname() |> dir.create(showWarnings = FALSE) - knit_expand( - "composite.qmd", species = composite$species[i], label = composite$label[i] - ) |> + file.path(source_folder, "composite.qmd") |> + knit_expand(species = composite$species[i], label = composite$label[i]) |> writeLines(file.path(target_folder, composite$output_file[i])) + file.path(target_pdf, composite$output_file[i]) |> + dirname() |> + dir.create(showWarnings = FALSE) + file.path(source_folder, "composite_pdf.qmd") |> + knit_expand(species = composite$species[i], label = composite$label[i]) |> + writeLines(file.path(target_pdf, composite$output_file[i])) } # toc -readLines("_quarto.yml") |> +file.path(source_folder, "_quarto.yml") |> + readLines() -> base_yaml +c( + base_yaml, + " - section: \"Indicatoren\"", + " contents:", + sprintf( + " - text: \"%s\"\n file: %s", + composite$speciesgroup, + composite$output_file + ), + " - section: \"Soorten\"", + " contents:", + " - text: \"Overzicht\"\n file: soort/trends.qmd", + sprintf( + " - text: \"%s\"\n file: %s", + species$speciesgroup, + species$output_file + ) +) |> + writeLines(file.path(target_folder, "_quarto.yml")) +tail(base_yaml, -4) |> + head(grep("website:", base_yaml) - 5) |> + gsub(pattern = "website-html", replacement = "book-pdf") |> + grepv(pattern = "cover: cover.png", invert = TRUE) |> c( - " - section: \"Indicatoren\"", " contents:", - sprintf( - " - text: \"%s\"\n file: %s", composite$speciesgroup, - composite$output_file - ), - " - section: \"Soorten\"", " file: soort/index.qmd", - " contents:", - " - text: \"Overzicht\"\n file: soort/index.qmd", - sprintf( - " - text: \"%s\"\n file: %s", species$speciesgroup, - species$output_file - ) + "book:", + " chapters:", + " - index.md", + " - abstract.md", + " - part: Methodologie", + " chapters:", + " - methodologie/inzameling.qmd", + " - methodologie/verwerking.qmd", + " - methodologie/voorstelling.qmd", + " - methodologie/reproduceerbaarheid.qmd", + " - part: Indicatoren", + " chapters:", + sprintf(" - %s", composite$output_file), + " - part: Soorten", + " chapters:", + " - soort/trends.qmd", + sprintf(" - %s", species$output_file), + " - references.md", + "", + "project:", + " output-dir: ../website", + " type: book", + " post-render: _extensions/inbo/flandersqmd-book/filters/post_render.R" ) |> - writeLines(file.path(target_folder, "_quarto.yml")) + writeLines(file.path(target_pdf, "_quarto.yml")) + +old_wd <- getwd() +on.exit(setwd(old_wd), add = TRUE) +setwd(target_pdf) +quarto_add_extension("inbo/flandersqmd-book", no_prompt = TRUE) +quarto::quarto_render(output_format = "flandersqmd-book-pdf") +setwd(target_folder) if ( !file_test("-f", file.path(target_folder, "cover.png")) && - file_test("-f", "../website/cover.pdf") + file_test( + "-f", + system.file("interactive/cover.pdf", package = "abvanalysis") + ) ) { requireNamespace("pdftools") + system.file("interactive/cover.pdf", package = "abvanalysis") |> + file.copy("cover.pdf") pdftools::pdf_convert( - "../website/cover.pdf", format = "png", pages = 1, dpi = 770 * 25.4 / 210, - filenames = file.path(target_folder, "cover.png") + "cover.pdf", + format = "png", + pages = 1, + dpi = 770 * 25.4 / 210, + filenames = "cover.png" ) + file.remove("cover.pdf") } -quarto_render(target_folder, use_freezer = TRUE, cache = TRUE, as_job = FALSE) +quarto_add_extension("inbo/flandersqmd-website", no_prompt = TRUE) +quarto::quarto_render(output_format = "flandersqmd-website-html") diff --git a/inst/interactive/render_vbp.R b/inst/interactive/render_vbp.R new file mode 100644 index 0000000..63937c0 --- /dev/null +++ b/inst/interactive/render_vbp.R @@ -0,0 +1,54 @@ +library(abvanalysis) +library(git2rdata) +library(keyring) +library(knitr) +library(quarto) +library(tidyverse) +conflicted::conflicts_prefer(dplyr::filter, dplyr::pull) +n_head <- Inf +results_folder <- system.file("results", package = "abvanalysis") +source_folder <- system.file("interactive", package = "abvanalysis") +target_folder <- key_get("vbp_folder", keyring = "abv") +raw_folder <- key_get("raw_folder", keyring = "abv") +dir.create(target_folder, showWarnings = FALSE, recursive = TRUE) + +# species +read_vc("meta", results_folder) |> + filter(.data$status == "converged", !.data$composite) |> + distinct(.data$speciesgroup) |> + arrange(.data$speciesgroup) |> + head(n_head) |> + mutate( + label = str_replace_all(.data$speciesgroup, " ", "-") |> + str_remove_all("'") |> + tolower(), + output_file = sprintf(fmt = "%s.qmd", .data$label) + ) -> species +for (i in seq_len(nrow(species))) { + file.path(target_folder, species$output_file[i]) |> + dirname() |> + dir.create(showWarnings = FALSE) + file.path(source_folder, "vbp_species.qmd") |> + knit_expand( + species = species$speciesgroup[i], + doi = "10.21436/inbor.141312967", + url = "https://www.vlaanderen.be/inbo/rapporten/abv-trends-2007-2025", + label = species$label[i] + ) |> + writeLines(file.path(target_folder, species$output_file[i])) +} + +file.path(target_folder, species$output_file) |> + quarto_render(use_freezer = TRUE, cache = TRUE, as_job = FALSE) + +read_vc("species/species", root = raw_folder) |> + select("id", "nl", "scientific_name") |> + inner_join(species, by = c("nl" = "speciesgroup")) |> + select(-"label") |> + write_csv(file.path(target_folder, "species.csv")) + +old_wd <- getwd() +on.exit(setwd(old_wd), add = TRUE) +setwd(target_folder) +list.files(pattern = "\\.(csv|html)") |> + zip(zipfile = "vbp.zip", flags = "-r9XqT") diff --git a/inst/interactive/samenvatting.md b/inst/interactive/samenvatting.md index b38a7ac..ad2dcea 100644 --- a/inst/interactive/samenvatting.md +++ b/inst/interactive/samenvatting.md @@ -1,5 +1,9 @@ -# Samenvatting +--- +title: Samenvatting +toc: false +--- + Dit rapport publiceert de meest recente analyses op basis van het telwerk voor het project "Algemene Broedvogelmonitoring Vlaanderen" of kortweg ABV. Dit project is een gezamenlijk initiatief van het Instituut voor Natuur- en Bosonderzoek (INBO) en Natuurpunt Studie in samenwerking met de lokale vogelwerkgroepen. De samenwerking tussen deze organisaties staat garant voor een goede ondersteuning van en communicatie naar het vrijwilligersnetwerk door Natuurpunt Studie, en een degelijke professionele wetenschappelijke ondersteuning en dataverwerking door het INBO. @@ -15,3 +19,4 @@ het tweede deel geeft een aantal indicatoren gebaseerd op trends van meerdere so het derde deel beschrijft de tijdreeks van individuele soorten. We beperken ons in delen twee en drie tot een gestandaardiseerde weergave van de meest relevante cijfers zonder ecologische interpretatie. Voor een ecologische interpretatie van de resultaten verwijzen we naar andere publicaties zoals [INBO Vogelnieuws](https://www.vlaanderen.be/inbo/publicaties/vogelnieuws). + diff --git a/inst/interactive/species.qmd b/inst/interactive/species.qmd index 69609bc..986a08d 100644 --- a/inst/interactive/species.qmd +++ b/inst/interactive/species.qmd @@ -2,7 +2,7 @@ title: "{{species}}" --- -```{r setup-{{label}}} +```{r setup} library(crosstalk) library(effectclass) library(git2rdata) @@ -10,15 +10,18 @@ library(INBOtheme) library(gt) library(plotly) library(tidyverse) -conflicted::conflicts_prefer(dplyr::filter, plotly::layout) +conflicted::conflicts_prefer( + dplyr::filter, + plotly::layout, + effectclass::class_labels +) +root <- system.file("results", package = "abvanalysis") if (interactive()) { - root <- file.path("..", "..", "..", "abv", "results") read_vc("meta", root) |> filter(!.data$composite, .data$status == "converged") |> slice_sample(n = 1) -> this this_species <- this$speciesgroup } else { - root <- file.path("..", "..", "results") this_species <- "{{species}}" } threshold <- log(0.75) @@ -33,7 +36,7 @@ trend_tekst <- function(x) { } ``` -```{r load-data-{{label}}} +```{r load-data} read_vc("meta", root) |> filter(.data$speciesgroup == this_species) |> select("analysis", "cycle", "linear", "status_fingerprint", "waic") -> meta @@ -41,34 +44,38 @@ paste0( "Evolutie van het gemodelleerde gemiddeld aantal waargenomen dieren op een ", "meetpunt per jaar voor %s tijdens de referentieperiode. ", "De band geeft het [90%% geloofwaardigheidsinterval]", - "(../methodologie/voorstelling.html#sec-onzekerheid)." + "(../methodologie/voorstelling.html#sec-onzekerheid) in stappen van 5%%. ", + "Beweeg met de muis over de grafiek om de cijfers zichtbaar te maken." ) |> sprintf(as.character(this_species)) -> caption_year_estimate paste0( "Evolutie van het gemodelleerde gemiddeld aantal waargenomen dieren op een ", "meetpunt per driejarige cyclus voor %s tijdens de referentieperiode. ", "De band geeft het [90%% geloofwaardigheidsinterval]", - "(../methodologie/voorstelling.html#sec-onzekerheid)." + "(../methodologie/voorstelling.html#sec-onzekerheid) in stappen van 5%%. ", + "Beweeg met de muis over de grafiek om de cijfers zichtbaar te maken." ) |> sprintf(as.character(this_species)) -> caption_cycle_estimate paste0( "Wijzigingen tussen jaren voor %s. ", "De band geeft het [90%% geloofwaardigheidsinterval]", - "(../methodologie/voorstelling.html#sec-onzekerheid). ", + "(../methodologie/voorstelling.html#sec-onzekerheid) in stappen van 30%%. ", "De [referentielijnen]", "(../methodologie/voorstelling.html#sec-trendklasse) ", "zorgen voor de opdeling van de wijzigingen in [klassen]", - "(../methodologie/voorstelling.html#sec-trendklasse)." + "(../methodologie/voorstelling.html#sec-trendklasse). ", + "Beweeg met de muis over de grafiek om de indeling zichtbaar te maken." ) |> sprintf(as.character(this_species)) -> caption_year_index paste0( "Wijzigingen tussen driejarige cycli voor %s. ", "De band geeft het [90%% geloofwaardigheidsinterval]", - "(../methodologie/voorstelling.html#sec-onzekerheid). ", + "(../methodologie/voorstelling.html#sec-onzekerheid) in stappen van 30%%. ", "De [referentielijnen]", "(../methodologie/voorstelling.html#sec-trendklasse) ", "zorgen voor de opdeling van de wijzigingen in [klassen]", - "(../methodologie/voorstelling.html#sec-trendklasse)." + "(../methodologie/voorstelling.html#sec-trendklasse). ", + "Beweeg met de muis over de grafiek om de indeling zichtbaar te maken." ) |> sprintf(as.character(this_species)) -> caption_cycle_index paste0( @@ -103,62 +110,133 @@ meta |> transmute( tekst = classification( .data$lower_confidence_limit * looptijd, - .data$upper_confidence_limit * looptijd, threshold = threshold + .data$upper_confidence_limit * looptijd, + threshold = threshold ), tekst = class_labels(lang = "nl")[.data$tekst], jaarlijks = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, sign = TRUE, - ucl = .data$upper_confidence_limit, percent = TRUE, link = "log", + .data$estimate, + lcl = .data$lower_confidence_limit, + sign = TRUE, + ucl = .data$upper_confidence_limit, + percent = TRUE, + link = "log", change = TRUE ), looptijd = format_ci( - looptijd * .data$estimate, lcl = looptijd * .data$lower_confidence_limit, - ucl = looptijd * .data$upper_confidence_limit, percent = TRUE, - sign = TRUE, link = "log", change = TRUE + looptijd * .data$estimate, + lcl = looptijd * .data$lower_confidence_limit, + ucl = looptijd * .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + link = "log", + change = TRUE ) ) -> trend_year -meta |> - filter(.data$cycle) |> - semi_join(x = read_vc("linear_estimate", root), by = "analysis") |> - tail(-1) |> - nrow() -> looptijd meta |> filter(.data$cycle) |> semi_join(x = read_vc("linear_trend", root), by = "analysis") |> transmute( tekst = classification( - .data$lower_confidence_limit * looptijd, - .data$upper_confidence_limit * looptijd, threshold = threshold + .data$lower_confidence_limit * looptijd / 3, + .data$upper_confidence_limit * looptijd / 3, + threshold = threshold ), tekst = class_labels(lang = "nl")[.data$tekst], jaarlijks = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, sign = TRUE, - ucl = .data$upper_confidence_limit, percent = TRUE, link = "log", + .data$estimate / 3, + lcl = .data$lower_confidence_limit / 3, + sign = TRUE, + ucl = .data$upper_confidence_limit / 3, + percent = TRUE, + link = "log", change = TRUE ), looptijd = format_ci( - looptijd * .data$estimate, lcl = looptijd * .data$lower_confidence_limit, - ucl = looptijd * .data$upper_confidence_limit, percent = TRUE, - sign = TRUE, link = "log", change = TRUE + looptijd * .data$estimate / 3, + sign = TRUE, + link = "log", + change = TRUE, + lcl = looptijd * .data$lower_confidence_limit / 3, + ucl = looptijd * .data$upper_confidence_limit / 3, + percent = TRUE ) ) -> trend_cycle +meta |> + inner_join(x = read_vc("index", root), by = "analysis") |> + group_by(.data$cycle) |> + slice_max(.data$alternative, n = 1, with_ties = TRUE) |> + filter(.data$reference %in% range(.data$reference)) |> + mutate( + estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, + lcl = ifelse(.data$cycle, 1 / 3, 1) * .data$lower_confidence_limit, + ucl = ifelse(.data$cycle, 1 / 3, 1) * .data$upper_confidence_limit, + klasse = classification(.data$lcl, .data$ucl, threshold), + wijziging = format_ci( + exp(.data$estimate) - 1, + lcl = exp(.data$lcl) - 1, + ucl = exp(.data$ucl) - 1, + percent = TRUE, + sign = TRUE + ) + ) |> + inner_join( + tribble( + ~tekst, ~y, "stabiel", 0, "toename", 1, "afname", -1, + "sterke toename", 1.5, "sterke afname", -1.5, "matige toename", 0.5, + "matige afname", -0.5, "mogelijke toename", 0.5, + "mogelijke afname", -0.5, "onduidelijk", 0 + ) |> + mutate( + s = c(rep(0.25, 7), 0.5, 0.5, 1), + lcl = qnorm(0.05, .data$y, .data$s), + ucl = qnorm(0.95, .data$y, .data$s) + ) |> + transmute(.data$tekst, klasse = classification(.data$lcl, .data$ucl, 1)), + by = "klasse" + ) |> + mutate( + .data$cycle, + verschil = sprintf( + "De toestand in %s verschilt met %s t.o.v. %s (%s).", + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$alternative, .data$alternative + 2), + .data$alternative + ), + .data$wijziging, + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$reference, .data$reference + 2), + .data$reference + ), + .data$tekst + ) + ) |> + summarise( + verschil = paste(.data$verschil, collapse = "\n"), .groups = "drop" + ) -> dit_verschil ``` -## Analyse per jaar {#sec-jaarlijks-{{label}}} +## Analyse per jaar {#sec-jaarlijks} Op basis van jaarlijkse gegevens zien we gemiddeld `r trend_tekst(trend_year$tekst)` `r trend_year$jaarlijks` per jaar of `r trend_year$looptijd` over de volledige looptijd van het meetnet. Deze trend is `r interpretatie$interpretatie[!interpretatie$cycle]`. +`r dit_verschil$verschil[!dit_verschil$cycle]` -```{r fig-year-estimate-{{label}}} +```{r fig-year-estimate} #| fig-cap: !expr caption_year_estimate meta |> filter(!.data$cycle) |> semi_join(x = read_vc("estimate", root), by = "analysis") |> transmute( - .data$year, .data$upper_confidence_limit, + .data$year, + .data$upper_confidence_limit, display = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + link = "log" ) |> sprintf(fmt = "%2$i: %1$s", .data$year), estimate = exp(.data$estimate), @@ -166,84 +244,133 @@ meta |> sd = .data$sd / 2 / qnorm(0.95) ) -> estimate_year plot_ly(estimate_year, x = ~year, y = ~estimate) |> - add_fan(sd = ~sd, link = "log", text = ~display) |> + add_fan( + sd = ~sd, + link = "log", + text = ~display, + hoverinfo = "text", + name = "index" + ) |> layout( xaxis = list(title = ""), yaxis = list( title = "gemiddeld aantal dieren per meetpunt", range = c(0, 1.1 * exp(max(estimate_year$upper_confidence_limit))) ), - title = this_species, - hoverlabel = list(bgcolor = inbo_lichtblauw, bordercolor = inbo_steun_blauw) + title = this_species ) |> config( modeBarButtonsToRemove = list( - "lasso2d", "select2d", "autoScale2d", "hoverClosestCartesian", - "hoverCompareCartesian", "toggleSpikelines" + "lasso2d", + "select2d", + "autoScale2d", + "hoverClosestCartesian", + "hoverCompareCartesian", + "toggleSpikelines" ), displaylogo = FALSE ) ``` -```{r fig-year-change-{{label}}} +```{r fig-year-change} #| warning: false #| fig-cap: !expr caption_year_index meta |> filter(!.data$cycle) |> semi_join(x = read_vc("index", root), by = "analysis") |> + select(-"analysis") |> mutate( interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) ) -> nadien nadien |> mutate( - old = .data$reference, reference = .data$alternative, - alternative = .data$old, estimate = -.data$estimate, + old = .data$reference, + reference = .data$alternative, + alternative = .data$old, + estimate = -.data$estimate, old = .data$lower_confidence_limit, lower_confidence_limit = -.data$upper_confidence_limit, upper_confidence_limit = -.data$old, interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) - ) -> voordien + ) |> + select(-"old") -> voordien voordien |> bind_rows( nadien, nadien |> distinct(.data$reference) |> mutate( - alternative = .data$reference, estimate = 0, lcl = 0, ucl = 0, - wijziging = "", interpretatie = "referentie" + alternative = .data$reference, + estimate = 0, + wijziging = "", + interpretatie = "referentie" ) ) -> jaarlijks -range(jaarlijks$alternative) + c(-1, 1) -> bereik_x +range(jaarlijks$reference) + c(-1, 1) -> bereik_x +c( + jaarlijks$lower_confidence_limit, + threshold, + jaarlijks$upper_confidence_limit, + -threshold +) |> + range(na.rm = TRUE) -> bereik +change_breaks(n = 5, extra = c(-threshold, threshold))( + bereik +) -> deze_breaks jaarlijks |> mutate(alternative = .data$alternative - 0.49999) |> bind_rows( @@ -253,13 +380,17 @@ jaarlijks |> ) |> arrange(.data$reference, .data$alternative) |> transmute( - .data$reference, .data$alternative, .data$estimate, - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$reference, + .data$alternative, + .data$estimate, sd = .data$upper_confidence_limit - .data$lower_confidence_limit, sd = .data$sd / 2 / qnorm(0.95), wijziging = sprintf( - "%.0f t.o.v. %i\n%s\n%s", round(.data$alternative), .data$reference, - .data$wijziging, .data$interpretatie + "%.0f t.o.v. %i\n%s\n%s", + .data$alternative, + .data$reference, + .data$wijziging, + .data$interpretatie ) |> str_replace_all(".*\n\nreferentie", "referentie"), key = sprintf("%.0f-%i", round(.data$alternative), .data$reference) @@ -271,72 +402,96 @@ jaarlijks |> ref_y = c(0, threshold, -threshold) |> c(rep(NA, n() - 3)), ref_text = c( - "toestand in", "belangrijke afname t.o.v.", "belangrijke toename t.o.v." + "toestand in", + "belangrijke afname t.o.v.", + "belangrijke toename t.o.v." ) |> - paste(.data$reference), - textposition = c("top left", "bottom left") |> - rep(n()) |> - head(n()) + paste(head(.data$reference, 3)) |> + c(rep("", n() - 3)), + textposition = c("top left", "bottom left", "top left") |> + c(rep("", n() - 3)) ) |> SharedData$new(key = ~key) -> index_year -c( - index_year$data(withFilter = FALSE)$lower_confidence_limit, threshold, - index_year$data(withFilter = FALSE)$upper_confidence_limit, -threshold -) |> - range(na.rm = TRUE) -> bereik -change_breaks(n = 5, extra = c(-threshold, threshold))( - bereik -) -> deze_breaks bscols( widths = c(10, 2), - plot_ly(index_year, x = ~alternative, y = ~estimate) |> - add_fan(sd = ~sd, text = ~wijziging) |> + plot_ly(index_year, x = ~alternative, y = ~estimate, text = ~wijziging) |> + add_fan( + sd = ~sd, + text = ~wijziging, + name = "index", + step = 0.3, + showlegend = FALSE + ) |> add_text( - x = ~ref_x, y = ~ref_y, text = ~ref_text, textposition = ~textposition, - color = I(inbo_hoofd), hoverinfo = "none" + x = ~ref_x, + y = ~ref_y, + text = ~ref_text, + textposition = ~textposition, + color = I(inbo_hoofd), + hoverinfo = "none", + showlegend = FALSE ) |> config( displaylogo = FALSE, modeBarButtonsToRemove = c( - "autoScale", "hoverCompareCartesian", "lasso2d", "select2d" + "autoScale", + "hoverCompareCartesian", + "lasso2d", + "select2d" ) ) |> layout( title = this_species, xaxis = list(title = list(font = list(size = 0)), range = bereik_x), yaxis = list( - title = list(font = list(size = 0)), zerolinewidth = 1, - tickvals = deze_breaks, ticktext = change_labels(deze_breaks) + title = list(font = list(size = 0)), + zerolinewidth = 1, + tickvals = deze_breaks, + ticktext = change_labels(deze_breaks) ), shapes = list( - type = "rect", x0 = 0, x1 = 1, xref = "paper", y0 = threshold, - y1 = -threshold, line = list(width = 0), opacity = 0.1, - fillcolor = "black", layer = "below" + type = "rect", + x0 = 0, + x1 = 1, + xref = "paper", + y0 = threshold, + y1 = -threshold, + line = list(width = 0), + opacity = 0.1, + fillcolor = "black", + layer = "below" ) ), filter_select( - id = "ref_year", label = "Referentiejaar", sharedData = index_year, - group = ~reference, multiple = FALSE + id = "ref_year", + label = "Referentiejaar", + sharedData = index_year, + group = ~reference, + multiple = FALSE ) ) %>% `class<-`(c(class(.), "htmlwidget")) ``` -## Analyse per driejarige cyclus {#sec-driejaarlijks-{{label}}} +## Analyse per driejarige cyclus {#sec-driejaarlijks} Op basis van jaarlijkse gegevens zien we gemiddeld `r trend_tekst(trend_cycle$tekst)` `r trend_cycle$jaarlijks` per jaar of `r trend_cycle$looptijd` over de volledige looptijd van het meetnet. Deze trend is `r interpretatie$interpretatie[interpretatie$cycle]`. +`r dit_verschil$verschil[dit_verschil$cycle]` -```{r fig-cycle-estimate-{{label}}} -#| fig-cap: !expr caption_year_estimate +```{r fig-cycle-estimate} +#| fig-cap: !expr caption_cycle_estimate meta |> filter(.data$cycle) |> semi_join(x = read_vc("estimate", root), by = "analysis") |> transmute( - .data$year, .data$upper_confidence_limit, + .data$year, + .data$upper_confidence_limit, display = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + link = "log" ) |> sprintf(fmt = "%2$i - %3$i: %1$s", .data$year, .data$year + 2), estimate = exp(.data$estimate), @@ -351,29 +506,39 @@ estimate_cycle |> mutate(year = .data$year + 1.49999) ) |> plot_ly(x = ~year, y = ~estimate) |> - add_fan(sd = ~sd, link = "log", text = ~display) |> + add_fan( + sd = ~sd, + link = "log", + text = ~display, + hoverinfo = "text", + name = "index" + ) |> layout( xaxis = list( - title = "", tickvals = estimate_cycle$year, + title = "", + tickvals = estimate_cycle$year, ticktext = sprintf("%i-%i", estimate_cycle$year, estimate_cycle$year + 2) ), yaxis = list( title = "gemiddeld aantal dieren per meetpunt", range = c(0, 1.1 * exp(max(estimate_cycle$upper_confidence_limit))) ), - title = this_species, - hoverlabel = list(bgcolor = inbo_lichtblauw, bordercolor = inbo_steun_blauw) + title = this_species ) |> config( modeBarButtonsToRemove = list( - "lasso2d", "select2d", "autoScale2d", "hoverClosestCartesian", - "hoverCompareCartesian", "toggleSpikelines" + "lasso2d", + "select2d", + "autoScale2d", + "hoverClosestCartesian", + "hoverCompareCartesian", + "toggleSpikelines" ), displaylogo = FALSE ) ``` -```{r fig-cycle-change-{{label}}} +```{r fig-cycle-change} #| warning: false #| fig-cap: !expr caption_cycle_index meta |> @@ -381,44 +546,70 @@ meta |> semi_join(x = read_vc("index", root), by = "analysis") |> mutate( interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) ) -> nadien nadien |> mutate( - old = .data$reference, reference = .data$alternative, - alternative = .data$old, estimate = -.data$estimate, + old = .data$reference, + reference = .data$alternative, + alternative = .data$old, + estimate = -.data$estimate, old = .data$lower_confidence_limit, lower_confidence_limit = -.data$upper_confidence_limit, upper_confidence_limit = -.data$old, interpretatie = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, + .data$lower_confidence_limit, + .data$upper_confidence_limit, threshold = threshold ) |> as.character() |> recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" + "++" = "sterke toename", + "+" = "toename", + "+~" = "matige toename", + "~" = "stabiel", + "-~" = "matige afname", + "-" = "afname", + "--" = "sterke afname", + "?+" = "mogelijke toename", + "?-" = "mogelijke afname", + "?" = "onduidelijk", + "R" = "referentie" ), wijziging = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, percent = TRUE, sign = TRUE, - change = TRUE, link = "log" + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + change = TRUE, + link = "log" ) ) -> voordien voordien |> @@ -427,8 +618,12 @@ voordien |> nadien |> distinct(.data$reference) |> mutate( - alternative = .data$reference, estimate = 0, lcl = 0, ucl = 0, - wijziging = "", interpretatie = "referentie" + alternative = .data$reference, + estimate = 0, + lcl = 0, + ucl = 0, + wijziging = "", + interpretatie = "referentie" ) ) |> mutate( @@ -436,8 +631,12 @@ voordien |> sd = .data$upper_confidence_limit - .data$lower_confidence_limit, sd = .data$sd / 2 / qnorm(0.95), wijziging = sprintf( - "%i-%i t.o.v. %s\n%s\n%s", .data$alternative, .data$alternative + 2, - .data$reference, .data$wijziging, .data$interpretatie + "%i-%i t.o.v. %s\n%s\n%s", + .data$alternative, + .data$alternative + 2, + .data$reference, + .data$wijziging, + .data$interpretatie ) |> str_replace_all(".*\n\nreferentie", "referentie") ) -> jaarlijks @@ -451,8 +650,13 @@ jaarlijks |> ) |> arrange(.data$reference, .data$alternative) |> select( - "reference", "alternative", "estimate", "lower_confidence_limit", - "upper_confidence_limit", "sd", "wijziging" + "reference", + "alternative", + "estimate", + "lower_confidence_limit", + "upper_confidence_limit", + "sd", + "wijziging" ) |> group_by(.data$reference) |> mutate( @@ -461,7 +665,9 @@ jaarlijks |> ref_y = c(0, threshold, -threshold) |> c(rep(NA, n() - 3)), ref_text = c( - "toestand in", "belangrijke afname t.o.v.", "belangrijke toename t.o.v." + "toestand in", + "belangrijke afname t.o.v.", + "belangrijke toename t.o.v." ) |> paste(.data$reference), textposition = c("top left", "bottom left") |> @@ -471,8 +677,10 @@ jaarlijks |> ) |> SharedData$new(key = ~key) -> index_cycle c( - index_cycle$data(withFilter = FALSE)$lower_confidence_limit, threshold, - index_cycle$data(withFilter = FALSE)$upper_confidence_limit, -threshold + index_cycle$data(withFilter = FALSE)$lower_confidence_limit, + threshold, + index_cycle$data(withFilter = FALSE)$upper_confidence_limit, + -threshold ) |> range(na.rm = TRUE) -> bereik change_breaks(n = 5, extra = c(-threshold, threshold))( @@ -481,33 +689,60 @@ change_breaks(n = 5, extra = c(-threshold, threshold))( bscols( widths = c(10, 2), plot_ly(index_cycle, x = ~alternative, y = ~estimate) |> - add_fan(sd = ~sd, text = ~wijziging) |> + add_fan( + sd = ~sd, + text = ~wijziging, + hoverinfo = "text", + step = 0.3, + name = "index", + showlegend = FALSE + ) |> add_text( - x = ~ref_x, y = ~ref_y, text = ~ref_text, textposition = ~textposition, - color = I(inbo_hoofd), hoverinfo = "none" + x = ~ref_x, + y = ~ref_y, + text = ~ref_text, + textposition = ~textposition, + color = I(inbo_hoofd), + hoverinfo = "none", + showlegend = FALSE ) |> config( displaylogo = FALSE, modeBarButtonsToRemove = c( - "autoScale", "hoverCompareCartesian", "lasso2d", "select2d" + "autoScale", + "hoverCompareCartesian", + "lasso2d", + "select2d" ) ) |> layout( title = this_species, xaxis = list(title = list(font = list(size = 0)), range = bereik_x), yaxis = list( - title = list(font = list(size = 0)), zerolinewidth = 1, - tickvals = deze_breaks, ticktext = change_labels(deze_breaks) + title = list(font = list(size = 0)), + zerolinewidth = 1, + tickvals = deze_breaks, + ticktext = change_labels(deze_breaks) ), shapes = list( - type = "rect", x0 = 0, x1 = 1, xref = "paper", y0 = threshold, - y1 = -threshold, line = list(width = 0), opacity = 0.1, - fillcolor = "black", layer = "below" + type = "rect", + x0 = 0, + x1 = 1, + xref = "paper", + y0 = threshold, + y1 = -threshold, + line = list(width = 0), + opacity = 0.1, + fillcolor = "black", + layer = "below" ) ), filter_select( - id = "ref_cycle", label = "Referentieperiode", sharedData = index_cycle, - group = ~reference, multiple = FALSE + id = "ref_cycle", + label = "Referentieperiode", + sharedData = index_cycle, + group = ~reference, + multiple = FALSE ) ) %>% `class<-`(c(class(.), "htmlwidget")) @@ -515,7 +750,9 @@ bscols( ## Kenmerken van de analyses -```{r tbl-strata-{{label}}} +Het cijfermateriaal om de figuren te reproduceren is beschikbaar via de [download pagina](../methodologie/reproduceerbaarheid.html#downloadbaar-cijfermateriaal). + +```{r tbl-strata} #| tbl-cap: !expr caption_strata read_vc("effort", root) |> distinct(.data$stratum, .data$location) |> @@ -531,12 +768,20 @@ read_vc("effort", root) |> by = "stratum" ) |> transmute( - .data$stratum, gewicht = sprintf("%.1f%%", 100 * .data$weight), + .data$stratum, + gewicht = sprintf("%.1f%%", 100 * .data$weight), aanwezig = round(.data$relevant * .data$totaal / .data$onderzocht, 1), - .data$relevant, .data$onderzocht, .data$totaal, bezoeken = .data$visits, + .data$relevant, + .data$onderzocht, + .data$totaal, + bezoeken = .data$visits, punten = ( - .data$points_1 / 6 + .data$points_2 / 3 + .data$points_3 / 2 + - .data$points_4 * 2 / 3 + .data$points_5 * 5 / 6 + .data$points_6 + .data$points_1 / 6 + + .data$points_2 / 3 + + .data$points_3 / 2 + + .data$points_4 * 2 / 3 + + .data$points_5 * 5 / 6 + + .data$points_6 ) / .data$relevant, punten = sprintf("%.1f%%", 100 * .data$punten) ) |> @@ -544,7 +789,7 @@ read_vc("effort", root) |> gt() ``` -```{r tbl-hash-{{label}}} +```{r tbl-hash} #| tbl-cap: Data-hashes van de analyses in het kader van #| [traceerbaarheid]( #| ../methodologie/reproduceerbaarheid.html#sec-traceerbaarheid). @@ -552,7 +797,8 @@ meta |> arrange(.data$cycle, .data$linear) |> transmute( frequentie = factor( - .data$cycle, levels = c(TRUE, FALSE), + .data$cycle, + levels = c(TRUE, FALSE), labels = c("driejaarlijks", "jaarlijks") ), model = ifelse(.data$linear, "lineair", "niet-lineair"), @@ -563,13 +809,13 @@ meta |> cols_width(analyse ~ px(20), status ~ px(20)) ``` -```{js change-js-{{label}}} +```{js change-js} function filter_default() { document.getElementById("ref_year").getElementsByClassName("selectized")[0].selectize.removeOption(""); - document.getElementById("ref_year").getElementsByClassName("selectized") + document.getElementById("ref_year").getElementsByClassName("selectized") [0].selectize.setValue("2007", false); document.getElementById("ref_cycle").getElementsByClassName("selectized")[0].selectize.removeOption(""); - document.getElementById("ref_cycle").getElementsByClassName("selectized") + document.getElementById("ref_cycle").getElementsByClassName("selectized") [0].selectize.setValue("2007-2009", false); } $(document).ready(filter_default); diff --git a/inst/interactive/species_pdf.qmd b/inst/interactive/species_pdf.qmd new file mode 100644 index 0000000..5462427 --- /dev/null +++ b/inst/interactive/species_pdf.qmd @@ -0,0 +1,505 @@ +--- +title: "{{species}}" +--- + +```{r} +#| label: setup-{{label}} +library(effectclass) +library(git2rdata) +library(INBOtheme) +library(knitr) +library(pander) +library(tidyverse) +conflicted::conflicts_prefer(dplyr::filter, dplyr::pull) +opts_chunk$set(dev = "cairo_pdf") +update_geom_defaults("point", list(size = 2)) +update_geom_defaults("text", list(size = 3)) +theme_set(theme_inbo(base_size = 8)) +root <- system.file("results", package = "abvanalysis") +if (interactive()) { + read_vc("meta", root) |> + filter(!.data$composite, .data$status == "converged") |> + sample_n(1) |> + pull(.data$speciesgroup) -> id +} else { + id <- "{{species}}" +} +threshold <- 0.75 +c(rev(traffic_palette(7)), "grey65", "grey35", "grey50") |> + setNames( + c("++", "+", "+~", "~", "-~", "-", "--", "?+", "?-", "?") + ) -> kleurgradient +kleurgradient[4] <- inbo_steun_blauw +periode_labels <- function(x) { + sprintf("%i - %i", x, x + 2) +} +``` + +```{r} +#| label: basis-{{label}} +read_vc("meta", root) |> + filter(.data$speciesgroup == id) -> deze_analyse +deze_analyse |> + select("analysis", "cycle") |> + inner_join(read_vc("estimate", root), by = "analysis") |> + filter( + !.data$cycle | + .data$year < max(.data$year) | + (.data$year == max(.data$year) & (max(.data$year) - 2007) %% 3 == 2) + ) -> dit_aantal +looptijd <- diff(range(dit_aantal$year)) +tribble( + ~x, ~y, "stabiel", 0, "toename", 1, "afname", -1, "sterke toename", 1.5, + "sterke afname", -1.5, "matige toename", 0.5, "matige afname", -0.5, + "mogelijke toename", 0.5, "mogelijke afname", -0.5, "onduidelijk", 0 +) |> + mutate( + s = c(rep(0.25, 7), 0.5, 0.5, 1), + lcl = qnorm(0.05, .data$y, .data$s), + ucl = qnorm(0.95, .data$y, .data$s), + klasse = classification(.data$lcl, .data$ucl, 1) + ) |> + arrange(.data$klasse) |> + mutate( + x = factor(.data$x, levels = rev(.data$x)), + x_int = as.integer(.data$x) + ) -> effect +deze_analyse |> + select("analysis", "cycle") |> + inner_join(read_vc("linear_trend", root), by = "analysis") |> + mutate( + estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, + lcl = ifelse(.data$cycle, 1 / 3, 1) * .data$lower_confidence_limit, + ucl = ifelse(.data$cycle, 1 / 3, 1) * .data$upper_confidence_limit, + klasse = classification( + .data$lcl * looptijd, + .data$ucl * looptijd, + log(threshold) + ), + jaarlijks = format_ci( + exp(.data$estimate) - 1, + lcl = exp(.data$lcl) - 1, + ucl = exp(.data$ucl) - 1, + percent = TRUE, + sign = TRUE + ), + looptijd = format_ci( + exp(looptijd * .data$estimate) - 1, + lcl = exp(looptijd * .data$lcl) - 1, + ucl = exp(looptijd * .data$ucl) - 1, + percent = TRUE, + sign = TRUE + ) + ) |> + inner_join( + effect |> + select("klasse", tekst = "x"), + by = "klasse" + ) -> deze_lineair +deze_analyse |> + select("analysis", "cycle") |> + inner_join(x = read_vc("index", root), by = "analysis") |> + filter( + !.data$cycle | + .data$alternative < max(.data$alternative) | + ( + .data$alternative == max(.data$alternative) & + (.data$alternative - 2007) %% 3 == 2 + ) + ) -> deze_wijziging +deze_wijziging |> + group_by(.data$cycle) |> + slice_max(.data$alternative, n = 1, with_ties = TRUE) |> + filter(.data$reference %in% range(.data$reference)) |> + mutate( + estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, + lcl = ifelse(.data$cycle, 1 / 3, 1) * .data$lower_confidence_limit, + ucl = ifelse(.data$cycle, 1 / 3, 1) * .data$upper_confidence_limit, + klasse = classification( + .data$lcl, + .data$ucl, + log(threshold) + ), + wijziging = format_ci( + exp(.data$estimate) - 1, + lcl = exp(.data$lcl) - 1, + ucl = exp(.data$ucl) - 1, + percent = TRUE, + sign = TRUE + ) + ) |> + inner_join( + effect |> + select("klasse", tekst = "x"), + by = "klasse" + ) |> + mutate( + .data$cycle, + verschil = sprintf( + "De toestand in %s verschilt met %s t.o.v. %s (%s).", + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$alternative, .data$alternative + 2), + .data$alternative + ), + .data$wijziging, + ifelse( + .data$cycle, + sprintf("de periode %i-%i", .data$reference, .data$reference + 2), + .data$reference + ), + .data$tekst + ) + ) |> + summarise( + verschil = paste(.data$verschil, collapse = "\n"), .groups = "drop" + ) -> dit_verschil +trend_tekst <- function(x) { + if (x == "stabiel") { + return("een stabiele toestand. De trend blijft beperkt tot") + } + if (x == "onduidelijk") { + return("een onduidelijke toestand. De geschatte trend bedraagt") + } + paste("een", x, "met") +} +``` + +## Analyse per jaar {#sec-jaar-{{label}}} + +Op basis van jaarlijkse gegevens zien we gemiddeld `r trend_tekst(deze_lineair$tekst[!deze_lineair$cycle])` `r deze_lineair$jaarlijks[!deze_lineair$cycle]` per jaar of `r deze_lineair$looptijd[!deze_lineair$cycle]` over de volledige looptijd van het meetnet. +`r dit_verschil$verschil[dit_verschil$cycle]` + +```{r} +#| label: fig-aantal-{{label}} +#| fig.cap: "Evolutie van het gemodelleerde gemiddeld aantal waargenomen dieren +#| op een meetpunt voor {{species}} tijdens de referentieperiode. +#| Zie §[-@sec-onzekerheid] voor een verklaring van de intervallen." +#| warning: false +dit_aantal |> + filter(!.data$cycle) |> + mutate( + sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / + 2 / + qnorm(0.95), + estimate = exp(.data$estimate) + ) |> + ggplot(aes(x = year, y = estimate)) + + stat_fan(aes(link_sd = sd), link = "log") + + scale_y_continuous("gemiddeld aantal dieren per meetput", limits = c(0, NA)) + + theme(axis.title.x = element_blank()) +``` + +```{r} +#| label: fig-wijziging-jaar-statisch-{{label}} +#| fig-cap: "Wijzigingen tussen jaren voor {{species}}. +#| Zie §[-@sec-onzekerheid] voor een verklaring van de intervallen en +#| §[-@sec-trendklasse] voor uitleg over de referentielijnen." +#| warning: false +deze_wijziging |> + filter(!.data$cycle, .data$reference == min(.data$reference)) |> + mutate( + sd = (.data$upper_confidence_limit - .data$estimate) / qnorm(0.95) + ) -> jaarlijks +change_breaks(n = 4, extra = c(log(threshold), -log(threshold)))( + c(jaarlijks$lower_confidence_limit, jaarlijks$upper_confidence_limit) +) -> deze_breaks +ggplot(jaarlijks, aes(x = alternative, y = estimate)) + + annotate( + geom = "rect", + xmin = -Inf, + xmax = Inf, + ymin = log(threshold), + ymax = -log(threshold), + alpha = 0.2, + fill = inbo_grijs + ) + + geom_hline(yintercept = 0) + + stat_fan(aes(link_sd = sd), geom = "rect") + + annotate( + geom = "text", + x = Inf, + y = 0, + hjust = 1, + vjust = -0.1, + label = "toestand in 2007", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = log(threshold), + hjust = 1, + vjust = 1, + label = "belangrijke afname t.o.v. 2007", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = -log(threshold), + hjust = 1, + vjust = 0, + label = "belangrijke toename t.o.v. 2007", + colour = inbo_hoofd + ) + + theme(axis.title = element_blank()) + + scale_y_continuous(breaks = deze_breaks, labels = change_labels) +``` + +```{r} +#| label: fig-wijziging-jaar-alles2-{{label}} +#| fig-cap: "Interpretatie van het verschil van een bepaald jaar (kolom) t.o.v. +#| een bepaald referentiejaar (rij) voor {{species}}. +#| Gebruik deze figuur om het verschil tussen twee jaren in te schatten. +#| Zie §[-@sec-paarsgewijs] voor meer uitleg over deze figuur." +deze_wijziging |> + filter(!.data$cycle) |> + rename(alternative = "reference", reference = "alternative") |> + mutate( + old = .data$lower_confidence_limit, + lower_confidence_limit = -.data$upper_confidence_limit, + upper_confidence_limit = -.data$old + ) |> + bind_rows( + deze_wijziging |> + filter(!.data$cycle) + ) |> + mutate( + klasse = classification( + .data$lower_confidence_limit, + .data$upper_confidence_limit, + threshold = log(threshold) + ) + ) |> + ggplot(aes(x = alternative, y = reference, label = klasse)) + + geom_point(aes(colour = klasse), size = 3, show.legend = FALSE) + + scale_colour_manual(values = kleurgradient) + + geom_text( + aes(label = klasse), + hjust = 0.5, + vjust = 0.5, + colour = "white", + size = 2 + ) + + coord_fixed() + + theme( + axis.title.x = element_blank(), + axis.text.x = element_text(angle = 90, vjust = 0.5) + ) +``` + +## Analyse per driejaarlijkse cyclus {#sec-cyclus-{{label}}} + +Op basis van driejaarlijkse gegevens zien we gemiddeld `r trend_tekst(deze_lineair$tekst[deze_lineair$cycle])` `r deze_lineair$jaarlijks[deze_lineair$cycle]` per jaar of `r deze_lineair$looptijd[deze_lineair$cycle]` over de volledige looptijd van het meetnet. +`r dit_verschil$verschil[dit_verschil$cycle]` + +```{r} +#| label: fig-aantal-cyclus-{{label}} +#| fig.cap: "Evolutie van het gemodelleerde gemiddeld aantal waargenomen dieren +#| op een meetpunt voor {{species}} tijdens de referentieperiode. +#| Zie §[-@sec-onzekerheid] voor een verklaring van de intervallen." +#| warning: false +dit_aantal |> + filter(.data$cycle) |> + mutate( + sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / + 2 / + qnorm(0.95), + estimate = exp(.data$estimate) + ) -> cyclus_aantal +ggplot(cyclus_aantal, aes(x = year, y = estimate)) + + stat_fan(aes(link_sd = sd), link = "log", geom = "rect") + + scale_x_continuous(breaks = cyclus_aantal$year, labels = periode_labels) + + scale_y_continuous("gemiddeld aantal dieren per meetput", limits = c(0, NA)) + + theme(axis.title.x = element_blank()) +``` + +```{r} +#| label: fig-wijziging-cyclus-statisch-{{label}} +#| fig-cap: "Wijzigingen per driejarige cyclus voor {{species}}. +#| Zie §[-@sec-onzekerheid] voor een verklaring van de intervallen en +#| §[-@sec-trendklasse] voor uitleg over de referentielijnen." +#| warning: false +deze_wijziging |> + filter(.data$cycle, .data$reference == min(.data$reference)) |> + mutate( + sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / + 2 / + qnorm(0.95) + ) -> jaarlijks +change_breaks(n = 4, extra = c(log(threshold), -log(threshold)))( + c(jaarlijks$lcl, jaarlijks$ucl) +) -> deze_breaks +ggplot(jaarlijks, aes(x = alternative, y = estimate)) + + annotate( + geom = "rect", + xmin = -Inf, + xmax = Inf, + ymin = log(threshold), + ymax = -log(threshold), + alpha = 0.2, + fill = inbo_grijs + ) + + geom_hline(yintercept = 0) + + stat_fan(aes(link_sd = sd), geom = "rect") + + annotate( + geom = "text", + x = Inf, + y = 0, + hjust = 1, + vjust = -0.1, + label = "toestand in 2007-2009", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = log(threshold), + hjust = 1, + vjust = 1, + label = "belangrijke afname t.o.v. 2007-2009", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = -log(threshold), + hjust = 1, + vjust = 0, + label = "belangrijke toename t.o.v. 2007-2009", + colour = inbo_hoofd + ) + + theme(axis.title = element_blank()) + + scale_x_continuous(breaks = jaarlijks$alternative, labels = periode_labels) + + scale_y_continuous(breaks = deze_breaks, labels = change_labels) +``` + +```{r} +#| label: fig-wijziging-jaar-alles3-{{label}} +#| fig-cap: "Interpretatie van het verschil van een bepaalde periode (kolom) +#| t.o.v. een bepaald referentieperiode (rij) voor {{species}}. +#| Gebruik deze figuur om het verschil tussen twee jaren in te schatten. +#| Zie §[-@sec-paarsgewijs] voor meer uitleg over deze figuur." +deze_wijziging |> + filter(.data$cycle) |> + rename(alternative = .data$reference, reference = .data$alternative) |> + mutate( + old = .data$lower_confidence_limit, + lower_confidence_limit = -.data$upper_confidence_limit, + upper_confidence_limit = -.data$old + ) |> + bind_rows( + deze_wijziging |> + filter(.data$cycle) + ) |> + mutate( + klasse = classification( + .data$lower_confidence_limit, + .data$upper_confidence_limit, + threshold = log(threshold) + ) + ) |> + ggplot(aes(x = alternative, y = reference, label = klasse)) + + geom_point(aes(colour = klasse), size = 3, show.legend = FALSE) + + scale_x_continuous( + breaks = seq( + min(deze_wijziging$reference), + max(deze_wijziging$alternative), + by = 3 + ), + labels = periode_labels + ) + + scale_y_continuous( + "referentie", + breaks = seq( + min(deze_wijziging$reference), + max(deze_wijziging$alternative), + by = 3 + ), + labels = periode_labels + ) + + scale_colour_manual(values = kleurgradient) + + geom_text( + aes(label = klasse), + hjust = 0.5, + vjust = 0.5, + colour = "white", + size = 2 + ) + + coord_fixed() + + theme( + axis.title.x = element_blank(), + axis.text.x = element_text(angle = 90, vjust = 0.5) + ) +``` + +## Kenmerken van de gegevens + +```{r} +#| label: tbl-stratum-{{label}} +#| tbl-cap: Stratumgewicht, raming van het aantal hokken waarin de soort +#| aanwezig is, aantal relevante hokken voor de analyse, aantal onderzochte +#| hokken in het stratum, totaal aantal hokken van het stratum in +#| Vlaanderen, aantal bezoeken aan een meetpunt en het gemiddeld aandeel +#| relevante punten per hok voor {{species}} (zie §[-@sec-stratumgewicht]). +read_vc("effort", root = root) |> + distinct(.data$stratum, .data$location) |> + count(.data$stratum, name = "onderzocht") |> + inner_join( + read_vc("stratum", root = root) |> + select(stratum = "description", totaal = "n"), + by = "stratum" + ) |> + inner_join( + read_vc("stratum_weight", root = root) |> + semi_join(deze_analyse, by = "analysis"), + by = "stratum" + ) |> + transmute( + .data$stratum, + gewicht = sprintf("%.1f%%", 100 * .data$weight), + aanwezig = sprintf( + "%.2f", + .data$relevant * .data$totaal / .data$onderzocht + ), + .data$relevant, + .data$onderzocht, + .data$totaal, + bezoeken = .data$visits, + punten = sprintf( + "%.1f%%", + 100 * + ( + .data$points_1 / 6 + + .data$points_2 / 3 + + .data$points_3 / 2 + + .data$points_4 * 2 / 3 + + .data$points_5 * 5 / 6 + + .data$points_6 + ) / .data$relevant + ) + ) |> + arrange(desc(.data$gewicht)) |> + kable(align = "lrrrrrrr") +``` + +```{r} +#| label: tbl-hash-{{label}} +#| tbl-cap: "Data-hashes van de analyses in het kader van traceerbaarheid +#| (zie §[-@sec-traceerbaarheid])." +#| results: "asis" +deze_analyse |> + arrange(.data$cycle, .data$linear) |> + transmute( + frequentie = factor( + .data$cycle, + levels = c(TRUE, FALSE), + labels = c("driejaarlijks", "jaarlijks") + ), + model = ifelse(.data$linear, "lineair", "niet-lineair"), + analyse = str_replace(.data$analysis, "(.{20})", "\\1 "), + status = str_replace(.data$status_fingerprint, "(.{20})", "\\1 ") + ) |> + pandoc.table() +``` diff --git a/inst/interactive/trends.qmd b/inst/interactive/trends.qmd index e466458..aac0012 100644 --- a/inst/interactive/trends.qmd +++ b/inst/interactive/trends.qmd @@ -6,12 +6,7 @@ library(git2rdata) library(DT) library(tidyverse) threshold <- log(0.75) -if (interactive()) { - root <- file.path("..", "..", "..", "abv", "results") -} else { - root <- file.path("..", "..", "results") - this_species <- "{{species}}" -} +root <- system.file("results", package = "abvanalysis") read_vc("meta", root) |> filter(!.data$composite) |> select("speciesgroup", "cycle", "waic", "linear") |> @@ -37,8 +32,10 @@ read_vc("meta", root) |> slice_max(.data$year, n = 1) |> transmute(.data$analysis, looptijd = .data$year - 1), by = "analysis" - ) -> tmp -tmp |> + ) |> + group_by(.data$speciesgroup) |> + mutate(looptijd = max(.data$looptijd)) |> + ungroup() |> mutate( estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, lower_confidence_limit = ifelse(.data$cycle, 1 / 3, 1) * @@ -50,7 +47,6 @@ tmp |> ucl = .data$upper_confidence_limit, percent = TRUE, link = "log", change = TRUE ), - looptijd = ifelse(.data$cycle, 3, 1) * .data$looptijd, klasse = classification( .data$lower_confidence_limit * .data$looptijd, .data$upper_confidence_limit * .data$looptijd, threshold = threshold @@ -100,11 +96,11 @@ trends |> escape = FALSE, filter = "bottom", rownames = NULL, options = list( language = list( - url = "//cdn.datatables.net/plug-ins/1.10.11/i18n/Dutch.json" + url = "https://cdn.datatables.net/plug-ins/1.10.11/i18n/Dutch.json" ), pageLength = 25 ) -) + ) ``` @@ -118,9 +114,9 @@ trends |> escape = FALSE, filter = "bottom", rownames = NULL, options = list( language = list( - url = "//cdn.datatables.net/plug-ins/1.10.11/i18n/Dutch.json" + url = "https://cdn.datatables.net/plug-ins/1.10.11/i18n/Dutch.json" ), pageLength = 25 ) -) + ) ``` diff --git a/inst/interactive/trends_pdf.qmd b/inst/interactive/trends_pdf.qmd new file mode 100644 index 0000000..8047618 --- /dev/null +++ b/inst/interactive/trends_pdf.qmd @@ -0,0 +1,176 @@ +# Overzicht van de trends + +```{r setup} +library(effectclass) +library(git2rdata) +library(tidyverse) +threshold <- log(0.75) +root <- system.file("results", package = "abvanalysis") +read_vc("meta", root) |> + filter(!.data$composite) |> + select("speciesgroup", "cycle", "waic", "linear") |> + pivot_wider(names_from = "linear", values_from = "waic") |> + transmute( + .data$speciesgroup, + .data$cycle, + interpretatie = (.data$`FALSE` - .data$`TRUE`) |> + cut( + breaks = c(-Inf, -2, 0, Inf), + labels = c("niet-lineair", "mogelijk niet-lineair", "lineair") + ) + ) |> + inner_join( + read_vc("meta", root) |> + filter(!.data$composite, .data$linear, .data$status == "converged") |> + select("speciesgroup", "cycle", "analysis"), + by = c("speciesgroup", "cycle") + ) |> + inner_join(read_vc("linear_trend", root), by = "analysis") |> + inner_join( + read_vc("linear_estimate", root) |> + group_by(.data$analysis) |> + slice_max(.data$year, n = 1) |> + transmute(.data$analysis, looptijd = .data$year - 1), + by = "analysis" + ) |> + group_by(.data$speciesgroup) |> + mutate(looptijd = max(.data$looptijd)) |> + ungroup() |> + mutate( + estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, + lower_confidence_limit = ifelse(.data$cycle, 1 / 3, 1) * + .data$lower_confidence_limit, + upper_confidence_limit = ifelse(.data$cycle, 1 / 3, 1) * + .data$upper_confidence_limit, + jaarlijks = format_ci( + .data$estimate, + lcl = .data$lower_confidence_limit, + sign = TRUE, + ucl = .data$upper_confidence_limit, + percent = TRUE, + link = "log", + change = TRUE + ), + klasse = classification( + .data$lower_confidence_limit * .data$looptijd, + .data$upper_confidence_limit * .data$looptijd, + threshold = threshold + ), + across( + c("estimate", "lower_confidence_limit", "upper_confidence_limit"), + ~ .x * .data$looptijd + ), + looptijd = format_ci( + .data$estimate, + lcl = .data$lower_confidence_limit, + sign = TRUE, + ucl = .data$upper_confidence_limit, + percent = TRUE, + link = "log", + change = TRUE + ) + ) -> trends +``` + +@tbl-jaarlijkse-trends geeft een overzicht van de lineaire trends voor elke soort. +We hebben de trends gesorteerd volgens opdeling van de klassen (@sec-trendklasse) en binnen de klasse volgens puntschatting van de trend. +Hierdoor start de tabel met de soorten met de sterkste positieve trends. +Bij de volgende soorten zal de trend minder sterk worden tot we aan de soorten met een stabiele trend komen. +Daarna volgen de soorten met een negatieve trend waarbij de trend steeds sterker negatief wordt. +We sluiten de tabel af met de soorten met een mogelijke of onduidelijke trend. +De tabel bevat de trend uitgedrukt als een jaarlijkse wijziging en als een wijziging over de volledige looptijd van het meetnet. +Verder bevat de tabel de opdeling van de trend in klassen en een indicatie of de trend al dan niet lineair (@sec-lineairetrend) is. +De naam van de soort is een snelkoppeling naar de detail van de soort zelf. +We raden aan om hiervan gebruik te maken bij de interpretatie van niet-lineaire trends. + +```{r} +#| label: tbl-jaarlijkse-trends +#| tbl-cap: Gemiddelde wijziging in veronderstelling van een lineaire trend +#| gebaseerd op jaarlijkse gegevens. +#| results: asis +trends |> + filter(!.data$cycle) |> + arrange(.data$klasse, desc(.data$estimate)) |> + transmute( + lijn = tolower(.data$speciesgroup) |> + str_replace_all(" ", "-") |> + str_remove_all("'") |> + sprintf( + fmt = "\\hyperref[sec-jaar-%s]{%s} & %s & %s & %s & %s \\\\", + .data$speciesgroup, + format(.data$klasse, type = "ascii") |> + sprintf(fmt = "\\texttt{%s}"), + .data$interpretatie, + .data$jaarlijks, + .data$looptijd + ) + ) |> + pull(.data$lijn) |> + str_replace_all("%", "\\\\%") |> + str_replace_all("~", "\\\\textasciitilde") |> + paste(collapse = "\n") |> + sprintf( + fmt = paste( + "\\begin{table}", + "\\begin{footnotesize}", + "\\begin{longtable}{p{31mm}ccp{37mm}p{37mm}}", + "\\hline", + "soort & klasse & interpretatie & jaarlijkse wijziging &", + "wijziging over de looptijd \\\\", + "\\hline", + "%s", + "\\hline", + "\\end{longtable}", + "\\end{footnotesize}", + "\\end{table}", + sep = "\n" + ) + ) |> + cat() +``` + + +```{r tbl-driejaarlijkse-trends} +#| tbl-cap: Gemiddelde wijziging in veronderstelling van een lineaire trend +#| gebaseerd op driejaarlijkse gegevens. +#| results: "asis" +trends |> + filter(.data$cycle) |> + arrange(.data$klasse, desc(.data$estimate)) |> + transmute( + lijn = tolower(.data$speciesgroup) |> + str_replace_all(" ", "-") |> + str_remove_all("'") |> + sprintf( + fmt = "\\hyperref[sec-jaar-%s]{%s} & %s & %s & %s & %s \\\\", + .data$speciesgroup, + format(.data$klasse, type = "ascii") |> + sprintf(fmt = "\\texttt{%s}"), + .data$interpretatie, + .data$jaarlijks, + .data$looptijd + ) + ) |> + pull(.data$lijn) |> + str_replace_all("%", "\\\\%") |> + str_replace_all("~", "\\\\textasciitilde") |> + paste(collapse = "\n") |> + sprintf( + fmt = paste( + "\\begin{table}", + "\\begin{footnotesize}", + "\\begin{longtable}{p{31mm}ccp{37mm}p{37mm}}", + "\\hline", + "soort & klasse & interpretatie & jaarlijkse wijziging &", + "wijziging over de looptijd \\\\", + "\\hline", + "%s", + "\\hline", + "\\end{longtable}", + "\\end{footnotesize}", + "\\end{table}", + sep = "\n" + ) + ) |> + cat() +``` diff --git a/inst/interactive/vbp_species.qmd b/inst/interactive/vbp_species.qmd new file mode 100644 index 0000000..2496f53 --- /dev/null +++ b/inst/interactive/vbp_species.qmd @@ -0,0 +1,390 @@ +--- +knitr: + opts_chunk: + echo: false + message: false + warning: false + dev: svg +lang: nl-BE +format: + html: + embed-resources: true +--- + +```{r setup} +library(effectclass) +library(git2rdata) +library(INBOtheme) +library(tidyverse) +library(knitr) +conflicted::conflicts_prefer(dplyr::filter, effectclass::class_labels) +opts_chunk$set(dev = "svg", echo = FALSE, message = FALSE) +root <- system.file("results", package = "abvanalysis") +if (interactive()) { + read_vc("meta", root) |> + filter(!.data$composite, .data$status == "converged") |> + slice_sample(n = 1) -> this + this_species <- this$speciesgroup + doi <- "https://doi.org/10.21436/inbor.141312967" +} else { + this_species <- "{{species}}" + doi <- "{{doi}}" +} +threshold <- log(0.75) +trend_tekst <- function(x) { + if (x == "stabiel") { + return("een stabiele toestand. De trend blijft beperkt tot") + } + if (x == "onduidelijk") { + return("een onduidelijke toestand. De geschatte trend bedraagt") + } + paste("een", x, "met") +} +``` + +```{r load-data} +read_vc("meta", root) |> + filter(.data$speciesgroup == this_species) |> + select("analysis", "cycle", "linear", "status_fingerprint", "waic") -> meta +paste0( + "Evolutie van het gemodelleerde gemiddeld aantal waargenomen dieren op een ", + "meetpunt per jaar voor %s tijdens de referentieperiode. ", + "De band geeft het [90%% geloofwaardigheidsinterval]", + "({{url}}/methodologie/voorstelling.html#sec-onzekerheid){target=\"_blank\"}", + " in stappen van 5%%." +) |> + sprintf(as.character(this_species)) -> caption_year_estimate +paste0( + "Evolutie van het gemodelleerde gemiddeld aantal waargenomen dieren op een ", + "meetpunt per driejarige cyclus voor %s tijdens de referentieperiode. ", + "De band geeft het [90%% geloofwaardigheidsinterval]", + "({{url}}/methodologie/voorstelling.html#sec-onzekerheid){target=\"_blank\"}", + " in stappen van 5%%." +) |> + sprintf(as.character(this_species)) -> caption_cycle_estimate +paste0( + "Wijzigingen tussen jaren voor %s. ", + "De band geeft het [90%% geloofwaardigheidsinterval]", + "({{url}}/methodologie/voorstelling.html#sec-onzekerheid){target=\"_blank\"}", + " in stappen van 5%%. ", + "De [referentielijnen]", + "({{url}}/methodologie/voorstelling.html#sec-trendklasse)", + "{target=\"_blank\"} ", + "zorgen voor de opdeling van de wijzigingen in [klassen]", + "({{url}}/methodologie/voorstelling.html#sec-trendklasse){target=\"_blank\"}." +) |> + sprintf(as.character(this_species)) -> caption_year_index +paste0( + "Wijzigingen tussen driejarige cycli voor %s. ", + "De band geeft het [90%% geloofwaardigheidsinterval]", + "({{url}}/methodologie/voorstelling.html#sec-onzekerheid){target=\"_blank\"}", + " in stappen van 5%%. ", + "De [referentielijnen]", + "({{url}}/methodologie/voorstelling.html#sec-trendklasse){target=\"_blank\"}", + " zorgen voor de opdeling van de wijzigingen in [klassen]", + "({{url}}/methodologie/voorstelling.html#sec-trendklasse){target=\"_blank\"}." +) |> + sprintf(as.character(this_species)) -> caption_cycle_index +meta |> + select("cycle", "linear", "waic") |> + pivot_wider(names_from = "linear", values_from = "waic") |> + transmute( + .data$cycle, + interpretatie = (.data$`FALSE` - .data$`TRUE`) |> + cut( + breaks = c(-Inf, -2, 0, Inf), + labels = c("niet-lineair", "mogelijk niet-lineair", "lineair") + ) + ) -> interpretatie +meta |> + filter(!.data$cycle) |> + semi_join(x = read_vc("linear_estimate", root), by = "analysis") |> + tail(-1) |> + nrow() -> looptijd +meta |> + filter(!.data$cycle) |> + semi_join(x = read_vc("linear_trend", root), by = "analysis") |> + transmute( + tekst = classification( + .data$lower_confidence_limit * looptijd, + .data$upper_confidence_limit * looptijd, + threshold = threshold + ), + tekst = class_labels(lang = "nl")[.data$tekst], + jaarlijks = format_ci( + .data$estimate, + lcl = .data$lower_confidence_limit, + sign = TRUE, + ucl = .data$upper_confidence_limit, + percent = TRUE, + link = "log", + change = TRUE + ), + looptijd = format_ci( + looptijd * .data$estimate, + lcl = looptijd * .data$lower_confidence_limit, + ucl = looptijd * .data$upper_confidence_limit, + percent = TRUE, + sign = TRUE, + link = "log", + change = TRUE + ) + ) -> trend_year +meta |> + filter(.data$cycle) |> + semi_join(x = read_vc("linear_trend", root), by = "analysis") |> + transmute( + tekst = classification( + .data$lower_confidence_limit * looptijd / 3, + .data$upper_confidence_limit * looptijd / 3, + threshold = threshold + ), + tekst = class_labels(lang = "nl")[.data$tekst], + jaarlijks = format_ci( + .data$estimate / 3, + lcl = .data$lower_confidence_limit / 3, + sign = TRUE, + ucl = .data$upper_confidence_limit / 3, + percent = TRUE, + link = "log", + change = TRUE + ), + looptijd = format_ci( + looptijd * .data$estimate / 3, + sign = TRUE, + link = "log", + change = TRUE, + lcl = looptijd * .data$lower_confidence_limit / 3, + ucl = looptijd * .data$upper_confidence_limit / 3, + percent = TRUE + ) + ) -> trend_cycle +``` + +Onderstaande grafieken zijn als [interactieve grafieken]({{url}}/soort/{{label}}.html){target="_blank"} beschikbaar in de online versie het [rapport]({{url}}){target="_blank"} over de toestand van de Algemene Broedvogels in Vlaanderen. +In die versie kan je tevens het nodige cijfermateriaal [downloaden]({{url}}/methodologie/reproduceerbaarheid.html#downloadbaar-cijfermateriaal){target="_blank"} om deze figuren te reproduceren. + +## Analyse per jaar + +Op basis van jaarlijkse gegevens zien we gemiddeld `r trend_tekst(trend_year$tekst)` `r trend_year$jaarlijks` per jaar of `r trend_year$looptijd` over de volledige looptijd van het meetnet. +Deze trend is `r interpretatie$interpretatie[!interpretatie$cycle]`. + +```{r fig-year-estimate} +#| fig-cap: !expr caption_year_estimate +meta |> + filter(!.data$cycle) |> + semi_join(x = read_vc("estimate", root), by = "analysis") |> + transmute( + .data$year, + .data$upper_confidence_limit, + display = format_ci( + .data$estimate, + lcl = .data$lower_confidence_limit, + ucl = .data$upper_confidence_limit, + link = "log" + ) |> + sprintf(fmt = "%2$i: %1$s", .data$year), + estimate = exp(.data$estimate), + sd = .data$upper_confidence_limit - .data$lower_confidence_limit, + sd = .data$sd / 2 / qnorm(0.95) + ) -> estimate_year +ggplot(estimate_year, aes(x = year, y = estimate)) + + annotate( + geom = "text", + x = -Inf, + y = -Inf, + label = doi, + hjust = 0, + vjust = 0, + colour = inbo_hoofd, + size = 2 + ) + + stat_fan(aes(link_sd = sd), link = "log") + + scale_y_continuous( + "gemiddeld aantal dieren per meetpunt", + limits = c(0, 1.1 * exp(max(estimate_year$upper_confidence_limit))) + ) + + theme(axis.title.x = element_blank()) + + ggtitle(this_species) +``` + +```{r fig-year-change} +#| warning: false +#| fig-cap: !expr caption_year_index +meta |> + filter(!.data$cycle) |> + semi_join(x = read_vc("index", root), by = "analysis") |> + filter(.data$reference == min(.data$reference)) |> + mutate( + sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / + diff(qnorm(c(0.025, 0.975))), + ) -> nadien +change_breaks(n = 4, extra = c(threshold, -threshold))( + c(nadien$lower_confidence_limit, nadien$upper_confidence_limit) +) -> deze_breaks +ggplot(nadien, aes(x = alternative, y = estimate)) + + annotate( + geom = "text", + x = -Inf, + y = -Inf, + label = doi, + hjust = 0, + vjust = 0, + colour = inbo_hoofd, + size = 2 + ) + + annotate( + geom = "rect", + xmin = -Inf, + xmax = Inf, + ymin = threshold, + ymax = -threshold, + alpha = 0.2, + fill = inbo_grijs + ) + + geom_hline(yintercept = 0) + + stat_fan(aes(link_sd = sd), geom = "rect") + + annotate( + geom = "text", + x = Inf, + y = 0, + hjust = 1, + vjust = -0.1, + label = "toestand in 2007", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = threshold, + hjust = 1, + vjust = 1, + label = "belangrijke afname t.o.v. 2007", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = -threshold, + hjust = 1, + vjust = 0, + label = "belangrijke toename t.o.v. 2007", + colour = inbo_hoofd + ) + + theme(axis.title = element_blank()) + + scale_y_continuous(breaks = deze_breaks, labels = change_labels) + + ggtitle(this_species) +``` + +## Analyse per driejarige cyclus + +Op basis van jaarlijkse gegevens zien we gemiddeld `r trend_tekst(trend_cycle$tekst)` `r trend_cycle$jaarlijks` per jaar of `r trend_cycle$looptijd` over de volledige looptijd van het meetnet. +Deze trend is `r interpretatie$interpretatie[interpretatie$cycle]`. + +```{r fig-cycle-estimate} +#| fig-cap: !expr caption_cycle_estimate +meta |> + filter(.data$cycle) |> + semi_join(x = read_vc("estimate", root), by = "analysis") |> + transmute( + .data$year, + .data$upper_confidence_limit, + estimate = exp(.data$estimate), + sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / + diff(qnorm(c(0.025, 0.975))) + ) -> estimate_cycle +ggplot(estimate_cycle, aes(x = year, y = estimate)) + + annotate( + geom = "text", + x = -Inf, + y = -Inf, + label = doi, + hjust = 0, + vjust = 0, + colour = inbo_hoofd, + size = 2 + ) + + stat_fan(aes(link_sd = sd), link = "log") + + scale_x_continuous( + breaks = estimate_cycle$year, + labels = ~ paste(.x, .x + 2, sep = "-") + ) + + scale_y_continuous( + "gemiddeld aantal dieren per meetpunt", + limits = c(0, 1.1 * exp(max(estimate_cycle$upper_confidence_limit))) + ) + + theme(axis.title.x = element_blank()) + + ggtitle(this_species) +``` + +```{r fig-cycle-change} +#| warning: false +#| fig-cap: !expr caption_cycle_index +meta |> + filter(.data$cycle) |> + semi_join(x = read_vc("index", root), by = "analysis") |> + filter(.data$reference == min(.data$reference)) |> + mutate( + sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / + diff(qnorm(c(0.025, 0.975))), + ) -> nadien +change_breaks(n = 4, extra = c(threshold, -threshold))( + c(nadien$lower_confidence_limit, nadien$upper_confidence_limit) +) -> deze_breaks +ggplot(nadien, aes(x = alternative, y = estimate)) + + annotate( + geom = "text", + x = -Inf, + y = -Inf, + label = doi, + hjust = 0, + vjust = 0, + colour = inbo_hoofd, + size = 2 + ) + + annotate( + geom = "rect", + xmin = -Inf, + xmax = Inf, + ymin = threshold, + ymax = -threshold, + alpha = 0.2, + fill = inbo_grijs + ) + + geom_hline(yintercept = 0) + + stat_fan(aes(link_sd = sd), geom = "rect") + + annotate( + geom = "text", + x = Inf, + y = 0, + hjust = 1, + vjust = -0.1, + label = "toestand in 2007-2009", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = threshold, + hjust = 1, + vjust = 1, + label = "belangrijke afname t.o.v. 2007-2009", + colour = inbo_hoofd + ) + + annotate( + geom = "text", + x = Inf, + y = -threshold, + hjust = 1, + vjust = 0, + label = "belangrijke toename t.o.v. 2007-2009", + colour = inbo_hoofd + ) + + theme(axis.title = element_blank()) + + scale_x_continuous( + breaks = nadien$alternative, + labels = ~ paste(.x, .x + 2, sep = "-") + ) + + scale_y_continuous(breaks = deze_breaks, labels = change_labels) + + ggtitle(this_species) +``` diff --git a/inst/nl_be.dic b/inst/nl_be.dic index 7848b42..1b5af95 100644 --- a/inst/nl_be.dic +++ b/inst/nl_be.dic @@ -7,6 +7,7 @@ INLA Natuur- R-script SHA-1 +Saxicola Soerink Vildaphoto Waarderingkaart @@ -24,4 +25,5 @@ nulwaarnemingen rechteras referentiejaartal rekenbladprogramma +rubicola zestientallig diff --git a/inst/results/composite_index.tsv b/inst/results/composite_index.tsv index f4cfdbd..555cffa 100644 --- a/inst/results/composite_index.tsv +++ b/inst/results/composite_index.tsv @@ -1,1058 +1,1345 @@ analysis reference alternative estimate lower_confidence_limit upper_confidence_limit -15 2007 2010 0.0116 -0.0096 0.0328 -15 2007 2013 -0.0144 -0.0393 0.0104 -15 2007 2016 -0.0209 -0.0468 0.005 -15 2007 2019 -0.0619 -0.0911 -0.0328 -15 2007 2022 -0.085 -0.1202 -0.0498 -15 2010 2013 -0.0261 -0.0475 -0.0046 -15 2010 2016 -0.0325 -0.0562 -0.0088 -15 2010 2019 -0.0735 -0.1019 -0.0451 -15 2010 2022 -0.0966 -0.1316 -0.0616 -15 2013 2016 -0.0064 -0.0281 0.0152 -15 2013 2019 -0.0475 -0.0743 -0.0206 -15 2013 2022 -0.0706 -0.104 -0.0371 -15 2016 2019 -0.0411 -0.0649 -0.0172 -15 2016 2022 -0.0641 -0.0966 -0.0317 -15 2019 2022 -0.0231 -0.0489 0.0027 -16 2007 2008 0.0173 -0.0026 0.0372 -16 2007 2009 0.0126 -0.0113 0.0365 -16 2007 2010 0.0179 -0.0066 0.0424 -16 2007 2011 0.0313 0.005 0.0577 -16 2007 2012 0.0476 0.0205 0.0747 -16 2007 2013 0.0025 -0.0247 0.0298 -16 2007 2014 -0.0355 -0.0635 -0.0075 -16 2007 2015 -0.0133 -0.0416 0.0149 -16 2007 2016 -0.0367 -0.0645 -0.0088 -16 2007 2017 -0.063 -0.0916 -0.0344 -16 2007 2018 -0.0602 -0.0893 -0.0311 -16 2007 2019 -0.0801 -0.1092 -0.0509 -16 2007 2020 -0.088 -0.1182 -0.0578 -16 2007 2021 -0.0913 -0.1216 -0.061 -16 2007 2022 -0.1142 -0.1458 -0.0826 -16 2007 2023 -0.1221 -0.1561 -0.0881 -16 2008 2009 -0.0047 -0.0243 0.0148 -16 2008 2010 6e-04 -0.0218 0.0229 -16 2008 2011 0.014 -0.0096 0.0376 -16 2008 2012 0.0302 0.0051 0.0554 -16 2008 2013 -0.0148 -0.0406 0.011 -16 2008 2014 -0.0528 -0.0788 -0.0268 -16 2008 2015 -0.0307 -0.0571 -0.0042 -16 2008 2016 -0.054 -0.0805 -0.0275 -16 2008 2017 -0.0803 -0.107 -0.0536 -16 2008 2018 -0.0775 -0.1052 -0.0498 -16 2008 2019 -0.0974 -0.1254 -0.0694 -16 2008 2020 -0.1053 -0.1341 -0.0766 -16 2008 2021 -0.1086 -0.1378 -0.0795 -16 2008 2022 -0.1315 -0.1623 -0.1008 -16 2008 2023 -0.1395 -0.1724 -0.1066 -16 2009 2010 0.0053 -0.0142 0.0248 -16 2009 2011 0.0188 -0.0044 0.0419 -16 2009 2012 0.0349 0.0106 0.0593 -16 2009 2013 -0.0101 -0.0359 0.0157 -16 2009 2014 -0.0481 -0.0745 -0.0217 -16 2009 2015 -0.0259 -0.0522 3e-04 -16 2009 2016 -0.0493 -0.0758 -0.0228 -16 2009 2017 -0.0756 -0.1026 -0.0487 -16 2009 2018 -0.0728 -0.1001 -0.0455 -16 2009 2019 -0.0927 -0.1208 -0.0646 -16 2009 2020 -0.1006 -0.1296 -0.0717 -16 2009 2021 -0.1039 -0.1329 -0.0749 -16 2009 2022 -0.1268 -0.1577 -0.0959 -16 2009 2023 -0.1347 -0.168 -0.1015 -16 2010 2011 0.0134 -0.006 0.0328 -16 2010 2012 0.0296 0.0068 0.0525 -16 2010 2013 -0.0154 -0.0393 0.0085 -16 2010 2014 -0.0534 -0.0787 -0.0281 -16 2010 2015 -0.0313 -0.057 -0.0055 -16 2010 2016 -0.0546 -0.0801 -0.0291 -16 2010 2017 -0.0809 -0.1074 -0.0544 -16 2010 2018 -0.0781 -0.1051 -0.0512 -16 2010 2019 -0.098 -0.1253 -0.0708 -16 2010 2020 -0.106 -0.1345 -0.0774 -16 2010 2021 -0.1092 -0.138 -0.0805 -16 2010 2022 -0.1322 -0.1624 -0.1019 -16 2010 2023 -0.1401 -0.1729 -0.1072 -16 2011 2012 0.0162 -0.0036 0.0361 -16 2011 2013 -0.0288 -0.0517 -0.0059 -16 2011 2014 -0.0668 -0.0912 -0.0424 -16 2011 2015 -0.0447 -0.0699 -0.0194 -16 2011 2016 -0.068 -0.0937 -0.0424 -16 2011 2017 -0.0943 -0.1204 -0.0683 -16 2011 2018 -0.0915 -0.1185 -0.0646 -16 2011 2019 -0.1114 -0.1389 -0.0839 -16 2011 2020 -0.1194 -0.1478 -0.0909 -16 2011 2021 -0.1226 -0.1516 -0.0937 -16 2011 2022 -0.1456 -0.1762 -0.1149 -16 2011 2023 -0.1535 -0.1863 -0.1206 -16 2012 2013 -0.045 -0.0648 -0.0252 -16 2012 2014 -0.083 -0.1062 -0.0599 -16 2012 2015 -0.0609 -0.0849 -0.0369 -16 2012 2016 -0.0843 -0.1096 -0.059 -16 2012 2017 -0.1106 -0.1367 -0.0845 -16 2012 2018 -0.1078 -0.1342 -0.0814 -16 2012 2019 -0.1276 -0.1551 -0.1002 -16 2012 2020 -0.1356 -0.1643 -0.1069 -16 2012 2021 -0.1389 -0.1674 -0.1103 -16 2012 2022 -0.1618 -0.1925 -0.1311 -16 2012 2023 -0.1697 -0.2026 -0.1368 -16 2013 2014 -0.038 -0.0579 -0.0181 -16 2013 2015 -0.0159 -0.039 0.0073 -16 2013 2016 -0.0392 -0.0635 -0.015 -16 2013 2017 -0.0655 -0.0915 -0.0396 -16 2013 2018 -0.0628 -0.0894 -0.0361 -16 2013 2019 -0.0826 -0.1097 -0.0556 -16 2013 2020 -0.0905 -0.1193 -0.0618 -16 2013 2021 -0.0938 -0.1228 -0.0648 -16 2013 2022 -0.1168 -0.147 -0.0865 -16 2013 2023 -0.1247 -0.1576 -0.0917 -16 2014 2015 0.0222 0.0021 0.0422 -16 2014 2016 -0.0012 -0.0247 0.0223 -16 2014 2017 -0.0276 -0.0522 -0.0029 -16 2014 2018 -0.0248 -0.051 0.0015 -16 2014 2019 -0.0446 -0.0718 -0.0174 -16 2014 2020 -0.0525 -0.0807 -0.0244 -16 2014 2021 -0.0558 -0.0844 -0.0273 -16 2014 2022 -0.0787 -0.1089 -0.0485 -16 2014 2023 -0.0867 -0.1188 -0.0546 -16 2015 2016 -0.0234 -0.0435 -0.0033 -16 2015 2017 -0.0497 -0.0734 -0.0259 -16 2015 2018 -0.0469 -0.0719 -0.0219 -16 2015 2019 -0.0668 -0.0932 -0.0403 -16 2015 2020 -0.0747 -0.1027 -0.0467 -16 2015 2021 -0.078 -0.1061 -0.0499 -16 2015 2022 -0.1009 -0.131 -0.0708 -16 2015 2023 -0.1088 -0.1413 -0.0764 -16 2016 2017 -0.0263 -0.0461 -0.0066 -16 2016 2018 -0.0235 -0.047 -1e-04 -16 2016 2019 -0.0434 -0.0684 -0.0184 -16 2016 2020 -0.0513 -0.0784 -0.0242 -16 2016 2021 -0.0546 -0.0823 -0.0269 -16 2016 2022 -0.0775 -0.1068 -0.0483 -16 2016 2023 -0.0855 -0.1174 -0.0535 -16 2017 2018 0.0028 -0.0173 0.0228 -16 2017 2019 -0.0171 -0.0412 0.007 -16 2017 2020 -0.025 -0.0509 8e-04 -16 2017 2021 -0.0283 -0.0553 -0.0013 -16 2017 2022 -0.0512 -0.0804 -0.022 -16 2017 2023 -0.0591 -0.0903 -0.0279 -16 2018 2019 -0.0199 -0.0403 6e-04 -16 2018 2020 -0.0278 -0.0525 -0.0031 -16 2018 2021 -0.0311 -0.0568 -0.0054 -16 2018 2022 -0.054 -0.0827 -0.0253 -16 2018 2023 -0.0619 -0.0933 -0.0305 -16 2019 2020 -0.0079 -0.0287 0.0129 -16 2019 2021 -0.0112 -0.0354 0.013 -16 2019 2022 -0.0341 -0.0612 -0.0071 -16 2019 2023 -0.042 -0.0725 -0.0115 -16 2020 2021 -0.0033 -0.024 0.0174 -16 2020 2022 -0.0262 -0.0519 -5e-04 -16 2020 2023 -0.0341 -0.0629 -0.0053 -16 2021 2022 -0.0229 -0.0442 -0.0016 -16 2021 2023 -0.0308 -0.0572 -0.0045 -16 2022 2023 -0.0079 -0.0301 0.0143 -17 2007 2008 -0.0246 -0.072 0.0227 -17 2007 2009 -0.0541 -0.1099 0.0017 -17 2007 2010 -0.058 -0.11 -0.006 -17 2007 2011 -0.0556 -0.113 0.0018 -17 2007 2012 -0.0596 -0.1187 -5e-04 -17 2007 2013 -0.1391 -0.1966 -0.0817 -17 2007 2014 -0.2297 -0.291 -0.1685 -17 2007 2015 -0.1993 -0.2605 -0.1381 -17 2007 2016 -0.2013 -0.2601 -0.1425 -17 2007 2017 -0.2535 -0.3136 -0.1934 -17 2007 2018 -0.2348 -0.2957 -0.1739 -17 2007 2019 -0.2414 -0.3011 -0.1817 -17 2007 2020 -0.2382 -0.3005 -0.1759 -17 2007 2021 -0.2403 -0.3024 -0.1783 -17 2007 2022 -0.35 -0.4158 -0.2841 -17 2007 2023 -0.3432 -0.413 -0.2735 -17 2008 2009 -0.0295 -0.0774 0.0184 -17 2008 2010 -0.0334 -0.0848 0.0181 -17 2008 2011 -0.031 -0.0847 0.0228 -17 2008 2012 -0.035 -0.0928 0.0229 -17 2008 2013 -0.1145 -0.173 -0.056 -17 2008 2014 -0.2051 -0.2644 -0.1459 -17 2008 2015 -0.1747 -0.2344 -0.1149 -17 2008 2016 -0.1767 -0.2347 -0.1186 -17 2008 2017 -0.2289 -0.2857 -0.1721 -17 2008 2018 -0.2101 -0.27 -0.1503 -17 2008 2019 -0.2168 -0.276 -0.1575 -17 2008 2020 -0.2136 -0.2735 -0.1537 -17 2008 2021 -0.2157 -0.2757 -0.1557 -17 2008 2022 -0.3253 -0.3904 -0.2603 -17 2008 2023 -0.3186 -0.3859 -0.2513 -17 2009 2010 -0.0039 -0.0519 0.0441 -17 2009 2011 -0.0015 -0.0572 0.0542 -17 2009 2012 -0.0055 -0.0629 0.0519 -17 2009 2013 -0.085 -0.1448 -0.0252 -17 2009 2014 -0.1757 -0.2375 -0.1138 -17 2009 2015 -0.1452 -0.2052 -0.0852 -17 2009 2016 -0.1472 -0.2067 -0.0877 -17 2009 2017 -0.1994 -0.259 -0.1399 -17 2009 2018 -0.1807 -0.241 -0.1203 -17 2009 2019 -0.1873 -0.2487 -0.1259 -17 2009 2020 -0.1841 -0.2468 -0.1214 -17 2009 2021 -0.1863 -0.2477 -0.1248 -17 2009 2022 -0.2959 -0.3625 -0.2293 -17 2009 2023 -0.2891 -0.3596 -0.2187 -17 2010 2011 0.0024 -0.0448 0.0496 -17 2010 2012 -0.0016 -0.055 0.0518 -17 2010 2013 -0.0811 -0.1341 -0.0282 -17 2010 2014 -0.1718 -0.2292 -0.1144 -17 2010 2015 -0.1413 -0.1989 -0.0837 -17 2010 2016 -0.1433 -0.1987 -0.0879 -17 2010 2017 -0.1955 -0.2524 -0.1387 -17 2010 2018 -0.1768 -0.2345 -0.1191 -17 2010 2019 -0.1834 -0.2404 -0.1264 -17 2010 2020 -0.1802 -0.2403 -0.1202 -17 2010 2021 -0.1824 -0.2413 -0.1234 -17 2010 2022 -0.292 -0.3546 -0.2293 -17 2010 2023 -0.2853 -0.3522 -0.2183 -17 2011 2012 -0.004 -0.0535 0.0455 -17 2011 2013 -0.0835 -0.1379 -0.0291 -17 2011 2014 -0.1741 -0.2315 -0.1168 -17 2011 2015 -0.1437 -0.2027 -0.0847 -17 2011 2016 -0.1457 -0.2038 -0.0876 -17 2011 2017 -0.1979 -0.255 -0.1409 -17 2011 2018 -0.1792 -0.2388 -0.1195 -17 2011 2019 -0.1858 -0.245 -0.1266 -17 2011 2020 -0.1826 -0.2424 -0.1228 -17 2011 2021 -0.1848 -0.245 -0.1245 -17 2011 2022 -0.2944 -0.3591 -0.2297 -17 2011 2023 -0.2876 -0.3553 -0.22 -17 2012 2013 -0.0795 -0.1285 -0.0305 -17 2012 2014 -0.1702 -0.2266 -0.1137 -17 2012 2015 -0.1397 -0.1965 -0.0829 -17 2012 2016 -0.1417 -0.2009 -0.0826 -17 2012 2017 -0.1939 -0.253 -0.1348 -17 2012 2018 -0.1752 -0.2335 -0.1168 -17 2012 2019 -0.1818 -0.2419 -0.1218 -17 2012 2020 -0.1786 -0.2407 -0.1165 -17 2012 2021 -0.1808 -0.2404 -0.1211 -17 2012 2022 -0.2904 -0.3575 -0.2232 -17 2012 2023 -0.2836 -0.3529 -0.2144 -17 2013 2014 -0.0906 -0.1396 -0.0416 -17 2013 2015 -0.0602 -0.1154 -0.0049 -17 2013 2016 -0.0622 -0.118 -0.0064 -17 2013 2017 -0.1144 -0.1725 -0.0563 -17 2013 2018 -0.0956 -0.1544 -0.0369 -17 2013 2019 -0.1023 -0.1606 -0.0439 -17 2013 2020 -0.0991 -0.1612 -0.037 -17 2013 2021 -0.1012 -0.1622 -0.0403 -17 2013 2022 -0.2108 -0.2753 -0.1464 -17 2013 2023 -0.2041 -0.2725 -0.1358 -17 2014 2015 0.0305 -0.0196 0.0805 -17 2014 2016 0.0284 -0.0285 0.0854 -17 2014 2017 -0.0238 -0.0803 0.0327 -17 2014 2018 -0.005 -0.0643 0.0543 -17 2014 2019 -0.0117 -0.072 0.0487 -17 2014 2020 -0.0085 -0.0704 0.0535 -17 2014 2021 -0.0106 -0.0725 0.0513 -17 2014 2022 -0.1202 -0.1867 -0.0538 -17 2014 2023 -0.1135 -0.1813 -0.0457 -17 2015 2016 -0.002 -0.0512 0.0471 -17 2015 2017 -0.0542 -0.1095 0.001 -17 2015 2018 -0.0355 -0.0922 0.0212 -17 2015 2019 -0.0421 -0.1011 0.0168 -17 2015 2020 -0.0389 -0.1011 0.0232 -17 2015 2021 -0.0411 -0.1015 0.0194 -17 2015 2022 -0.1507 -0.217 -0.0844 -17 2015 2023 -0.144 -0.2128 -0.0751 -17 2016 2017 -0.0522 -0.1004 -0.004 -17 2016 2018 -0.0334 -0.0893 0.0224 -17 2016 2019 -0.0401 -0.0958 0.0157 -17 2016 2020 -0.0369 -0.0966 0.0228 -17 2016 2021 -0.039 -0.0993 0.0213 -17 2016 2022 -0.1487 -0.2118 -0.0855 -17 2016 2023 -0.1419 -0.2096 -0.0743 -17 2017 2018 0.0188 -0.0303 0.0678 -17 2017 2019 0.0121 -0.0428 0.067 -17 2017 2020 0.0153 -0.0414 0.0721 -17 2017 2021 0.0132 -0.0457 0.0721 -17 2017 2022 -0.0964 -0.1602 -0.0326 -17 2017 2023 -0.0897 -0.1556 -0.0239 -17 2018 2019 -0.0066 -0.0554 0.0421 -17 2018 2020 -0.0034 -0.0609 0.0541 -17 2018 2021 -0.0056 -0.0623 0.0511 -17 2018 2022 -0.1152 -0.1794 -0.051 -17 2018 2023 -0.1085 -0.1761 -0.0408 -17 2019 2020 0.0032 -0.0473 0.0537 -17 2019 2021 0.0011 -0.0541 0.0563 -17 2019 2022 -0.1086 -0.1696 -0.0476 -17 2019 2023 -0.1018 -0.1681 -0.0356 -17 2020 2021 -0.0021 -0.053 0.0487 -17 2020 2022 -0.1118 -0.1731 -0.0505 -17 2020 2023 -0.105 -0.1698 -0.0403 -17 2021 2022 -0.1096 -0.1631 -0.0561 -17 2021 2023 -0.1029 -0.1647 -0.0411 -17 2022 2023 0.0067 -0.049 0.0624 -18 2007 2010 0.0495 0.035 0.064 -18 2007 2013 0.0249 0.0088 0.0409 -18 2007 2016 -0.0082 -0.0246 0.0081 -18 2007 2019 -0.03 -0.0472 -0.0127 -18 2007 2022 -0.0077 -0.028 0.0125 -18 2010 2013 -0.0246 -0.0396 -0.0097 -18 2010 2016 -0.0577 -0.0736 -0.0419 -18 2010 2019 -0.0795 -0.0964 -0.0626 -18 2010 2022 -0.0572 -0.0772 -0.0372 -18 2013 2016 -0.0331 -0.0479 -0.0183 -18 2013 2019 -0.0548 -0.0712 -0.0385 -18 2013 2022 -0.0326 -0.0521 -0.0131 -18 2016 2019 -0.0217 -0.0362 -0.0073 -18 2016 2022 5e-04 -0.0178 0.0189 -18 2019 2022 0.0222 0.0053 0.0392 -19 2007 2008 0.0606 0.0399 0.0813 -19 2007 2009 0.081 0.0576 0.1043 -19 2007 2010 0.0808 0.059 0.1027 -19 2007 2011 0.0872 0.0632 0.1113 -19 2007 2012 0.1349 0.1096 0.1602 -19 2007 2013 0.0881 0.0645 0.1117 -19 2007 2014 0.0459 0.0208 0.071 -19 2007 2015 0.0873 0.0615 0.113 -19 2007 2016 0.0463 0.0226 0.0701 -19 2007 2017 0.0314 0.0061 0.0567 -19 2007 2018 0.0417 0.0161 0.0673 -19 2007 2019 0.0111 -0.0137 0.0358 -19 2007 2020 0.0168 -0.0096 0.0431 -19 2007 2021 0.0255 -5e-04 0.0515 -19 2007 2022 0.0391 0.0128 0.0653 -19 2007 2023 0.027 -9e-04 0.0548 -19 2008 2009 0.0204 -2e-04 0.041 -19 2008 2010 0.0203 -0.0014 0.0419 -19 2008 2011 0.0267 0.0057 0.0477 -19 2008 2012 0.0743 0.0506 0.0981 -19 2008 2013 0.0275 0.004 0.0511 -19 2008 2014 -0.0147 -0.0373 0.0079 -19 2008 2015 0.0267 0.0024 0.051 -19 2008 2016 -0.0142 -0.0379 0.0094 -19 2008 2017 -0.0292 -0.0519 -0.0065 -19 2008 2018 -0.0189 -0.0431 0.0053 -19 2008 2019 -0.0495 -0.0741 -0.0249 -19 2008 2020 -0.0438 -0.0678 -0.0199 -19 2008 2021 -0.0351 -0.0594 -0.0108 -19 2008 2022 -0.0215 -0.0475 0.0044 -19 2008 2023 -0.0336 -0.0591 -0.0081 -19 2009 2010 -1e-04 -0.0208 0.0205 -19 2009 2011 0.0063 -0.0164 0.029 -19 2009 2012 0.0539 0.0314 0.0765 -19 2009 2013 0.0071 -0.0168 0.031 -19 2009 2014 -0.0351 -0.0594 -0.0108 -19 2009 2015 0.0063 -0.0171 0.0297 -19 2009 2016 -0.0346 -0.0587 -0.0106 -19 2009 2017 -0.0496 -0.074 -0.0252 -19 2009 2018 -0.0393 -0.0625 -0.016 -19 2009 2019 -0.0699 -0.0949 -0.0449 -19 2009 2020 -0.0642 -0.0898 -0.0387 -19 2009 2021 -0.0555 -0.0792 -0.0318 -19 2009 2022 -0.0419 -0.0684 -0.0155 -19 2009 2023 -0.054 -0.0812 -0.0268 -19 2010 2011 0.0064 -0.0142 0.027 -19 2010 2012 0.0541 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-0.0471 0.1352 +14 2012 2022 0.0663 -0.0282 0.1607 +14 2012 2023 0.1023 0.007 0.1976 +14 2012 2024 0.1418 0.0471 0.2364 +14 2012 2025 0.1708 0.0689 0.2727 +14 2013 2014 -0.0114 -0.0777 0.0549 +14 2013 2015 -0.0461 -0.1268 0.0347 +14 2013 2016 0.0017 -0.0792 0.0827 +14 2013 2017 0.0413 -0.045 0.1275 +14 2013 2018 0.066 -0.022 0.154 +14 2013 2019 0.0636 -0.0246 0.1519 +14 2013 2020 0.0639 -0.0272 0.155 +14 2013 2021 0.0495 -0.0407 0.1398 +14 2013 2022 0.0718 -0.0204 0.1639 +14 2013 2023 0.1078 0.0141 0.2015 +14 2013 2024 0.1472 0.0531 0.2414 +14 2013 2025 0.1763 0.0773 0.2752 +14 2014 2015 -0.0347 -0.1028 0.0335 +14 2014 2016 0.0131 -0.0643 0.0906 +14 2014 2017 0.0527 -0.0303 0.1357 +14 2014 2018 0.0774 -0.0098 0.1646 +14 2014 2019 0.075 -0.0138 0.1638 +14 2014 2020 0.0753 -0.015 0.1656 +14 2014 2021 0.0609 -0.0297 0.1516 +14 2014 2022 0.0832 -0.01 0.1763 +14 2014 2023 0.1192 0.0259 0.2124 +14 2014 2024 0.1586 0.0645 0.2528 +14 2014 2025 0.1876 0.0872 0.2881 +14 2015 2016 0.0478 -0.0191 0.1147 +14 2015 2017 0.0874 0.0068 0.1679 +14 2015 2018 0.1121 0.0268 0.1973 +14 2015 2019 0.1097 0.0217 0.1976 +14 2015 2020 0.11 0.0187 0.2013 +14 2015 2021 0.0956 0.0054 0.1857 +14 2015 2022 0.1178 0.0253 0.2104 +14 2015 2023 0.1538 0.0599 0.2477 +14 2015 2024 0.1933 0.0988 0.2878 +14 2015 2025 0.2223 0.122 0.3226 +14 2016 2017 0.0396 -0.0252 0.1043 +14 2016 2018 0.0643 -0.012 0.1405 +14 2016 2019 0.0619 -0.0172 0.1409 +14 2016 2020 0.0622 -0.0227 0.147 +14 2016 2021 0.0478 -0.0374 0.133 +14 2016 2022 0.07 -0.0167 0.1567 +14 2016 2023 0.106 0.0175 0.1946 +14 2016 2024 0.1455 0.0561 0.2348 +14 2016 2025 0.1745 0.0802 0.2688 +14 2017 2018 0.0247 -0.0412 0.0906 +14 2017 2019 0.0223 -0.054 0.0985 +14 2017 2020 0.0226 -0.0594 0.1046 +14 2017 2021 0.0082 -0.076 0.0925 +14 2017 2022 0.0305 -0.0567 0.1176 +14 2017 2023 0.0665 -0.0209 0.1539 +14 2017 2024 0.106 0.0166 0.1953 +14 2017 2025 0.135 0.0387 0.2312 +14 2018 2019 -0.0024 -0.068 0.0632 +14 2018 2020 -0.0021 -0.0799 0.0757 +14 2018 2021 -0.0165 -0.0973 0.0644 +14 2018 2022 0.0058 -0.0797 0.0912 +14 2018 2023 0.0418 -0.0454 0.1289 +14 2018 2024 0.0812 -0.0058 0.1683 +14 2018 2025 0.1103 0.017 0.2035 +14 2019 2020 3e-04 -0.0654 0.066 +14 2019 2021 -0.0141 -0.0898 0.0616 +14 2019 2022 0.0082 -0.0725 0.0888 +14 2019 2023 0.0442 -0.0397 0.128 +14 2019 2024 0.0836 -0.0018 0.1691 +14 2019 2025 0.1127 0.0209 0.2044 +14 2020 2021 -0.0144 -0.0798 0.0511 +14 2020 2022 0.0079 -0.0708 0.0865 +14 2020 2023 0.0439 -0.0383 0.1261 +14 2020 2024 0.0833 -0.0028 0.1695 +14 2020 2025 0.1124 0.0186 0.2061 +14 2021 2022 0.0222 -0.0427 0.0872 +14 2021 2023 0.0582 -0.0175 0.134 +14 2021 2024 0.0977 0.0171 0.1783 +14 2021 2025 0.1267 0.0359 0.2176 +14 2022 2023 0.036 -0.029 0.101 +14 2022 2024 0.0755 -0.0025 0.1535 +14 2022 2025 0.1045 0.0177 0.1913 +14 2023 2024 0.0395 -0.026 0.1049 +14 2023 2025 0.0685 -0.0143 0.1513 +14 2024 2025 0.029 -0.0405 0.0985 diff --git a/inst/results/composite_index.yml b/inst/results/composite_index.yml index b068c8d..45e26da 100644 --- a/inst/results/composite_index.yml +++ b/inst/results/composite_index.yml @@ -1,53 +1,64 @@ ..generic: - git2rdata: 0.4.0 + git2rdata: 0.5.2 optimize: yes NA string: NA sorting: - analysis - reference - alternative - hash: 1b2162ebc5045ee239260ca4517f89a6ff329475 - data_hash: 2a240b6d8c6cf1b9099037134a0b100e7f9a4c31 + hash: 198af67d8d77f4f187de0dedf84bfb67d75af81c + data_hash: 50787c6cd6b2cc07095ca6183514d48f1997361b + name: composite_index + title: Indices by species group analysis: class: factor labels: - - 106c5f05239fe003eb709cc174f2f390906703a4 - - 1080515a46eba7a990f4283dd0732b8de57d3fb9 - - 1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 - - 4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 - - 50e7070c7087e7ce993c59fd71ba17db1963ca57 - - 584e7adc66eed7cfe7822a865eda2c4279265b03 - - 5d6bee1ad54c4e87ed3eb362bc8ad8c3dc2725a7 - - 6714226e0dff29c7e5dbe71a1f43137b5a4f6656 - - 84c6a4d9c834395f41120e6e4df67c82eb9e4e20 - - a28c3e0a86e72290391aeee6d4cd4fbb8189ec5d - - a8c35af09973b4fb822f3bc8d98a9e50e385ccb5 - - b19c23046c219b08c536b0a34b9d51a497c2fa82 - - ea1d7d9655298ce0c62dad29b883c1ca478dae67 - - f5aebd43ff9b6e06d1e1e7ba7ebb028c1ddd091d + - 0c87725e89e54d7a91134e56045c26a3a0c598fe + - 26e0a132b85822bf9d2894f11fad419f3df28ed0 + - 3467e6b072760f472d7fc09898c7e6abe65a56a6 + - 40ec2fbc25dafdd0edf00a1b7f9ac46c709a4ae9 + - 66335ec4972c682dabeffa7aa92e2a3afe5f7270 + - 71aec071018610c8c32b566d0cc13580b96821aa + - 893db398b31d1a3581118644709a39e1f50f4729 + - ad7ecc327c5ca1806d7934a1292c95ee252ec3a4 + - af6a168eced84b18e57938e4b4982d7e13b56802 + - b0b5c25663dfb65a8828b17eae19985f3ea0bfe2 + - cd7d7480035e99435fdf1ae2028358cca9e03144 + - d5e12413e3004018302641cc852f9a4684533daa + - f870a60d43e61b0756b3d484fbe5b19ca53bef48 + - fc4608a9294fe6e005d713b6a15f6bf67604fd06 index: - - 15 - - 16 - - 17 - - 18 - - 19 - - 20 - - 21 - - 22 - - 23 - - 24 - - 25 - - 26 - - 27 - - 28 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 ordered: no + description: Identifier of the analysis reference: class: integer + description: Year or period used as reference alternative: class: integer + description: year or period to compare with the reference estimate: class: numeric + digits: 6 + description: Estimate on the log-scale lower_confidence_limit: class: numeric + digits: 6 + description: 5% quantile of the distribution on the log-scale upper_confidence_limit: class: numeric + digits: 6 + description: 95% quantile of the distribution on the log-scale diff --git a/inst/results/composite_moving_trend.tsv b/inst/results/composite_moving_trend.tsv index ac2815c..53e775d 100644 --- a/inst/results/composite_moving_trend.tsv +++ b/inst/results/composite_moving_trend.tsv @@ -1,197 +1,239 @@ analysis midpoint window estimate lower_confidence_limit upper_confidence_limit -106c5f05239fe003eb709cc174f2f390906703a4 2007.5 2 0.0116 -0.0096 0.0328 -106c5f05239fe003eb709cc174f2f390906703a4 2008.5 2 -0.0261 -0.0475 -0.0046 -106c5f05239fe003eb709cc174f2f390906703a4 2009.5 2 -0.0064 -0.0281 0.0152 -106c5f05239fe003eb709cc174f2f390906703a4 2010.5 2 -0.0411 -0.0649 -0.0172 -106c5f05239fe003eb709cc174f2f390906703a4 2011.5 2 -0.0231 -0.0489 0.0027 -106c5f05239fe003eb709cc174f2f390906703a4 2008.5 4 -0.0089 -0.0174 -3e-04 -106c5f05239fe003eb709cc174f2f390906703a4 2009.5 4 -0.0227 -0.0319 -0.0135 -106c5f05239fe003eb709cc174f2f390906703a4 2010.5 4 -0.0253 -0.0365 -0.0141 -106c5f05239fe003eb709cc174f2f390906703a4 2009.5 6 -0.0186 -0.0253 -0.012 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2009.5 6 0.0081 0.0029 0.0133 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2010.5 6 0.0013 -0.0038 0.0063 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2011.5 6 -0.0077 -0.0129 -0.0026 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2012.5 6 -0.0115 -0.0165 -0.0064 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2013.5 6 -0.016 -0.021 -0.011 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2014.5 6 -0.0185 -0.0236 -0.0134 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2015.5 6 -0.012 -0.0172 -0.0067 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2016.5 6 -0.0111 -0.0164 -0.0058 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2017.5 6 -0.0143 -0.0197 -0.0089 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2018.5 6 -0.0105 -0.016 -0.005 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2019.5 6 -0.0102 -0.0159 -0.0045 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2020.5 6 -0.0119 -0.018 -0.0057 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2012.5 12 -0.0075 -0.0098 -0.0051 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2013.5 12 -0.01 -0.0123 -0.0077 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2014.5 12 -0.0115 -0.0138 -0.0091 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2015.5 12 -0.0125 -0.015 -0.0101 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2016.5 12 -0.0135 -0.0161 -0.011 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2017.5 12 -0.0133 -0.016 -0.0106 -1080515a46eba7a990f4283dd0732b8de57d3fb9 2015 17 -0.0097 -0.0114 -0.0079 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2009.5 6 -0.0113 -0.0223 -3e-04 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2010.5 6 -0.0168 -0.028 -0.0055 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2011.5 6 -0.0322 -0.0436 -0.0207 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2012.5 6 -0.0374 -0.0484 -0.0263 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2013.5 6 -0.0354 -0.0465 -0.0242 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2014.5 6 -0.0322 -0.0434 -0.0209 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2015.5 6 -0.0158 -0.027 -0.0045 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2016.5 6 -0.0062 -0.0176 0.0052 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2017.5 6 -0.0085 -0.02 0.0031 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2018.5 6 -0.0045 -0.0158 0.0069 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2019.5 6 -0.0142 -0.0261 -0.0023 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2020.5 6 -0.0249 -0.0377 -0.012 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2012.5 12 -0.0244 -0.029 -0.0198 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2013.5 12 -0.0231 -0.0277 -0.0186 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2014.5 12 -0.0212 -0.0258 -0.0166 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2015.5 12 -0.0191 -0.0237 -0.0145 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2016.5 12 -0.0202 -0.025 -0.0154 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2017.5 12 -0.0188 -0.024 -0.0137 -1b1eff01fc9bd8c8faf06150d2018cdb467dcbc7 2015 17 -0.0209 -0.024 -0.0177 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2007.5 2 0.0495 0.035 0.064 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2008.5 2 -0.0246 -0.0396 -0.0097 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2009.5 2 -0.0331 -0.0479 -0.0183 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2010.5 2 -0.0217 -0.0362 -0.0073 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2011.5 2 0.0222 0.0053 0.0392 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2008.5 4 -0.0049 -0.0103 4e-04 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2009.5 4 -0.0271 -0.0327 -0.0216 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2010.5 4 -0.0119 -0.0182 -0.0057 -4c7f626c0ac1dc092c72c1a5b5fb909734fe0c83 2009.5 6 -0.0089 -0.0125 -0.0052 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2009.5 6 0.0216 0.0171 0.026 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2010.5 6 0.0087 0.0044 0.013 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2011.5 6 -0.003 -0.0074 0.0013 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2012.5 6 -0.004 -0.0083 4e-04 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2013.5 6 -0.0111 -0.0155 -0.0068 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2014.5 6 -0.0172 -0.0216 -0.0128 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2015.5 6 -0.009 -0.0134 -0.0047 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2016.5 6 -0.0093 -0.0137 -0.0049 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2017.5 6 -0.0128 -0.0172 -0.0084 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2018.5 6 -0.0051 -0.0094 -9e-04 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2019.5 6 -1e-04 -0.0045 0.0043 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2020.5 6 5e-04 -0.0041 0.0052 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2012.5 12 -6e-04 -0.0024 0.0011 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2013.5 12 -0.0057 -0.0075 -0.0039 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2014.5 12 -0.0079 -0.0097 -0.0061 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2015.5 12 -0.0085 -0.0103 -0.0067 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2016.5 12 -0.0082 -0.01 -0.0063 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2017.5 12 -0.0078 -0.0097 -0.0058 -50e7070c7087e7ce993c59fd71ba17db1963ca57 2015 17 -0.0032 -0.0044 -0.0019 -584e7adc66eed7cfe7822a865eda2c4279265b03 2009.5 6 0.0071 8e-04 0.0134 -584e7adc66eed7cfe7822a865eda2c4279265b03 2010.5 6 -0.0011 -0.0072 0.005 -584e7adc66eed7cfe7822a865eda2c4279265b03 2011.5 6 -0.0065 -0.0125 -4e-04 -584e7adc66eed7cfe7822a865eda2c4279265b03 2012.5 6 -0.0032 -0.0093 0.0028 -584e7adc66eed7cfe7822a865eda2c4279265b03 2013.5 6 -0.0053 -0.0113 6e-04 -584e7adc66eed7cfe7822a865eda2c4279265b03 2014.5 6 -0.0057 -0.0117 2e-04 -584e7adc66eed7cfe7822a865eda2c4279265b03 2015.5 6 -0.0011 -0.0072 0.0049 -584e7adc66eed7cfe7822a865eda2c4279265b03 2016.5 6 -0.0049 -0.0112 0.0013 -584e7adc66eed7cfe7822a865eda2c4279265b03 2017.5 6 -0.0122 -0.0186 -0.0057 -584e7adc66eed7cfe7822a865eda2c4279265b03 2018.5 6 -0.0131 -0.0197 -0.0064 -584e7adc66eed7cfe7822a865eda2c4279265b03 2019.5 6 -0.014 -0.021 -0.007 -584e7adc66eed7cfe7822a865eda2c4279265b03 2020.5 6 -0.0146 -0.0221 -0.0072 -584e7adc66eed7cfe7822a865eda2c4279265b03 2012.5 12 -0.0017 -0.0046 0.0011 -584e7adc66eed7cfe7822a865eda2c4279265b03 2013.5 12 -0.0043 -0.0071 -0.0015 -584e7adc66eed7cfe7822a865eda2c4279265b03 2014.5 12 -0.0054 -0.0083 -0.0025 -584e7adc66eed7cfe7822a865eda2c4279265b03 2015.5 12 -0.0069 -0.0099 -0.0039 -584e7adc66eed7cfe7822a865eda2c4279265b03 2016.5 12 -0.0086 -0.0118 -0.0054 -584e7adc66eed7cfe7822a865eda2c4279265b03 2017.5 12 -0.0096 -0.0131 -0.0062 -584e7adc66eed7cfe7822a865eda2c4279265b03 2015 17 -0.0059 -0.0081 -0.0036 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+fc4608a9294fe6e005d713b6a15f6bf67604fd06 2009.5 6 -0.0024 -0.0221 0.0174 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2010.5 6 0.001 -0.0173 0.0193 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2011.5 6 0.0018 -0.0165 0.0201 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2012.5 6 -0.0057 -0.0241 0.0126 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2013.5 6 -0.0035 -0.0216 0.0146 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2014.5 6 0.0043 -0.0133 0.0218 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2015.5 6 0.0153 -0.0021 0.0328 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2016.5 6 0.0214 0.0039 0.039 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2017.5 6 0.0217 0.0042 0.0393 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2018.5 6 0.0087 -0.0082 0.0256 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2019.5 6 0.003 -0.0141 0.02 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2020.5 6 0.0063 -0.0108 0.0233 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2021.5 6 0.0163 -6e-04 0.0333 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2022.5 6 0.0255 0.0075 0.0435 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2012.5 12 0.0028 -0.0058 0.0113 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2013.5 12 0.0056 -0.0026 0.0138 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2014.5 12 0.0077 -4e-04 0.0158 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2015.5 12 0.0078 -2e-04 0.0158 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2016.5 12 0.0086 6e-04 0.0166 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2017.5 12 0.0103 0.0024 0.0183 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2018.5 12 0.0134 0.0054 0.0213 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2019.5 12 0.0163 0.0082 0.0243 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 2016 19 0.0086 0.0036 0.0135 diff --git a/inst/results/composite_moving_trend.yml b/inst/results/composite_moving_trend.yml index 35d7285..2652a19 100644 --- a/inst/results/composite_moving_trend.yml +++ b/inst/results/composite_moving_trend.yml @@ -1,22 +1,34 @@ ..generic: - git2rdata: 0.4.0 + git2rdata: 0.5.2 optimize: yes NA string: NA sorting: - analysis - window - midpoint - hash: fc6fc8d047d84d09dc7220cce78913c1223aac6b - data_hash: 1987cb2a147c301db56b080123149fb988faab0a + hash: 5f915a50ce7b55fcb7d07d3289628dab524f940e + data_hash: ef879ece23973da0a45f44ad126fd68c70c6b39c + name: composite_moving_trend + title: Moving trend for the composite indices analysis: class: character + description: Identifier of the analysis midpoint: class: numeric + digits: 6 + description: Year in the middle of the interval window: class: integer + description: Width of the interval as a number of years estimate: class: numeric + digits: 6 + description: Estimate on the log-scale lower_confidence_limit: class: numeric + digits: 6 + description: 5% quantile of the distribution on the log-scale upper_confidence_limit: class: numeric + digits: 6 + description: 95% quantile of the distribution on the log-scale diff --git a/inst/results/effort.tsv b/inst/results/effort.tsv index 12a0395..6d1881f 100644 --- a/inst/results/effort.tsv +++ b/inst/results/effort.tsv @@ -2,35 +2,47 @@ year location stratum visits 2010 5146 1 18 2013 5146 1 18 2016 5146 1 18 +2025 5148 1 18 2009 5149 1 18 +2025 5149 1 18 2009 5150 1 18 +2024 5150 1 18 +2025 5150 1 18 2009 5151 1 18 +2025 5151 1 18 2007 5152 1 18 2010 5152 1 18 +2024 5152 1 12 2008 5155 1 18 2013 5156 1 6 2016 5156 1 12 2019 5156 1 18 2022 5156 1 18 +2025 5156 1 12 2010 5163 1 12 2013 5163 1 6 2018 5173 1 18 2015 5205 1 18 2018 5205 1 18 2021 5205 1 18 +2024 5205 1 18 2009 5206 1 6 2015 5206 1 12 2018 5206 1 18 2021 5206 1 18 +2024 5206 1 12 +2025 5206 1 18 2009 5207 1 18 2012 5207 1 6 2015 5207 1 18 2018 5207 1 18 +2025 5207 1 18 2008 5208 1 18 2011 5208 1 6 2014 5208 1 18 2017 5208 1 18 2020 5208 1 6 +2025 5208 1 18 2009 5209 1 12 2007 5211 1 18 2010 5211 1 18 @@ -38,25 +50,31 @@ year location stratum visits 2016 5211 1 18 2019 5211 1 18 2022 5211 1 18 +2025 5211 1 18 2007 5212 1 18 2010 5212 1 18 2013 5212 1 18 2016 5212 1 18 2019 5212 1 18 +2025 5212 1 18 2009 5220 1 18 2015 5220 1 18 2018 5220 1 18 2021 5220 1 12 +2025 5220 1 18 2009 5221 1 18 2015 5221 1 18 2018 5221 1 18 2021 5221 1 12 +2025 5221 1 18 2009 5223 1 18 2018 5223 1 18 2021 5223 1 6 +2025 5223 1 18 2009 5225 1 18 2018 5225 1 18 2021 5225 1 18 +2024 5225 1 18 2008 5226 1 12 2011 5226 1 18 2017 5226 1 18 @@ -78,48 +96,62 @@ year location stratum visits 2016 5232 1 18 2019 5232 1 18 2022 5232 1 12 +2025 5232 1 18 2009 5233 1 18 2012 5233 1 16 2015 5233 1 18 2018 5233 1 18 2021 5233 1 18 +2024 5233 1 12 +2025 5233 1 18 2008 5234 1 18 2011 5234 1 18 2014 5234 1 18 2017 5234 1 18 2020 5234 1 18 2023 5234 1 18 +2025 5234 1 6 2010 5235 1 18 2013 5235 1 18 2016 5235 1 18 2019 5235 1 18 2022 5235 1 12 +2025 5235 1 18 2009 5236 1 18 2012 5236 1 18 2015 5236 1 18 2018 5236 1 18 2021 5236 1 18 +2024 5236 1 12 +2025 5236 1 18 2007 5237 1 18 2010 5237 1 18 2013 5237 1 18 +2025 5237 1 18 2007 5238 1 12 2010 5238 1 18 2013 5238 1 18 +2025 5238 1 18 2007 5239 1 18 2011 5239 1 18 2014 5239 1 18 2015 5239 1 6 +2025 5239 1 6 2009 5240 1 18 2012 5240 1 12 +2024 5240 1 18 2009 5241 1 18 2012 5241 1 18 2015 5241 1 12 +2025 5241 1 12 2009 5242 1 18 2012 5242 1 18 +2024 5242 1 18 2007 5244 1 18 2009 5244 1 18 2012 5244 1 18 2015 5244 1 6 +2025 5244 1 18 2008 5248 1 6 2011 5248 1 12 2014 5248 1 18 @@ -128,14 +160,17 @@ year location stratum visits 2013 5283 1 12 2016 5283 1 18 2019 5283 1 18 -2022 5283 1 24 +2022 5283 1 18 +2025 5283 1 18 2009 5284 1 18 2012 5284 1 18 2015 5284 1 18 2018 5284 1 18 2021 5284 1 18 +2024 5284 1 18 2007 5318 1 18 2010 5318 1 12 +2025 5318 1 18 2009 5345 1 6 2014 5345 1 18 2017 5345 1 18 @@ -146,6 +181,7 @@ year location stratum visits 2016 5349 1 18 2019 5349 1 18 2022 5349 1 18 +2025 5349 1 18 2008 5351 1 18 2011 5351 1 18 2017 5351 1 18 @@ -156,18 +192,22 @@ year location stratum visits 2011 5352 1 18 2017 5352 1 18 2020 5352 1 18 +2025 5352 1 18 2008 5353 1 18 2011 5353 1 18 2014 5353 1 18 2017 5353 1 18 2020 5353 1 18 +2025 5353 1 18 2009 5354 1 18 2012 5354 1 18 2015 5354 1 18 2018 5354 1 18 2021 5354 1 18 +2025 5354 1 18 2018 5358 1 18 2021 5358 1 18 +2024 5358 1 12 2008 5362 1 18 2011 5362 1 18 2008 5363 1 18 @@ -177,12 +217,15 @@ year location stratum visits 2014 5364 1 12 2017 5364 1 12 2020 5364 1 16 +2024 5364 1 16 2007 5432 1 18 +2025 5432 1 18 2007 5436 1 6 2008 5439 1 15 2011 5439 1 15 2014 5439 1 15 2017 5439 1 15 +2025 5439 1 17 2009 5440 1 18 2012 5440 1 6 2015 5440 1 18 @@ -192,11 +235,13 @@ year location stratum visits 2013 5441 1 18 2016 5441 1 18 2019 5441 1 18 +2025 5441 1 18 2007 5442 1 18 2010 5442 1 12 2013 5442 1 18 2016 5442 1 18 2019 5442 1 18 +2025 5442 1 18 2008 5444 1 18 2011 5444 1 18 2014 5444 1 18 @@ -204,16 +249,22 @@ year location stratum visits 2020 5444 1 18 2009 5447 1 18 2012 5447 1 18 +2025 5447 1 18 2009 5451 1 18 2012 5451 1 18 +2025 5451 1 18 2007 5454 1 18 +2025 5454 1 18 2009 5455 1 18 2012 5455 1 18 +2025 5455 1 18 2009 5462 1 6 2009 5498 1 18 2012 5498 1 6 +2025 5498 1 18 2009 5500 1 18 2012 5500 1 6 +2025 5500 1 18 2008 5502 1 18 2011 5502 1 18 2015 5502 1 17 @@ -222,23 +273,31 @@ year location stratum visits 2023 5502 1 18 2009 5519 1 18 2015 5519 1 17 +2025 5519 1 18 2009 5564 1 12 2009 5591 1 18 2009 5592 1 18 +2025 5592 1 18 2009 5593 1 18 +2025 5593 1 18 +2025 5597 1 18 2008 5598 1 18 2011 5598 1 18 2008 5600 1 18 2011 5600 1 18 +2025 5600 1 18 2008 5601 1 18 2011 5601 1 18 2009 5602 1 18 +2025 5602 1 18 2009 5604 1 18 +2025 5604 1 18 2009 5605 1 18 2013 5605 1 18 2015 5605 1 18 2018 5605 1 18 2021 5605 1 18 +2025 5605 1 18 2009 5611 1 18 2012 5611 1 18 2007 5613 1 18 @@ -257,19 +316,24 @@ year location stratum visits 2013 5639 1 18 2016 5639 1 18 2019 5639 1 24 +2025 5639 1 6 2009 5644 1 18 2009 5654 1 18 2007 5666 1 18 +2025 5666 1 18 2010 5667 1 18 2011 5667 1 18 2013 5667 1 18 +2025 5667 1 18 2007 5668 1 18 +2025 5668 1 18 2007 5671 1 18 2010 5671 1 18 2013 5671 1 18 2016 5671 1 18 2019 5671 1 18 2022 5671 1 18 +2025 5671 1 17 2017 5673 1 18 2020 5673 1 24 2023 5673 1 18 @@ -283,6 +347,7 @@ year location stratum visits 2023 5723 1 18 2011 5725 1 12 2014 5725 1 12 +2025 5725 1 15 2010 5738 1 18 2013 5738 1 18 2016 5738 1 18 @@ -291,14 +356,18 @@ year location stratum visits 2007 5786 1 18 2008 5788 1 18 2009 5788 1 18 +2025 5788 1 18 2008 5789 1 18 2011 5789 1 18 +2025 5789 1 18 2008 5791 1 18 2011 5791 1 18 +2025 5791 1 18 2009 5840 1 18 2012 5840 1 18 2018 5840 1 18 2021 5840 1 6 +2024 5840 1 18 2007 5851 1 18 2008 5851 1 18 2009 5851 1 18 @@ -313,18 +382,21 @@ year location stratum visits 2009 5881 1 18 2012 5881 1 18 2016 5881 1 18 +2025 5881 1 18 2007 5886 1 18 2010 5886 1 18 2015 5886 1 18 2016 5886 1 15 2019 5886 1 18 2022 5886 1 24 +2025 5886 1 18 2007 5887 1 18 2010 5887 1 18 2015 5887 1 18 2016 5887 1 18 2019 5887 1 18 2022 5887 1 30 +2025 5887 1 18 2023 5898 1 18 2007 5906 1 18 2010 5906 1 18 @@ -332,15 +404,20 @@ year location stratum visits 2016 5906 1 18 2019 5906 1 18 2022 5906 1 18 +2025 5906 1 18 2007 5950 1 18 2010 5950 1 18 2013 5950 1 18 +2025 5950 1 18 2008 5953 1 18 2011 5953 1 18 +2025 5953 1 18 2018 5960 1 18 2021 5960 1 18 +2024 5960 1 18 2017 5961 1 18 2020 5961 1 18 +2023 5961 1 18 2007 5962 1 18 2008 5962 1 18 2010 5962 1 18 @@ -352,6 +429,7 @@ year location stratum visits 2016 5962 1 18 2019 5962 1 18 2022 5962 1 18 +2025 5962 1 18 2007 5967 1 18 2010 5967 1 18 2007 5968 1 18 @@ -369,6 +447,7 @@ year location stratum visits 2015 5971 1 6 2016 5971 1 18 2022 5971 1 18 +2025 5971 1 12 2007 5973 1 12 2010 5973 1 12 2008 5974 1 18 @@ -382,6 +461,7 @@ year location stratum visits 2016 5976 1 18 2018 5976 1 18 2021 5976 1 18 +2024 5976 1 18 2008 5978 1 18 2011 5978 1 18 2014 5978 1 18 @@ -425,15 +505,18 @@ year location stratum visits 2012 6018 1 12 2018 6018 1 18 2021 6018 1 18 +2024 6018 1 18 2009 6020 1 18 2012 6020 1 12 2018 6020 1 18 2021 6020 1 18 +2024 6020 1 18 2008 6021 1 12 2011 6021 1 18 2021 6021 1 18 2019 6023 1 18 2022 6023 1 18 +2025 6023 1 18 2007 6024 1 18 2008 6024 1 18 2009 6024 1 18 @@ -446,6 +529,7 @@ year location stratum visits 2016 6024 1 18 2019 6024 1 18 2022 6024 1 18 +2025 6024 1 18 2007 6025 1 18 2008 6025 1 18 2009 6025 1 18 @@ -458,14 +542,17 @@ year location stratum visits 2016 6025 1 18 2019 6025 1 18 2022 6025 1 18 +2025 6025 1 6 2007 6027 1 18 2008 6027 1 18 -2009 6027 1 12 +2009 6027 1 18 2010 6027 1 18 2011 6027 1 18 2012 6027 1 18 2016 6027 1 18 2019 6027 1 18 +2022 6027 1 18 +2025 6027 1 18 2007 6031 1 18 2009 6034 1 18 2015 6034 1 12 @@ -479,6 +566,8 @@ year location stratum visits 2016 6072 1 18 2019 6072 1 18 2022 6072 1 18 +2024 6072 1 18 +2025 6072 1 18 2007 6126 1 18 2008 6136 1 18 2011 6136 1 18 @@ -506,6 +595,7 @@ year location stratum visits 2008 6151 1 18 2011 6151 1 12 2017 6151 1 18 +2025 6151 1 18 2011 6165 1 18 2014 6165 1 18 2017 6165 1 18 @@ -516,6 +606,7 @@ year location stratum visits 2016 6166 1 18 2019 6166 1 18 2022 6166 1 18 +2025 6166 1 18 2007 6169 1 18 2008 6169 1 18 2009 6169 1 18 @@ -524,25 +615,30 @@ year location stratum visits 2015 6169 1 18 2018 6169 1 18 2021 6169 1 18 +2024 6169 1 18 2007 6181 1 18 2010 6181 1 18 2013 6181 1 18 2016 6181 1 12 2019 6181 1 18 2022 6181 1 18 +2025 6181 1 18 2008 6182 1 18 2011 6182 1 18 2014 6182 1 18 2018 6182 1 18 2021 6182 1 18 +2024 6182 1 18 2018 6185 1 18 2021 6185 1 18 +2024 6185 1 18 2011 6187 1 18 2012 6187 1 18 2014 6187 1 6 2016 6188 1 18 2019 6188 1 18 2022 6188 1 18 +2025 6188 1 18 2009 6192 1 18 2012 6192 1 18 2015 6192 1 18 @@ -565,11 +661,13 @@ year location stratum visits 2016 6200 1 18 2019 6200 1 18 2022 6200 1 18 +2025 6200 1 18 2009 6202 1 12 2012 6202 1 15 2015 6202 1 15 2018 6202 1 15 2021 6202 1 8 +2024 6202 1 18 2008 6203 1 18 2011 6203 1 18 2014 6203 1 18 @@ -584,19 +682,23 @@ year location stratum visits 2007 6249 1 6 2019 6249 1 18 2022 6249 1 18 +2025 6249 1 18 2019 6250 1 18 2022 6250 1 18 +2025 6250 1 18 2017 6251 1 18 2020 6251 1 18 2023 6251 1 18 2018 6252 1 18 2021 6252 1 18 +2024 6252 1 18 2007 6253 1 5 2017 6253 1 18 2020 6253 1 18 2023 6253 1 18 2018 6254 1 18 2021 6254 1 18 +2024 6254 1 18 2009 6255 1 18 2007 6256 1 12 2007 6257 1 18 @@ -613,19 +715,23 @@ year location stratum visits 2016 6260 1 18 2018 6265 1 24 2021 6265 1 12 +2024 6265 1 18 2010 6272 1 18 2013 6272 1 12 +2025 6272 1 18 2008 6273 1 18 2014 6273 1 6 2017 6273 1 18 2020 6273 1 6 2023 6273 1 12 2007 6306 1 18 -2019 6306 1 24 +2019 6306 1 18 2022 6306 1 18 +2025 6306 1 18 2009 6310 1 18 2012 6310 1 18 2015 6310 1 18 +2025 6310 1 18 2008 6316 1 18 2017 6316 1 15 2020 6316 1 18 @@ -651,11 +757,14 @@ year location stratum visits 2009 6339 1 18 2012 6339 1 18 2015 6339 1 18 +2025 6339 1 6 2014 5433 2 18 2017 5433 2 18 2020 5433 2 12 2021 5433 2 6 +2025 5434 2 18 2007 5435 2 12 +2025 5435 2 16 2009 5437 2 15 2012 5437 2 12 2015 5437 2 18 @@ -664,8 +773,10 @@ year location stratum visits 2011 5438 2 18 2014 5438 2 18 2017 5438 2 18 +2025 5438 2 18 2010 5479 2 5 2013 5479 2 16 +2025 5479 2 18 2008 5480 2 18 2014 5480 2 18 2017 5480 2 18 @@ -696,15 +807,18 @@ year location stratum visits 2007 5636 2 18 2010 5636 2 12 2014 5636 2 18 +2025 5636 2 18 2007 5638 2 18 2010 5638 2 12 2012 5638 2 12 2015 5638 2 18 2018 5638 2 18 2021 5638 2 18 +2024 5638 2 18 2007 5640 2 18 2010 5640 2 12 2014 5640 2 18 +2025 5640 2 18 2009 5641 2 18 2012 5641 2 10 2015 5641 2 18 @@ -721,58 +835,81 @@ year location stratum visits 2016 5643 2 18 2019 5643 2 18 2022 5643 2 18 +2025 5643 2 6 2007 5645 2 18 2010 5645 2 12 2013 5645 2 18 +2025 5645 2 18 2008 5646 2 18 2011 5646 2 18 2014 5646 2 12 2017 5646 2 18 +2025 5646 2 18 2007 5647 2 18 2010 5647 2 18 2013 5647 2 18 2016 5647 2 18 +2025 5647 2 18 2007 5664 2 18 +2025 5664 2 18 2007 5665 2 18 2016 5665 2 18 2019 5665 2 18 2022 5665 2 18 +2025 5665 2 18 2008 5766 2 6 2017 5766 2 18 +2025 5766 2 18 2007 5767 2 18 +2025 5767 2 17 2008 5769 2 17 2017 5769 2 18 +2025 5769 2 18 2008 5770 2 9 2012 5770 2 12 2017 5770 2 9 +2025 5770 2 18 2008 5792 2 18 2011 5792 2 18 +2025 5792 2 18 2008 5793 2 18 2011 5793 2 18 +2025 5793 2 18 2007 5934 2 12 2010 5934 2 18 2013 5934 2 18 2016 5934 2 18 2019 5934 2 18 2022 5934 2 18 +2025 5934 2 18 2009 5937 2 18 2021 5937 2 18 +2024 5937 2 18 2021 5938 2 18 +2024 5938 2 18 2008 5939 2 18 2020 5939 2 11 +2024 5939 2 16 2008 5940 2 18 2020 5940 2 12 2023 5940 2 18 2007 5941 2 18 2019 5941 2 18 +2024 5941 2 6 +2025 5941 2 18 2007 5942 2 6 2019 5942 2 12 +2024 5942 2 12 2021 5943 2 18 +2024 5943 2 18 2021 5946 2 17 +2025 5946 2 18 2007 5947 2 18 2019 5947 2 18 +2025 5947 2 18 2008 5948 2 15 2017 5948 2 15 +2025 5948 2 18 2007 5988 2 12 2016 5988 2 18 2008 5989 2 12 @@ -799,31 +936,37 @@ year location stratum visits 2016 6007 2 15 2019 6007 2 15 2022 6007 2 21 +2025 6007 2 15 2009 6008 2 18 2012 6008 2 18 2018 6008 2 18 2021 6008 2 18 +2024 6008 2 18 2010 6010 2 18 2013 6010 2 18 2016 6010 2 18 2019 6010 2 18 2022 6010 2 18 +2025 6010 2 18 2010 6120 2 18 2013 6120 2 18 2016 6120 2 18 2019 6120 2 18 2022 6120 2 18 +2025 6120 2 18 2012 6121 2 18 2015 6121 2 18 2018 6121 2 18 2021 6121 2 18 2022 6121 2 6 +2024 6121 2 18 2007 6122 2 18 2010 6122 2 18 2013 6122 2 18 2016 6122 2 18 2019 6122 2 18 2022 6122 2 18 +2025 6122 2 18 2011 6124 2 15 2014 6124 2 15 2017 6124 2 15 @@ -840,10 +983,12 @@ year location stratum visits 2016 6132 2 18 2019 6132 2 18 2022 6132 2 24 +2025 6132 2 18 2012 6134 2 18 2015 6134 2 18 2018 6134 2 18 2021 6134 2 18 +2024 6134 2 18 2007 6139 2 12 2010 6139 2 6 2016 6139 2 18 @@ -861,6 +1006,7 @@ year location stratum visits 2015 6170 2 18 2018 6170 2 18 2021 6170 2 18 +2024 6170 2 18 2007 6171 2 18 2008 6171 2 18 2009 6171 2 18 @@ -878,6 +1024,7 @@ year location stratum visits 2015 6173 2 18 2018 6173 2 18 2021 6173 2 18 +2024 6173 2 18 2007 6174 2 18 2008 6174 2 18 2009 6174 2 18 @@ -886,6 +1033,7 @@ year location stratum visits 2016 6174 2 18 2019 6174 2 18 2022 6174 2 18 +2025 6174 2 18 2007 6189 2 18 2010 6189 2 18 2014 6189 2 18 @@ -904,6 +1052,7 @@ year location stratum visits 2016 6196 2 18 2019 6196 2 18 2022 6196 2 18 +2025 6196 2 18 2011 6197 2 18 2014 6197 2 18 2017 6197 2 18 @@ -913,6 +1062,7 @@ year location stratum visits 2015 6199 2 18 2018 6199 2 18 2021 6199 2 18 +2024 6199 2 18 2008 6246 2 18 2011 6246 2 6 2014 6246 2 12 @@ -921,11 +1071,13 @@ year location stratum visits 2023 6246 2 18 2019 6263 2 18 2022 6263 2 18 +2025 6263 2 18 2007 6267 2 18 2010 6267 2 18 2013 6267 2 18 2019 6267 2 18 2022 6267 2 18 +2025 6267 2 6 2007 6288 2 18 2010 6288 2 18 2013 6288 2 18 @@ -937,6 +1089,7 @@ year location stratum visits 2015 6289 2 18 2018 6289 2 12 2021 6289 2 6 +2024 6289 2 18 2007 6290 2 18 2010 6290 2 12 2013 6290 2 18 @@ -945,6 +1098,7 @@ year location stratum visits 2012 6291 2 18 2016 6291 2 12 2019 6291 2 18 +2024 6291 2 18 2009 6292 2 18 2012 6292 2 12 2016 6292 2 18 @@ -962,13 +1116,16 @@ year location stratum visits 2008 6304 2 12 2011 6304 2 18 2017 6304 2 18 +2025 6304 2 6 2007 6307 2 18 2017 6307 2 18 +2025 6307 2 18 2007 6308 2 18 2010 6308 2 18 2013 6308 2 18 2016 6308 2 6 2022 6308 2 18 +2025 6308 2 18 2007 6309 2 18 2008 6309 2 6 2010 6309 2 18 @@ -976,18 +1133,22 @@ year location stratum visits 2013 6309 2 18 2016 6309 2 18 2019 6309 2 108 +2025 6309 2 18 2009 6311 2 12 2018 6311 2 18 2021 6311 2 18 +2024 6311 2 18 2007 6312 2 18 2018 6312 2 18 2021 6312 2 18 +2024 6312 2 18 2007 6313 2 12 2010 6313 2 12 2013 6313 2 12 2016 6313 2 12 2019 6313 2 14 2023 6313 2 18 +2025 6313 2 18 2007 6317 2 18 2011 6320 2 18 2017 6320 2 18 @@ -996,18 +1157,23 @@ year location stratum visits 2016 6324 2 12 2019 6324 2 18 2022 6324 2 24 +2025 6324 2 18 2007 5145 3 18 2010 5145 3 18 2013 5145 3 18 2016 5145 3 18 +2025 5145 3 9 2008 5153 3 18 2018 5158 3 18 2021 5158 3 18 +2024 5158 3 18 2018 5159 3 18 +2024 5159 3 18 2008 5160 3 18 2016 5160 3 18 2019 5160 3 18 2022 5160 3 18 +2025 5160 3 18 2008 5161 3 18 2017 5161 3 18 2023 5161 3 18 @@ -1018,33 +1184,43 @@ year location stratum visits 2020 5165 3 12 2021 5165 3 6 2023 5165 3 18 +2025 5168 3 18 2009 5171 3 18 2012 5171 3 18 +2025 5171 3 18 2007 5175 3 18 2010 5175 3 12 +2025 5175 3 18 2007 5176 3 18 2010 5176 3 12 +2025 5176 3 18 2008 5177 3 18 2011 5177 3 18 +2025 5177 3 18 2008 5178 3 18 2011 5178 3 18 +2025 5178 3 18 2019 5181 3 6 +2025 5181 3 18 2009 5182 3 18 2012 5182 3 18 2015 5182 3 18 2018 5182 3 18 2021 5182 3 18 +2024 5182 3 18 2009 5183 3 18 2012 5183 3 18 2015 5183 3 18 2018 5183 3 18 2021 5183 3 18 +2024 5183 3 18 2007 5184 3 18 2010 5184 3 18 2013 5184 3 18 2016 5184 3 18 2019 5184 3 18 2022 5184 3 18 +2025 5184 3 18 2008 5185 3 18 2011 5185 3 18 2014 5185 3 18 @@ -1062,10 +1238,12 @@ year location stratum visits 2015 5187 3 18 2018 5187 3 6 2021 5187 3 6 +2024 5187 3 6 2009 5188 3 18 2012 5188 3 18 2015 5188 3 18 2018 5188 3 6 +2024 5188 3 12 2009 5189 3 18 2012 5189 3 18 2015 5189 3 18 @@ -1084,11 +1262,14 @@ year location stratum visits 2010 5192 3 18 2013 5192 3 18 2016 5192 3 18 +2025 5192 3 12 2007 5194 3 18 2010 5194 3 6 +2025 5194 3 18 2008 5195 3 18 2007 5196 3 18 2010 5196 3 6 +2025 5196 3 18 2008 5197 3 18 2009 5198 3 6 2015 5201 3 18 @@ -1103,23 +1284,33 @@ year location stratum visits 2011 5218 3 12 2008 5254 3 18 2011 5254 3 18 +2024 5254 3 12 +2025 5254 3 18 2008 5255 3 18 2011 5255 3 18 +2024 5255 3 12 +2025 5255 3 18 2008 5256 3 18 2011 5256 3 18 2014 5256 3 12 +2025 5256 3 18 2009 5257 3 12 2012 5257 3 18 2015 5257 3 18 2019 5257 3 18 +2025 5257 3 18 2009 5258 3 18 +2024 5258 3 12 +2025 5258 3 18 2009 5259 3 6 2017 5259 3 18 2021 5259 3 18 +2024 5259 3 18 2010 5260 3 18 2016 5260 3 18 2019 5260 3 18 2022 5260 3 18 +2025 5260 3 18 2017 5261 3 18 2021 5261 3 18 2023 5261 3 18 @@ -1134,16 +1325,19 @@ year location stratum visits 2015 5263 3 18 2018 5263 3 18 2021 5263 3 18 +2024 5263 3 18 2007 5264 3 18 2010 5264 3 18 2013 5264 3 18 2016 5264 3 18 2019 5264 3 18 2022 5264 3 18 +2025 5264 3 18 2007 5265 3 18 2010 5265 3 18 2016 5265 3 18 2019 5265 3 18 +2025 5265 3 18 2008 5266 3 18 2011 5266 3 18 2014 5266 3 18 @@ -1154,14 +1348,17 @@ year location stratum visits 2010 5267 3 18 2016 5267 3 18 2019 5267 3 12 +2024 5267 3 18 2007 5268 3 18 2010 5268 3 6 2016 5268 3 18 2019 5268 3 18 +2024 5268 3 18 2008 5269 3 18 2011 5269 3 18 2014 5269 3 12 2020 5269 3 12 +2024 5269 3 18 2008 5270 3 18 2011 5270 3 6 2014 5270 3 18 @@ -1173,23 +1370,27 @@ year location stratum visits 2015 5271 3 18 2018 5271 3 18 2021 5271 3 18 +2024 5271 3 15 2009 5272 3 6 2012 5272 3 12 2015 5272 3 18 2018 5272 3 15 2021 5272 3 15 +2024 5272 3 15 2007 5273 3 18 2010 5273 3 6 2013 5273 3 6 2016 5273 3 12 2019 5273 3 18 2022 5273 3 18 +2025 5273 3 18 2007 5274 3 18 2010 5274 3 12 2013 5274 3 6 2016 5274 3 5 2019 5274 3 17 2022 5274 3 17 +2025 5274 3 18 2008 5275 3 18 2014 5275 3 18 2017 5275 3 18 @@ -1201,22 +1402,26 @@ year location stratum visits 2016 5276 3 18 2019 5276 3 18 2022 5276 3 11 +2025 5276 3 18 2007 5277 3 18 2010 5277 3 12 2013 5277 3 18 2016 5277 3 18 2019 5277 3 18 2022 5277 3 18 +2025 5277 3 18 2009 5278 3 6 2012 5278 3 12 2015 5278 3 6 2018 5278 3 18 2021 5278 3 18 +2024 5278 3 18 2009 5279 3 18 2012 5279 3 18 2015 5279 3 18 2018 5279 3 18 2021 5279 3 18 +2024 5279 3 18 2008 5280 3 18 2011 5280 3 18 2014 5280 3 18 @@ -1239,17 +1444,20 @@ year location stratum visits 2015 5285 3 18 2018 5285 3 18 2021 5285 3 18 +2024 5285 3 18 2007 5286 3 18 2010 5286 3 18 2013 5286 3 18 2016 5286 3 18 2007 5287 3 18 2013 5287 3 6 +2025 5287 3 18 2007 5288 3 18 2010 5288 3 18 2013 5288 3 12 2016 5288 3 18 2019 5288 3 18 +2024 5288 3 6 2011 5289 3 18 2017 5289 3 18 2007 5290 3 18 @@ -1261,6 +1469,7 @@ year location stratum visits 2007 5292 3 18 2013 5292 3 12 2019 5292 3 6 +2025 5292 3 18 2008 5293 3 18 2011 5293 3 18 2014 5293 3 18 @@ -1269,24 +1478,31 @@ year location stratum visits 2023 5293 3 18 2009 5294 3 12 2009 5297 3 18 +2025 5297 3 18 2007 5300 3 12 2013 5300 3 18 2014 5301 3 18 2008 5303 3 12 +2025 5306 3 18 2007 5307 3 18 2008 5307 3 18 2012 5307 3 18 +2025 5307 3 18 2007 5309 3 18 2015 5309 3 18 +2025 5309 3 18 +2025 5310 3 18 2007 5311 3 18 2015 5311 3 18 2019 5311 3 18 2022 5311 3 18 +2025 5311 3 18 2007 5312 3 18 2012 5312 3 18 2015 5312 3 18 2018 5312 3 18 2021 5312 3 18 +2024 5312 3 18 2007 5313 3 18 2011 5313 3 18 2014 5313 3 18 @@ -1296,30 +1512,38 @@ year location stratum visits 2008 5314 3 18 2015 5314 3 18 2019 5314 3 18 +2025 5314 3 18 2011 5315 3 18 +2025 5315 3 18 2008 5319 3 12 2011 5319 3 18 +2025 5319 3 18 2008 5320 3 12 2011 5320 3 18 +2025 5320 3 18 2007 5321 3 12 2010 5321 3 6 2013 5321 3 18 2016 5321 3 18 +2025 5321 3 18 2007 5322 3 18 2010 5322 3 18 2013 5322 3 18 2016 5322 3 18 2019 5322 3 18 2022 5322 3 18 +2025 5322 3 18 2008 5323 3 18 2011 5323 3 12 2014 5323 3 18 2017 5323 3 18 +2025 5323 3 18 2007 5324 3 12 2010 5324 3 18 2012 5324 3 18 2013 5324 3 12 2018 5324 3 18 +2025 5324 3 18 2007 5328 3 18 2013 5328 3 6 2012 5329 3 12 @@ -1363,6 +1587,7 @@ year location stratum visits 2017 5340 3 18 2020 5340 3 18 2023 5340 3 18 +2025 5340 3 18 2009 5341 3 18 2012 5341 3 18 2015 5341 3 18 @@ -1375,38 +1600,47 @@ year location stratum visits 2009 5344 3 18 2016 5357 3 12 2019 5357 3 18 +2025 5357 3 18 2007 5365 3 18 2010 5365 3 18 2013 5365 3 18 2016 5365 3 12 2018 5365 3 18 +2025 5365 3 18 2007 5366 3 18 2013 5366 3 18 2016 5366 3 18 2019 5366 3 18 2022 5366 3 18 +2025 5366 3 18 2012 5367 3 18 2013 5367 3 18 2015 5367 3 18 2016 5367 3 18 2019 5367 3 18 2022 5367 3 18 +2025 5367 3 18 2008 5368 3 12 2013 5368 3 12 2017 5368 3 18 2021 5368 3 18 +2024 5368 3 18 2008 5369 3 18 2009 5370 3 6 2021 5370 3 18 +2024 5370 3 18 2009 5371 3 12 2021 5371 3 18 +2024 5371 3 18 2022 5372 3 18 2007 5373 3 18 2010 5373 3 18 2016 5373 3 18 2019 5373 3 18 2022 5373 3 18 +2025 5373 3 18 2022 5374 3 18 +2025 5374 3 18 2008 5375 3 18 2011 5375 3 18 2017 5375 3 6 @@ -1415,6 +1649,7 @@ year location stratum visits 2009 5376 3 17 2018 5376 3 18 2021 5376 3 18 +2024 5376 3 18 2008 5378 3 18 2017 5378 3 6 2021 5378 3 17 @@ -1423,35 +1658,43 @@ year location stratum visits 2021 5379 3 17 2023 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3 18 +2024 5395 3 18 2008 5396 3 18 2011 5396 3 12 2012 5396 3 6 2014 5396 3 18 2017 5396 3 18 2020 5396 3 12 +2025 5396 3 18 2007 5397 3 18 2013 5397 3 18 2016 5397 3 18 2019 5397 3 18 2022 5397 3 12 +2025 5397 3 18 2007 5398 3 18 2010 5398 3 18 2013 5398 3 18 2016 5398 3 18 2019 5398 3 18 2022 5398 3 18 +2024 5398 3 12 2016 5399 3 18 2019 5399 3 18 2009 5400 3 18 @@ -1505,21 +1755,27 @@ year location stratum visits 2015 5400 3 18 2018 5400 3 18 2021 5400 3 18 +2025 5400 3 18 +2024 5401 3 18 2009 5402 3 17 2012 5402 3 6 +2024 5402 3 12 2007 5403 3 18 2010 5403 3 18 +2024 5403 3 12 2008 5405 3 18 2022 5405 3 12 2011 5406 3 18 2014 5406 3 18 2017 5406 3 18 2020 5406 3 18 +2025 5406 3 18 2007 5407 3 18 2010 5407 3 18 2013 5407 3 18 2016 5407 3 18 2020 5407 3 18 +2025 5407 3 18 2008 5409 3 12 2011 5409 3 16 2015 5409 3 18 @@ -1527,27 +1783,33 @@ year location stratum visits 2017 5416 3 18 2020 5416 3 18 2023 5416 3 18 +2025 5417 3 18 2007 5419 3 18 2010 5419 3 18 2013 5419 3 18 2016 5419 3 18 2019 5419 3 18 2022 5419 3 18 +2025 5419 3 18 2009 5420 3 18 2012 5420 3 6 2015 5420 3 18 +2025 5420 3 18 2008 5421 3 12 2011 5421 3 18 2014 5421 3 18 +2025 5421 3 18 2007 5422 3 18 2010 5422 3 18 2013 5422 3 18 2016 5422 3 18 2008 5473 3 10 2019 5473 3 18 +2025 5473 3 18 2008 5474 3 10 2018 5474 3 18 2021 5474 3 18 +2025 5474 3 18 2009 5481 3 18 2008 5482 3 18 2011 5482 3 18 @@ -1562,12 +1824,14 @@ year location stratum visits 2015 5484 3 18 2018 5484 3 18 2021 5484 3 18 +2024 5484 3 18 2007 5485 3 18 2010 5485 3 18 2013 5485 3 18 2016 5485 3 18 2019 5485 3 18 2022 5485 3 18 +2025 5485 3 18 2007 5487 3 18 2010 5487 3 18 2013 5487 3 18 @@ -1576,15 +1840,18 @@ year location stratum visits 2011 5488 3 12 2009 5490 3 12 2016 5490 3 18 +2024 5490 3 18 2008 5491 3 18 2011 5491 3 12 2007 5493 3 18 2010 5493 3 18 2018 5493 3 18 2021 5493 3 18 +2024 5493 3 18 2009 5494 3 18 2018 5494 3 18 2021 5494 3 18 +2024 5494 3 18 2008 5495 3 18 2007 5496 3 18 2010 5496 3 18 @@ -1610,6 +1877,7 @@ year location stratum visits 2014 5503 3 15 2018 5503 3 17 2021 5503 3 17 +2024 5503 3 15 2007 5505 3 18 2010 5505 3 6 2019 5505 3 18 @@ -1619,25 +1887,32 @@ year location stratum visits 2015 5515 3 18 2018 5515 3 18 2009 5517 3 12 +2025 5517 3 18 2015 5518 3 18 +2025 5518 3 18 2009 5520 3 18 2018 5520 3 6 +2024 5520 3 12 +2025 5520 3 18 2009 5521 3 18 2012 5521 3 12 2015 5521 3 18 2018 5521 3 18 2021 5521 3 18 +2024 5521 3 18 2009 5522 3 18 2012 5522 3 12 2015 5522 3 18 2018 5522 3 18 2021 5522 3 18 +2024 5522 3 18 2007 5523 3 18 2010 5523 3 18 2013 5523 3 18 2016 5523 3 18 2019 5523 3 18 2022 5523 3 18 +2025 5523 3 18 2008 5524 3 18 2011 5524 3 18 2014 5524 3 18 @@ -1650,12 +1925,15 @@ year location stratum visits 2016 5525 3 18 2019 5525 3 18 2022 5525 3 18 +2025 5525 3 18 2011 5526 3 12 2017 5526 3 18 +2024 5526 3 18 2007 5527 3 18 2013 5527 3 18 2019 5527 3 18 2022 5527 3 18 +2025 5527 3 18 2008 5528 3 18 2011 5528 3 18 2014 5528 3 18 @@ -1667,15 +1945,18 @@ year location stratum visits 2015 5529 3 18 2018 5529 3 18 2021 5529 3 18 +2024 5529 3 18 2014 5530 3 18 2017 5530 3 18 2020 5530 3 18 +2024 5530 3 18 2007 5531 3 18 2010 5531 3 18 2013 5531 3 18 2016 5531 3 18 2019 5531 3 18 2022 5531 3 18 +2025 5531 3 18 2008 5533 3 18 2011 5533 3 18 2014 5533 3 17 @@ -1688,62 +1969,87 @@ year location stratum visits 2016 5534 3 18 2019 5534 3 18 2022 5534 3 18 +2025 5534 3 18 2009 5535 3 18 2012 5535 3 12 2015 5535 3 18 2018 5535 3 18 2021 5535 3 18 +2024 5535 3 18 2008 5539 3 12 2014 5539 3 12 2017 5539 3 18 2020 5539 3 18 2023 5539 3 18 +2024 5541 3 15 2009 5542 3 18 2013 5542 3 18 2016 5542 3 18 +2025 5542 3 18 2014 5543 3 18 +2025 5543 3 18 2009 5544 3 18 +2024 5544 3 15 2007 5545 3 18 +2024 5545 3 17 +2024 5546 3 15 2013 5548 3 18 2016 5548 3 18 2019 5548 3 18 2021 5548 3 6 +2025 5548 3 18 2015 5549 3 15 2017 5549 3 6 2021 5549 3 18 +2025 5549 3 18 2009 5551 3 12 2012 5551 3 18 2018 5551 3 18 2021 5551 3 18 +2024 5551 3 18 +2024 5558 3 15 +2024 5559 3 15 2009 5560 3 18 +2024 5560 3 14 2009 5561 3 18 2021 5561 3 12 +2024 5561 3 18 2009 5562 3 18 +2024 5562 3 12 2007 5563 3 18 +2025 5563 3 18 2007 5566 3 10 2013 5566 3 18 2007 5567 3 8 2008 5571 3 14 2007 5572 3 18 +2025 5572 3 18 2010 5573 3 18 +2025 5573 3 18 2007 5575 3 12 2010 5575 3 18 2010 5576 3 18 +2025 5576 3 18 2007 5577 3 18 2010 5577 3 18 +2025 5577 3 18 2008 5578 3 18 +2025 5578 3 18 2007 5579 3 9 2010 5579 3 18 2021 5579 3 6 2014 5580 3 18 +2025 5580 3 18 2009 5581 3 18 2013 5581 3 18 +2025 5581 3 18 2008 5582 3 18 2009 5585 3 18 2009 5660 3 18 2012 5660 3 18 2018 5660 3 18 2021 5660 3 18 +2024 5660 3 18 2008 5670 3 18 2011 5670 3 18 2014 5670 3 6 @@ -1757,9 +2063,11 @@ year location stratum visits 2013 5686 3 18 2016 5686 3 18 2019 5686 3 18 +2025 5686 3 18 2016 5687 3 12 2019 5687 3 18 2022 5687 3 18 +2025 5687 3 18 2008 5688 3 18 2011 5688 3 18 2014 5688 3 18 @@ -1780,6 +2088,7 @@ year location stratum visits 2012 5696 3 18 2018 5696 3 18 2021 5696 3 18 +2024 5696 3 18 2008 5697 3 18 2010 5697 3 18 2013 5697 3 18 @@ -1789,21 +2098,25 @@ year location stratum visits 2016 5698 3 18 2019 5698 3 18 2022 5698 3 18 +2025 5698 3 18 2007 5700 3 18 2010 5700 3 18 2016 5700 3 18 2019 5700 3 18 2022 5700 3 18 +2025 5700 3 18 2009 5708 3 18 2012 5708 3 6 2015 5708 3 6 2018 5708 3 18 2021 5708 3 18 +2024 5708 3 18 2010 5710 3 18 2013 5710 3 18 2016 5710 3 18 2019 5710 3 12 2022 5710 3 18 +2025 5710 3 18 2008 5711 3 18 2011 5711 3 18 2014 5711 3 18 @@ -1841,38 +2154,48 @@ year location stratum visits 2015 5717 3 18 2018 5717 3 18 2021 5717 3 18 +2024 5717 3 18 2009 5718 3 18 2012 5718 3 12 2015 5718 3 17 2018 5718 3 18 2021 5718 3 18 +2024 5718 3 18 2009 5719 3 18 2012 5719 3 12 2015 5719 3 18 2018 5719 3 18 2021 5719 3 18 +2024 5719 3 18 2009 5720 3 12 2012 5720 3 6 2015 5720 3 18 2018 5720 3 18 2021 5720 3 17 +2024 5720 3 18 2009 5722 3 18 2012 5722 3 6 2015 5722 3 18 2018 5722 3 18 2021 5722 3 18 +2024 5722 3 18 2015 5724 3 18 +2024 5724 3 12 2013 5726 3 18 2014 5726 3 4 2016 5726 3 18 +2024 5726 3 15 2009 5728 3 18 2015 5728 3 18 +2025 5728 3 18 2014 5730 3 18 +2025 5730 3 18 2007 5731 3 18 2013 5731 3 12 2016 5731 3 18 2019 5731 3 18 2022 5731 3 18 +2025 5731 3 18 2011 5732 3 12 2014 5732 3 6 2017 5732 3 18 @@ -1883,6 +2206,7 @@ year location stratum visits 2016 5733 3 18 2021 5733 3 18 2022 5733 3 18 +2025 5733 3 18 2008 5734 3 18 2011 5734 3 18 2014 5734 3 6 @@ -1891,6 +2215,7 @@ year location stratum visits 2009 5737 3 18 2018 5737 3 18 2021 5737 3 18 +2024 5737 3 17 2017 5740 3 18 2020 5740 3 18 2023 5740 3 18 @@ -1899,8 +2224,12 @@ year location stratum visits 2010 5742 3 12 2018 5742 3 18 2021 5742 3 18 -2018 5743 3 24 +2024 5742 3 6 +2025 5742 3 18 +2018 5743 3 18 2021 5743 3 18 +2024 5743 3 6 +2025 5743 3 18 2019 5744 3 18 2022 5744 3 24 2019 5745 3 18 @@ -1908,24 +2237,30 @@ year location stratum visits 2007 5746 3 18 2018 5746 3 18 2021 5746 3 18 +2024 5746 3 18 2017 5747 3 18 2020 5747 3 12 2021 5747 3 6 2023 5747 3 18 2007 5749 3 12 2010 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2010 5879 3 12 2012 5879 3 18 @@ -2001,16 +2344,19 @@ year location stratum visits 2013 5888 3 18 2016 5888 3 18 2021 5888 3 6 +2024 5888 3 18 2009 5889 3 12 2016 5889 3 18 2021 5889 3 18 2022 5889 3 18 2023 5889 3 18 +2024 5889 3 6 2009 5890 3 6 2010 5890 3 18 2013 5890 3 18 2016 5890 3 18 2021 5890 3 6 +2024 5890 3 18 2009 5892 3 12 2014 5892 3 12 2017 5892 3 6 @@ -2025,9 +2371,11 @@ year location stratum visits 2016 5897 3 18 2019 5897 3 18 2022 5897 3 18 +2025 5897 3 18 2018 5899 3 6 2019 5899 3 18 2022 5899 3 18 +2025 5899 3 18 2011 5900 3 18 2014 5900 3 18 2017 5900 3 18 @@ -2038,16 +2386,20 @@ year location stratum visits 2015 5901 3 18 2018 5901 3 18 2021 5901 3 18 +2024 5901 3 18 2015 5902 3 18 2019 5902 3 18 +2025 5902 3 6 2009 5903 3 18 2012 5903 3 18 2015 5903 3 18 2018 5903 3 18 2021 5903 3 18 +2024 5903 3 18 2012 5904 3 6 2018 5904 3 18 2021 5904 3 12 +2024 5904 3 18 2008 5905 3 18 2011 5905 3 18 2014 5905 3 18 @@ -2060,11 +2412,13 @@ year location stratum visits 2016 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18 2019 5924 3 18 2022 5924 3 18 +2025 5924 3 18 2009 5925 3 17 2012 5925 3 6 2015 5925 3 18 2018 5925 3 18 2021 5925 3 18 +2024 5925 3 18 2008 5926 3 18 2011 5926 3 18 2014 5926 3 18 @@ -2150,6 +2513,7 @@ year location stratum visits 2015 5927 3 18 2018 5927 3 18 2021 5927 3 18 +2024 5927 3 18 2008 5928 3 18 2011 5928 3 18 2014 5928 3 18 @@ -2166,6 +2530,7 @@ year location stratum visits 2015 5930 3 18 2018 5930 3 18 2021 5930 3 18 +2024 5930 3 18 2007 5931 3 18 2010 5931 3 12 2012 5931 3 6 @@ -2173,6 +2538,7 @@ year location stratum visits 2016 5931 3 18 2019 5931 3 18 2022 5931 3 18 +2025 5931 3 18 2008 5932 3 18 2011 5932 3 18 2014 5932 3 18 @@ -2184,10 +2550,13 @@ year location stratum visits 2015 5933 3 18 2018 5933 3 18 2021 5933 3 18 +2024 5933 3 18 2007 5936 3 6 2019 5936 3 18 +2025 5936 3 18 2008 5956 3 18 2011 5956 3 18 +2025 5956 3 18 2008 6012 3 18 2011 6012 3 18 2014 6012 3 12 @@ -2199,6 +2568,7 @@ year location stratum visits 2010 6013 3 18 2018 6013 3 18 2021 6013 3 18 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6089 3 18 2022 6089 3 18 +2025 6089 3 18 2008 6090 3 18 2011 6090 3 18 2014 6090 3 18 @@ -2310,25 +2688,31 @@ year location stratum visits 2010 6092 3 12 2018 6092 3 18 2021 6092 3 18 +2024 6092 3 18 2007 6093 3 18 2010 6093 3 18 2013 6093 3 18 2016 6093 3 18 2019 6093 3 18 2022 6093 3 18 +2025 6093 3 18 2015 6094 3 18 2021 6094 3 18 +2024 6094 3 18 2009 6095 3 12 2012 6095 3 12 2015 6095 3 18 2018 6095 3 18 2021 6095 3 18 +2024 6095 3 18 2010 6096 3 18 2015 6096 3 18 +2025 6096 3 18 2015 6097 3 18 2015 6098 3 18 2015 6099 3 18 2018 6099 3 12 +2025 6099 3 15 2008 6100 3 18 2011 6100 3 18 2015 6100 3 18 @@ -2340,11 +2724,13 @@ year location stratum visits 2014 6105 3 18 2018 6105 3 18 2021 6105 3 18 +2024 6105 3 18 2008 6110 3 16 2011 6110 3 14 2014 6110 3 15 2017 6110 3 15 2021 6110 3 18 +2024 6110 3 18 2008 6111 3 18 2011 6111 3 18 2014 6111 3 18 @@ -2356,11 +2742,13 @@ year location stratum visits 2015 6112 3 18 2018 6112 3 18 2021 6112 3 18 +2024 6112 3 18 2009 6113 3 12 2012 6113 3 14 2015 6113 3 12 2018 6113 3 18 2021 6113 3 18 +2024 6113 3 16 2008 6114 3 12 2014 6114 3 12 2017 6114 3 18 @@ -2371,22 +2759,26 @@ year location stratum visits 2016 6115 3 18 2019 6115 3 18 2022 6115 3 18 +2025 6115 3 18 2009 6116 3 18 2012 6116 3 18 2015 6116 3 18 2018 6116 3 18 2021 6116 3 18 +2024 6116 3 18 2007 6117 3 18 2010 6117 3 18 2013 6117 3 18 2016 6117 3 18 2019 6117 3 18 2022 6117 3 18 +2025 6117 3 18 2009 6118 3 18 2012 6118 3 18 2015 6118 3 18 2018 6118 3 18 2021 6118 3 18 +2024 6118 3 18 2011 6119 3 12 2012 6119 3 18 2014 6119 3 18 @@ -2405,26 +2797,32 @@ year location stratum visits 2016 6125 3 18 2019 6125 3 18 2022 6125 3 18 +2025 6125 3 18 2009 6157 3 18 2018 6157 3 18 2021 6157 3 18 +2024 6157 3 18 2009 6158 3 18 2009 6159 3 18 2007 6161 3 6 2019 6161 3 18 -2022 6161 3 24 +2022 6161 3 18 +2025 6161 3 18 2007 6172 3 18 2016 6172 3 18 2019 6172 3 12 2022 6172 3 6 +2025 6172 3 18 2007 6178 3 18 2010 6178 3 18 2013 6178 3 6 2016 6178 3 6 +2025 6178 3 6 2010 6208 3 18 2013 6208 3 18 2014 6208 3 6 2016 6208 3 18 +2025 6208 3 6 2007 6229 3 12 2010 6229 3 8 2007 6230 3 12 @@ -2438,10 +2836,12 @@ year location stratum visits 2016 6232 3 18 2019 6232 3 18 2022 6232 3 18 +2025 6232 3 18 2009 6233 3 9 2015 6233 3 18 2018 6233 3 18 2021 6233 3 18 +2024 6233 3 15 2008 6234 3 18 2011 6234 3 12 2017 6234 3 18 @@ -2453,6 +2853,7 @@ year location stratum visits 2016 6235 3 18 2019 6235 3 18 2022 6235 3 18 +2025 6235 3 18 2008 6236 3 18 2014 6236 3 18 2017 6236 3 18 @@ -2463,6 +2864,7 @@ year location stratum visits 2015 6237 3 6 2018 6237 3 18 2021 6237 3 18 +2024 6237 3 18 2007 6238 3 18 2013 6238 3 6 2014 6238 3 2 @@ -2470,20 +2872,24 @@ year location stratum visits 2016 6238 3 6 2019 6238 3 10 2022 6238 3 8 +2025 6238 3 6 2008 6239 3 6 2012 6239 3 17 2015 6239 3 18 2018 6239 3 18 2021 6239 3 24 +2024 6239 3 18 2013 6240 3 18 2016 6240 3 12 2019 6240 3 18 2022 6240 3 24 +2025 6240 3 18 2009 6241 3 10 2012 6241 3 15 2015 6241 3 13 2018 6241 3 8 2021 6241 3 18 +2024 6241 3 18 2009 6242 3 3 2010 6242 3 10 2007 6244 3 12 @@ -2492,16 +2898,19 @@ year location stratum visits 2016 6244 3 14 2019 6244 3 17 2022 6244 3 18 +2025 6244 3 18 2008 6245 3 6 2010 6245 3 18 2015 6245 3 18 2018 6245 3 18 2021 6245 3 18 +2024 6245 3 12 2009 6269 3 18 2012 6269 3 18 2015 6269 3 18 2018 6269 3 18 2021 6269 3 18 +2024 6269 3 18 2008 6270 3 18 2011 6270 3 18 2014 6270 3 18 @@ -2518,12 +2927,16 @@ year location stratum visits 2016 6275 3 18 2019 6275 3 18 2022 6275 3 6 +2024 6275 3 48 +2025 6275 3 6 2018 6276 3 18 2021 6276 3 6 2008 6278 3 18 +2024 6278 3 18 2007 6279 3 18 2010 6279 3 18 2013 6279 3 18 +2024 6279 3 18 2009 6280 3 18 2011 6280 3 18 2012 6280 3 18 @@ -2534,6 +2947,7 @@ year location stratum visits 2016 6281 3 12 2019 6281 3 18 2022 6281 3 18 +2025 6281 3 18 2007 6282 3 12 2010 6282 3 18 2009 6283 3 12 @@ -2569,6 +2983,8 @@ year location stratum visits 2015 6300 3 18 2018 6300 3 18 2017 6301 3 18 +2025 6301 3 18 +2024 6303 3 18 2009 6325 3 15 2009 6333 3 12 2012 6333 3 18 @@ -2580,6 +2996,7 @@ year location stratum visits 2011 6336 3 13 2014 6336 3 18 2007 6337 3 18 +2024 6338 3 18 2007 6340 3 18 2008 6340 3 18 2010 6340 3 18 @@ -2590,11 +3007,13 @@ year location stratum visits 2014 6341 3 18 2009 5179 4 18 2012 5179 4 18 +2025 5179 4 18 2009 5213 4 10 2012 5213 4 12 2015 5213 4 18 2018 5213 4 18 2021 5213 4 18 +2024 5213 4 18 2015 5214 4 12 2018 5214 4 18 2008 5222 4 12 @@ -2607,6 +3026,7 @@ year location stratum visits 2016 5356 4 12 2019 5356 4 18 2022 5356 4 18 +2025 5356 4 18 2008 5394 4 18 2011 5394 4 18 2014 5394 4 18 @@ -2620,6 +3040,7 @@ year location stratum visits 2009 5429 4 18 2012 5429 4 18 2007 5431 4 18 +2025 5431 4 18 2012 5468 4 18 2015 5468 4 6 2018 5468 4 10 @@ -2630,16 +3051,20 @@ year location stratum visits 2008 5471 4 10 2011 5471 4 4 2014 5471 4 15 +2025 5471 4 18 2009 5472 4 18 2012 5472 4 18 2015 5472 4 18 +2025 5472 4 18 2007 5477 4 18 2010 5477 4 6 2013 5477 4 18 2016 5477 4 18 2019 5477 4 18 +2025 5477 4 18 2007 5478 4 6 2016 5478 4 12 +2025 5478 4 18 2007 5486 4 18 2011 5486 4 18 2014 5486 4 18 @@ -2666,16 +3091,20 @@ year location stratum visits 2013 5536 4 18 2016 5536 4 18 2019 5536 4 18 +2022 5536 4 18 2008 5568 4 16 2010 5569 4 18 2010 5570 4 18 2007 5574 4 18 2010 5574 4 18 2018 5574 4 18 +2025 5574 4 18 2008 5589 4 18 +2025 5589 4 18 2007 5590 4 18 2009 5590 4 18 2010 5590 4 12 +2025 5590 4 18 2009 5599 4 18 2012 5599 4 18 2015 5599 4 18 @@ -2684,6 +3113,7 @@ year location stratum visits 2009 5649 4 18 2008 5656 4 18 2017 5656 4 18 +2025 5656 4 18 2007 5662 4 18 2008 5669 4 18 2011 5669 4 18 @@ -2695,12 +3125,14 @@ year location stratum visits 2018 5678 4 18 2019 5678 4 18 2021 5678 4 18 +2024 5678 4 18 2017 5679 4 18 2020 5679 4 18 2023 5679 4 18 2016 5680 4 12 2019 5680 4 18 2022 5680 4 18 +2025 5680 4 18 2008 5681 4 18 2007 5682 4 18 2007 5683 4 18 @@ -2715,6 +3147,7 @@ year location stratum visits 2016 5701 4 18 2019 5701 4 18 2022 5701 4 18 +2025 5701 4 18 2008 5721 4 18 2011 5721 4 12 2014 5721 4 18 @@ -2730,15 +3163,20 @@ year location stratum visits 2012 5866 4 12 2013 5866 4 6 2016 5866 4 12 +2024 5866 4 18 +2025 5866 4 18 2008 5880 4 18 2011 5880 4 18 2014 5880 4 18 +2025 5880 4 18 2007 5885 4 18 2015 5885 4 18 2019 5885 4 18 +2025 5885 4 18 2012 5894 4 12 2015 5894 4 18 2018 5894 4 18 +2024 5894 4 18 2011 5895 4 18 2014 5895 4 18 2017 5895 4 18 @@ -2750,6 +3188,7 @@ year location stratum visits 2016 5935 4 18 2019 5935 4 18 2022 5935 4 18 +2025 5935 4 18 2007 5964 4 18 2010 5964 4 18 2013 5964 4 18 @@ -2772,17 +3211,20 @@ year location stratum visits 2015 5999 4 18 2018 5999 4 18 2021 5999 4 18 +2024 5999 4 16 2007 6001 4 12 2010 6001 4 18 2013 6001 4 18 2016 6001 4 18 2019 6001 4 18 2022 6001 4 18 +2025 6001 4 18 2012 6002 4 12 2015 6002 4 12 2018 6002 4 14 2021 6002 4 12 2022 6002 4 8 +2024 6002 4 12 2012 6005 4 15 2009 6006 4 18 2012 6006 4 12 @@ -2802,9 +3244,11 @@ year location stratum visits 2016 6045 4 18 2019 6045 4 18 2022 6045 4 18 +2025 6045 4 12 2009 6046 4 6 2018 6046 4 18 2021 6046 4 18 +2024 6046 4 18 2008 6048 4 18 2011 6048 4 18 2014 6048 4 9 @@ -2818,21 +3262,25 @@ year location stratum visits 2015 6051 4 18 2018 6051 4 18 2021 6051 4 16 +2024 6051 4 18 2009 6053 4 8 2009 6054 4 12 2007 6060 4 18 2010 6060 4 12 2018 6060 4 17 2021 6060 4 18 +2024 6060 4 18 2007 6061 4 18 2010 6061 4 12 2019 6061 4 18 2022 6061 4 18 +2025 6061 4 18 2009 6074 4 18 2012 6074 4 6 2015 6074 4 18 2018 6074 4 18 2021 6074 4 18 +2024 6074 4 18 2011 6075 4 18 2014 6075 4 18 2017 6075 4 18 @@ -2841,6 +3289,7 @@ year location stratum visits 2015 6078 4 18 2018 6078 4 18 2022 6078 4 18 +2025 6078 4 18 2009 6106 4 18 2014 6106 4 18 2017 6106 4 18 @@ -2852,11 +3301,13 @@ year location stratum visits 2016 6133 4 18 2019 6133 4 18 2022 6133 4 24 +2025 6133 4 18 2007 6135 4 6 2012 6135 4 12 2015 6135 4 17 2018 6135 4 18 2021 6135 4 18 +2024 6135 4 18 2007 6205 4 12 2010 6205 4 18 2013 6205 4 18 @@ -2873,10 +3324,12 @@ year location stratum visits 2010 6213 4 18 2016 6213 4 12 2019 6213 4 18 +2025 6213 4 18 2007 6264 4 18 2010 6264 4 18 2018 6264 4 18 2021 6264 4 18 +2024 6264 4 18 2009 5224 5 18 2018 5224 5 18 2008 5228 5 18 @@ -2901,6 +3354,7 @@ year location stratum visits 2021 5295 5 18 2009 5299 5 18 2012 5299 5 12 +2025 5299 5 18 2007 5325 5 18 2010 5325 5 12 2013 5325 5 18 @@ -2918,6 +3372,7 @@ year location stratum visits 2014 5327 5 18 2017 5327 5 18 2020 5327 5 18 +2024 5327 5 18 2008 5359 5 18 2011 5359 5 18 2014 5359 5 18 @@ -2932,18 +3387,21 @@ year location stratum visits 2013 5470 5 8 2016 5470 5 12 2009 5475 5 18 +2025 5475 5 18 2007 5511 5 18 2009 5513 5 18 2012 5513 5 18 2015 5513 5 18 2018 5513 5 18 2021 5513 5 18 +2024 5513 5 18 2008 5514 5 18 2009 5514 5 18 2011 5514 5 16 2014 5514 5 18 2017 5514 5 18 2020 5514 5 18 +2024 5514 5 17 2007 5516 5 18 2010 5516 5 18 2013 5516 5 18 @@ -2953,6 +3411,7 @@ year location stratum visits 2015 5537 5 18 2018 5537 5 18 2021 5537 5 18 +2024 5537 5 18 2008 5538 5 18 2011 5538 5 18 2014 5538 5 18 @@ -2960,11 +3419,19 @@ year location stratum visits 2020 5538 5 12 2021 5538 5 12 2023 5538 5 18 +2024 5547 5 18 +2024 5552 5 17 +2024 5553 5 17 2008 5554 5 18 2011 5554 5 18 2015 5554 5 18 2018 5554 5 24 2021 5554 5 18 +2024 5554 5 18 +2024 5555 5 15 +2024 5556 5 18 +2025 5556 5 18 +2024 5557 5 16 2007 5632 5 12 2010 5632 5 12 2008 5650 5 11 @@ -2974,12 +3441,14 @@ year location stratum visits 2009 5652 5 6 2015 5652 5 18 2021 5652 5 18 +2024 5652 5 18 2008 5653 5 12 2011 5653 5 6 2014 5653 5 18 2017 5653 5 18 2009 5655 5 12 2012 5655 5 18 +2025 5655 5 18 2008 5659 5 18 2007 5703 5 18 2011 5703 5 12 @@ -2997,6 +3466,7 @@ year location stratum visits 2016 5709 5 18 2019 5709 5 18 2022 5709 5 18 +2025 5709 5 18 2008 5844 5 6 2011 5844 5 18 2014 5844 5 6 @@ -3012,6 +3482,7 @@ year location stratum visits 2016 5848 5 18 2007 5865 5 12 2021 5865 5 17 +2024 5865 5 12 2008 5873 5 12 2011 5873 5 6 2018 5873 5 18 @@ -3029,10 +3500,13 @@ year location stratum visits 2016 5908 5 6 2019 5908 5 6 2022 5908 5 18 +2025 5908 5 18 2008 5944 5 18 2020 5944 5 12 +2025 5944 5 18 2007 5945 5 18 2019 5945 5 18 +2025 5945 5 18 2008 5966 5 12 2010 5966 5 18 2014 5966 5 6 @@ -3042,6 +3516,7 @@ year location stratum visits 2015 6037 5 18 2018 6037 5 18 2021 6037 5 18 +2024 6037 5 18 2008 6041 5 18 2014 6041 5 18 2017 6041 5 18 @@ -3056,6 +3531,7 @@ year location stratum visits 2015 6108 5 18 2018 6108 5 18 2021 6108 5 18 +2024 6108 5 18 2017 6156 5 18 2020 6156 5 18 2023 6156 5 18 @@ -3077,6 +3553,7 @@ year location stratum visits 2016 6191 5 18 2019 6191 5 18 2022 6191 5 18 +2025 6191 5 18 2008 6193 5 18 2011 6193 5 18 2017 6193 5 18 @@ -3100,11 +3577,13 @@ year location stratum visits 2016 6243 5 16 2019 6243 5 18 2022 6243 5 18 +2025 6243 5 17 2010 6247 5 12 2012 6247 5 18 2015 6247 5 18 2018 6247 5 18 2021 6247 5 18 +2024 6247 5 18 2007 6305 5 18 2010 6305 5 18 2017 6305 5 12 @@ -3131,20 +3610,25 @@ year location stratum visits 2010 5247 6 18 2013 5247 6 18 2016 5247 6 18 +2025 5249 6 18 2007 5253 6 18 2010 5253 6 12 +2025 5253 6 12 2009 5296 6 18 2012 5296 6 12 2015 5296 6 18 2018 5296 6 18 2021 5296 6 18 +2025 5296 6 18 2009 5298 6 18 +2025 5298 6 18 2007 5346 6 18 2011 5346 6 12 2013 5346 6 18 2016 5346 6 12 2019 5346 6 18 2022 5346 6 18 +2025 5346 6 18 2011 5347 6 12 2016 5347 6 18 2019 5347 6 18 @@ -3152,28 +3636,34 @@ year location stratum visits 2014 5348 6 18 2017 5348 6 18 2020 5348 6 18 -2023 5348 6 12 +2023 5348 6 18 2007 5350 6 18 2010 5350 6 18 2019 5350 6 18 2007 5377 6 18 2013 5377 6 18 2019 5377 6 16 +2024 5377 6 18 2008 5383 6 18 2011 5383 6 18 2023 5383 6 6 +2024 5383 6 18 2008 5392 6 18 2011 5392 6 12 2014 5392 6 18 2017 5392 6 18 2020 5392 6 18 2023 5392 6 18 +2025 5392 6 18 2009 5393 6 18 2012 5393 6 6 2015 5393 6 18 2019 5393 6 6 +2025 5393 6 18 2010 5415 6 18 +2025 5415 6 18 2007 5453 6 18 +2025 5453 6 18 2009 5463 6 18 2009 5466 6 18 2009 5492 6 18 @@ -3191,9 +3681,12 @@ year location stratum visits 2016 5532 6 18 2019 5532 6 18 2022 5532 6 18 +2025 5532 6 18 2016 5540 6 18 2019 5540 6 18 2022 5540 6 18 +2025 5540 6 18 +2024 5550 6 18 2011 5663 6 18 2014 5663 6 12 2017 5663 6 18 @@ -3208,17 +3701,21 @@ year location stratum visits 2015 5689 6 18 2018 5689 6 18 2021 5689 6 18 +2024 5689 6 18 2012 5690 6 18 2015 5690 6 12 2018 5690 6 18 2021 5690 6 18 +2024 5690 6 18 2008 5692 6 18 +2025 5692 6 12 2008 5693 6 18 2011 5693 6 18 2014 5693 6 12 2017 5693 6 17 2021 5693 6 12 2023 5693 6 6 +2025 5693 6 18 2007 5702 6 18 2011 5702 6 12 2017 5702 6 18 @@ -3227,12 +3724,15 @@ year location stratum visits 2014 5704 6 12 2017 5704 6 18 2020 5704 6 18 +2024 5704 6 18 2007 5705 6 6 2012 5705 6 18 2017 5705 6 6 2021 5705 6 18 2022 5705 6 6 2023 5705 6 18 +2024 5705 6 12 +2025 5705 6 18 2008 5706 6 18 2012 5706 6 18 2015 5706 6 12 @@ -3244,6 +3744,7 @@ year location stratum visits 2010 5736 6 18 2019 5736 6 18 2022 5736 6 18 +2025 5736 6 12 2017 5739 6 18 2020 5739 6 18 2023 5739 6 18 @@ -3262,9 +3763,11 @@ year location stratum visits 2015 5856 6 18 2018 5856 6 18 2021 5856 6 18 +2024 5856 6 12 2016 5862 6 18 2017 5863 6 18 2016 5864 6 18 +2025 5864 6 18 2007 5867 6 12 2008 5867 6 18 2009 5867 6 12 @@ -3272,6 +3775,8 @@ year location stratum visits 2011 5867 6 18 2012 5867 6 12 2016 5867 6 18 +2024 5867 6 18 +2025 5867 6 18 2008 5868 6 18 2008 5872 6 12 2014 5872 6 18 @@ -3281,6 +3786,7 @@ year location stratum visits 2016 5875 6 18 2019 5875 6 18 2022 5875 6 6 +2025 5875 6 18 2008 5876 6 18 2011 5876 6 18 2014 5876 6 18 @@ -3289,15 +3795,17 @@ year location stratum visits 2023 5876 6 6 2009 5878 6 18 2012 5878 6 6 -2018 5878 6 24 +2018 5878 6 18 2007 5883 6 18 2010 5883 6 18 2013 5883 6 18 +2025 5883 6 18 2009 5884 6 18 2012 5884 6 18 2015 5884 6 18 2009 5893 6 12 2017 5893 6 6 +2023 5893 6 18 2008 5957 6 18 2011 5957 6 18 2007 5963 6 18 @@ -3323,6 +3831,7 @@ year location stratum visits 2015 6028 6 18 2018 6028 6 18 2021 6028 6 18 +2024 6028 6 18 2007 6029 6 18 2008 6029 6 18 2009 6029 6 18 @@ -3331,6 +3840,7 @@ year location stratum visits 2016 6029 6 18 2019 6029 6 18 2022 6029 6 18 +2025 6029 6 18 2007 6032 6 18 2008 6032 6 18 2009 6032 6 18 @@ -3345,7 +3855,7 @@ year location stratum visits 2016 6033 6 18 2017 6036 6 18 2020 6036 6 18 -2023 6036 6 6 +2023 6036 6 18 2008 6038 6 18 2010 6038 6 18 2013 6038 6 12 @@ -3359,19 +3869,23 @@ year location stratum visits 2016 6040 6 18 2019 6040 6 18 2022 6040 6 18 +2025 6040 6 18 2009 6042 6 18 2011 6042 6 18 2012 6042 6 18 2015 6042 6 18 2018 6042 6 18 2021 6042 6 18 +2024 6042 6 18 2009 6043 6 12 2016 6043 6 18 2019 6043 6 18 2022 6043 6 18 +2025 6043 6 18 2008 6047 6 18 2018 6047 6 18 2021 6047 6 18 +2024 6047 6 18 2008 6052 6 18 2011 6052 6 18 2014 6052 6 12 @@ -3380,26 +3894,31 @@ year location stratum visits 2015 6062 6 18 2018 6062 6 18 2021 6062 6 18 +2024 6062 6 18 2007 6070 6 18 2010 6070 6 18 2013 6070 6 18 2016 6070 6 18 2019 6070 6 18 +2025 6070 6 18 2007 6101 6 11 2010 6101 6 18 2013 6101 6 18 2016 6101 6 18 2019 6101 6 18 2022 6101 6 18 +2025 6101 6 18 2008 6102 6 18 2013 6102 6 18 2016 6102 6 18 2019 6102 6 18 2022 6102 6 18 +2025 6102 6 18 2008 6103 6 18 2016 6103 6 18 2019 6103 6 18 2022 6103 6 18 +2025 6103 6 18 2009 6104 6 18 2012 6104 6 18 2015 6104 6 18 @@ -3415,18 +3934,21 @@ year location stratum visits 2015 6109 6 12 2018 6109 6 18 2021 6109 6 18 +2024 6109 6 18 2017 6163 6 18 2020 6163 6 18 2023 6163 6 18 2007 6164 6 12 2019 6164 6 18 2022 6164 6 18 +2025 6164 6 18 2007 6167 6 18 2010 6167 6 18 2013 6167 6 18 2016 6167 6 18 2019 6167 6 18 2022 6167 6 18 +2025 6167 6 18 2009 6175 6 12 2012 6175 6 6 2009 6183 6 18 @@ -3445,6 +3967,7 @@ year location stratum visits 2016 6207 6 18 2018 6207 6 18 2021 6207 6 18 +2024 6207 6 18 2009 6261 6 18 2018 6261 6 18 2008 6262 6 18 diff --git a/inst/results/effort.yml b/inst/results/effort.yml index da8ec8e..18c415b 100644 --- a/inst/results/effort.yml +++ b/inst/results/effort.yml @@ -1,17 +1,21 @@ ..generic: - git2rdata: 0.4.0 + git2rdata: 0.5.2 optimize: yes NA string: NA sorting: - stratum - location - year - hash: b72ed4a64614e110e30829820399c2981bae06cc - data_hash: 239da4527a53b3efa1c96326e0e1a959a93e2a14 + hash: a389a963790e3e523740b2f1c8d98f8a6b10052c + data_hash: 1f199715f95a1a5481b887ae61f00a492818bc74 + name: effort + title: Sampling effort year: class: integer + description: year of the survey location: class: integer + description: identifier of the location stratum: class: factor labels: @@ -29,5 +33,7 @@ stratum: - 5 - 6 ordered: no + description: name of the stratum visits: class: integer + description: number of visited point locations diff --git a/inst/results/estimate.tsv b/inst/results/estimate.tsv index a57c80a..4405c87 100644 --- a/inst/results/estimate.tsv +++ b/inst/results/estimate.tsv @@ -1,2209 +1,2757 @@ analysis year estimate lower_confidence_limit upper_confidence_limit -193 2007 -1.6659 -2.0917 -1.2401 -193 2010 -2.1244 -2.6172 -1.6317 -193 2013 -2.4509 -2.9686 -1.9333 -193 2016 -1.5949 -1.9589 -1.2308 -193 2019 -0.5079 -0.7954 -0.2203 -193 2022 -0.2309 -0.5632 0.1013 -194 2007 0.1817 -0.3004 0.6639 -194 2010 0.2276 -0.2367 0.692 -194 2013 0.3726 -0.0386 0.7839 -194 2016 0.632 0.2354 1.0285 -194 2019 0.7972 0.4241 1.1703 -194 2022 0.828 0.3981 1.258 -195 2007 -1.0449 -1.2088 -0.8809 -195 2010 -0.8365 -0.9977 -0.6753 -195 2013 -0.9678 -1.1437 -0.7918 -195 2016 -0.9863 -1.1548 -0.8177 -195 2019 -1.3654 -1.5497 -1.1811 -195 2022 -1.4165 -1.6432 -1.1898 -196 2007 -0.9442 -1.0723 -0.8161 -196 2010 -0.806 -0.9238 -0.6881 -196 2013 -0.6631 -0.7785 -0.5477 -196 2016 -0.546 -0.6585 -0.4335 -196 2019 -0.5543 -0.6661 -0.4425 -196 2022 -0.5288 -0.6573 -0.4002 -197 2007 -0.4198 -0.5022 -0.3373 -197 2008 -0.3557 -0.4381 -0.2733 -197 2009 -0.3681 -0.4528 -0.2834 -197 2010 -0.419 -0.5017 -0.3364 -197 2011 -0.3805 -0.466 -0.2949 -197 2012 -0.4087 -0.4986 -0.3189 -197 2013 -0.3686 -0.4532 -0.284 -197 2014 -0.4826 -0.5692 -0.396 -197 2015 -0.4001 -0.4885 -0.3117 -197 2016 -0.538 -0.6229 -0.4531 -197 2017 -0.5587 -0.6453 -0.4721 -197 2018 -0.5858 -0.672 -0.4997 -197 2019 -0.6107 -0.6984 -0.523 -197 2020 -0.5219 -0.6085 -0.4352 -197 2021 -0.6035 -0.6889 -0.5181 -197 2022 -0.715 -0.8093 -0.6206 -197 2023 -0.6826 -0.7738 -0.5913 -198 2007 -0.1099 -0.1641 -0.0557 -198 2010 -0.0115 -0.0661 0.0431 -198 2013 0.095 0.04 0.1501 -198 2016 0.0638 0.0097 0.1179 -198 2019 0.0394 -0.0153 0.094 -198 2022 -0.0242 -0.0849 0.0366 -199 2007 -1.5902 -1.8149 -1.3655 -199 2010 -1.5556 -1.762 -1.3493 -199 2013 -1.5505 -1.7551 -1.3458 -199 2016 -1.5435 -1.7468 -1.3402 -199 2019 -1.5124 -1.7172 -1.3077 -199 2022 -1.5262 -1.7463 -1.306 -200 2007 -1.907 -2.0491 -1.7649 -200 2010 -1.9444 -2.0857 -1.8031 -200 2013 -1.9036 -2.0453 -1.7619 -200 2016 -1.7602 -1.8901 -1.6303 -200 2019 -1.636 -1.7742 -1.4977 -200 2022 -1.7018 -1.8637 -1.5399 -201 2007 0.6285 0.5632 0.6939 -201 2010 0.5892 0.5225 0.6559 -201 2013 0.3892 0.3197 0.4586 -201 2016 0.264 0.1955 0.3325 -201 2019 0.3698 0.3019 0.4378 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355 - - 356 - - 357 - - 358 - - 359 - - 360 - - 361 - - 362 - - 363 - - 364 - - 365 - - 366 - - 367 - - 368 - - 369 - - 370 - - 371 - - 372 - - 373 - - 374 - - 375 - - 376 - - 377 - - 378 - - 379 - - 380 - - 381 - - 382 - - 383 - - 384 ordered: no + description: Identifier of the analysis year: class: integer + description: Year or period of the estimate estimate: class: numeric + digits: 6 + description: Estimate on the log-scale lower_confidence_limit: class: numeric + digits: 6 + description: 5% quantile of the distribution on the log-scale upper_confidence_limit: class: numeric + digits: 6 + description: 95% quantile of the distribution on the log-scale diff --git a/inst/results/index.tsv b/inst/results/index.tsv index 8e8faa0..55e5be1 100644 --- a/inst/results/index.tsv +++ b/inst/results/index.tsv @@ -1,14497 +1,20353 @@ analysis reference alternative estimate lower_confidence_limit upper_confidence_limit -193 2007 2010 -0.4585 -1.0434 0.1264 -193 2007 2013 -0.785 -1.4422 -0.1278 -193 2007 2016 0.0711 -0.4508 0.5929 -193 2007 2019 1.1581 0.7138 1.6023 -193 2007 2022 1.435 0.9619 1.908 -193 2010 2013 -0.3266 -0.945 0.2919 -193 2010 2016 0.5295 -0.0536 1.1126 -193 2010 2019 1.6166 1.0831 2.1501 -193 2010 2022 1.8935 1.326 2.4609 -193 2013 2016 0.8561 0.303 1.4091 -193 2013 2019 1.9431 1.392 2.4941 -193 2013 2022 2.22 1.6344 2.8055 -193 2016 2019 1.087 0.7191 1.4549 -193 2016 2022 1.3639 0.9436 1.7842 -193 2019 2022 0.2769 -0.0365 0.5903 -194 2007 2010 0.0465 -0.2593 0.3523 -194 2007 2013 0.1912 -0.2049 0.5873 -194 2007 2016 0.4505 -0.0492 0.9502 -194 2007 2019 0.616 0.1013 1.1306 -194 2007 2022 0.6467 0.0826 1.2108 -194 2010 2013 0.145 -0.1613 0.4512 -194 2010 2016 0.4043 -0.0661 0.8746 -194 2010 2019 0.5701 0.076 1.0643 -194 2010 2022 0.6008 0.0534 1.1482 -194 2013 2016 0.2585 -0.096 0.6131 -194 2013 2019 0.4248 0.0191 0.8306 -194 2013 2022 0.4555 -0.0158 0.9269 -194 2016 2019 0.1656 -0.1059 0.437 -194 2016 2022 0.1962 -0.171 0.5635 -194 2019 2022 0.0307 -0.2508 0.3123 -195 2007 2010 0.2084 0.0817 0.3351 -195 2007 2013 0.0771 -0.0744 0.2286 -195 2007 2016 0.0586 -0.087 0.2042 -195 2007 2019 -0.3205 -0.4833 -0.1578 -195 2007 2022 -0.3716 -0.5838 -0.1594 -195 2010 2013 -0.1313 -0.2751 0.0124 -195 2010 2016 -0.1498 -0.2888 -0.0108 -195 2010 2019 -0.5289 -0.6953 -0.3625 -195 2010 2022 -0.58 -0.7948 -0.3653 -195 2013 2016 -0.0185 -0.1671 0.1301 -195 2013 2019 -0.3976 -0.57 -0.2253 -195 2013 2022 -0.4487 -0.6694 -0.228 -195 2016 2019 -0.3791 -0.5367 -0.2216 -195 2016 2022 -0.4302 -0.6418 -0.2186 -195 2019 2022 -0.0511 -0.2562 0.1541 -196 2007 2010 0.1383 0.0202 0.2564 -196 2007 2013 0.2811 0.1377 0.4246 -196 2007 2016 0.3982 0.2485 0.548 -196 2007 2019 0.3899 0.2457 0.5341 -196 2007 2022 0.4155 0.2549 0.576 -196 2010 2013 0.1429 0.0225 0.2632 -196 2010 2016 0.26 0.1242 0.3958 -196 2010 2019 0.2517 0.1158 0.3876 -196 2010 2022 0.2772 0.1243 0.4301 -196 2013 2016 0.1171 0.0035 0.2308 -196 2013 2019 0.1088 -0.0198 0.2374 -196 2013 2022 0.1343 -0.0116 0.2803 -196 2016 2019 -0.0083 -0.117 0.1004 -196 2016 2022 0.0172 -0.1169 0.1513 -196 2019 2022 0.0255 -0.0914 0.1425 -197 2007 2008 0.0641 -0.0158 0.144 -197 2007 2009 0.0517 -0.0332 0.1366 -197 2007 2010 7e-04 -0.0715 0.0729 -197 2007 2011 0.0393 -0.0465 0.1252 -197 2007 2012 0.011 -0.0788 0.1009 -197 2007 2013 0.0512 -0.0247 0.127 -197 2007 2014 -0.0628 -0.1501 0.0244 -197 2007 2015 0.0197 -0.0701 0.1094 -197 2007 2016 -0.1182 -0.1936 -0.0429 -197 2007 2017 -0.1389 -0.2256 -0.0523 -197 2007 2018 -0.1661 -0.2528 -0.0794 -197 2007 2019 -0.1909 -0.2705 -0.1114 -197 2007 2020 -0.1021 -0.1899 -0.0143 -197 2007 2021 -0.1837 -0.27 -0.0974 -197 2007 2022 -0.2952 -0.3827 -0.2078 -197 2007 2023 -0.2628 -0.3545 -0.1711 -197 2008 2009 -0.0124 -0.0938 0.069 -197 2008 2010 -0.0634 -0.1457 0.0189 -197 2008 2011 -0.0248 -0.0985 0.0489 -197 2008 2012 -0.0531 -0.142 0.0359 -197 2008 2013 -0.0129 -0.0976 0.0717 -197 2008 2014 -0.1269 -0.2045 -0.0493 -197 2008 2015 -0.0444 -0.1318 0.0429 -197 2008 2016 -0.1823 -0.2673 -0.0974 -197 2008 2017 -0.203 -0.279 -0.1271 -197 2008 2018 -0.2302 -0.3153 -0.145 -197 2008 2019 -0.255 -0.3426 -0.1675 -197 2008 2020 -0.1662 -0.2433 -0.0892 -197 2008 2021 -0.2478 -0.3311 -0.1645 -197 2008 2022 -0.3593 -0.4534 -0.2652 -197 2008 2023 -0.3269 -0.409 -0.2448 -197 2009 2010 -0.0509 -0.1325 0.0306 -197 2009 2011 -0.0124 -0.0992 0.0745 -197 2009 2012 -0.0406 -0.123 0.0418 -197 2009 2013 -5e-04 -0.087 0.086 -197 2009 2014 -0.1145 -0.2033 -0.0257 -197 2009 2015 -0.032 -0.1126 0.0486 -197 2009 2016 -0.1699 -0.2564 -0.0834 -197 2009 2017 -0.1906 -0.2786 -0.1027 -197 2009 2018 -0.2178 -0.2959 -0.1397 -197 2009 2019 -0.2426 -0.3322 -0.1531 -197 2009 2020 -0.1538 -0.2415 -0.0661 -197 2009 2021 -0.2354 -0.313 -0.1578 -197 2009 2022 -0.3469 -0.4432 -0.2505 -197 2009 2023 -0.3145 -0.4075 -0.2215 -197 2010 2011 0.0386 -0.0449 0.1221 -197 2010 2012 0.0103 -0.079 0.0996 -197 2010 2013 0.0504 -0.0259 0.1268 -197 2010 2014 -0.0636 -0.1509 0.0238 -197 2010 2015 0.0189 -0.0703 0.1081 -197 2010 2016 -0.119 -0.1943 -0.0436 -197 2010 2017 -0.1397 -0.2267 -0.0526 -197 2010 2018 -0.1668 -0.2525 -0.0811 -197 2010 2019 -0.1917 -0.272 -0.1114 -197 2010 2020 -0.1029 -0.1907 -0.015 -197 2010 2021 -0.1845 -0.2696 -0.0993 -197 2010 2022 -0.296 -0.3837 -0.2082 -197 2010 2023 -0.2635 -0.3553 -0.1718 -197 2011 2012 -0.0283 -0.1166 0.06 -197 2011 2013 0.0119 -0.076 0.0997 -197 2011 2014 -0.1021 -0.1829 -0.0213 -197 2011 2015 -0.0196 -0.1101 0.0709 -197 2011 2016 -0.1576 -0.2459 -0.0692 -197 2011 2017 -0.1782 -0.2576 -0.0989 -197 2011 2018 -0.2054 -0.294 -0.1168 -197 2011 2019 -0.2302 -0.3214 -0.1391 -197 2011 2020 -0.1414 -0.2219 -0.061 -197 2011 2021 -0.223 -0.3101 -0.136 -197 2011 2022 -0.3345 -0.432 -0.237 -197 2011 2023 -0.3021 -0.3877 -0.2165 -197 2012 2013 0.0401 -0.048 0.1283 -197 2012 2014 -0.0739 -0.1668 0.0191 -197 2012 2015 0.0086 -0.0779 0.0951 -197 2012 2016 -0.1293 -0.2208 -0.0378 -197 2012 2017 -0.15 -0.2428 -0.0572 -197 2012 2018 -0.1771 -0.2616 -0.0926 -197 2012 2019 -0.202 -0.2959 -0.108 -197 2012 2020 -0.1132 -0.2059 -0.0204 -197 2012 2021 -0.1948 -0.2797 -0.1098 -197 2012 2022 -0.3063 -0.4067 -0.2058 -197 2012 2023 -0.2738 -0.3713 -0.1764 -197 2013 2014 -0.114 -0.2007 -0.0273 -197 2013 2015 -0.0315 -0.1219 0.0589 -197 2013 2016 -0.1694 -0.2469 -0.092 -197 2013 2017 -0.1901 -0.2792 -0.1011 -197 2013 2018 -0.2173 -0.3059 -0.1286 -197 2013 2019 -0.2421 -0.3253 -0.1589 -197 2013 2020 -0.1533 -0.2423 -0.0643 -197 2013 2021 -0.2349 -0.3229 -0.1469 -197 2013 2022 -0.3464 -0.437 -0.2558 -197 2013 2023 -0.314 -0.4077 -0.2203 -197 2014 2015 0.0825 -0.0071 0.1721 -197 2014 2016 -0.0554 -0.1438 0.033 -197 2014 2017 -0.0761 -0.1562 0.004 -197 2014 2018 -0.1033 -0.193 -0.0136 -197 2014 2019 -0.1281 -0.2194 -0.0368 -197 2014 2020 -0.0393 -0.1212 0.0426 -197 2014 2021 -0.1209 -0.2095 -0.0323 -197 2014 2022 -0.2324 -0.3296 -0.1352 -197 2014 2023 -0.2 -0.2861 -0.1138 -197 2015 2016 -0.1379 -0.2262 -0.0496 -197 2015 2017 -0.1586 -0.2495 -0.0677 -197 2015 2018 -0.1857 -0.2677 -0.1038 -197 2015 2019 -0.2106 -0.3041 -0.1172 -197 2015 2020 -0.1218 -0.2122 -0.0313 -197 2015 2021 -0.2034 -0.2857 -0.1211 -197 2015 2022 -0.3149 -0.4156 -0.2142 -197 2015 2023 -0.2825 -0.3788 -0.1862 -197 2016 2017 -0.0207 -0.1064 0.065 -197 2016 2018 -0.0478 -0.1358 0.0401 -197 2016 2019 -0.0727 -0.1518 0.0065 -197 2016 2020 0.0161 -0.0733 0.1056 -197 2016 2021 -0.0655 -0.1531 0.0221 -197 2016 2022 -0.177 -0.2638 -0.0901 -197 2016 2023 -0.1446 -0.2375 -0.0516 -197 2017 2018 -0.0271 -0.1129 0.0586 -197 2017 2019 -0.052 -0.1428 0.0388 -197 2017 2020 0.0368 -0.042 0.1157 -197 2017 2021 -0.0448 -0.1319 0.0423 -197 2017 2022 -0.1563 -0.2535 -0.059 -197 2017 2023 -0.1239 -0.2081 -0.0397 -197 2018 2019 -0.0249 -0.1118 0.0621 -197 2018 2020 0.064 -0.0243 0.1522 -197 2018 2021 -0.0176 -0.0949 0.0596 -197 2018 2022 -0.1291 -0.2258 -0.0325 -197 2018 2023 -0.0967 -0.1903 -0.0031 -197 2019 2020 0.0888 0 0.1776 -197 2019 2021 0.0072 -0.0821 0.0965 -197 2019 2022 -0.1043 -0.1914 -0.0172 -197 2019 2023 -0.0719 -0.1669 0.0232 -197 2020 2021 -0.0816 -0.1658 0.0025 -197 2020 2022 -0.1931 -0.2911 -0.095 -197 2020 2023 -0.1607 -0.2452 -0.0762 -197 2021 2022 -0.1115 -0.2035 -0.0195 -197 2021 2023 -0.0791 -0.1702 0.0121 -197 2022 2023 0.0324 -0.0631 0.1279 -198 2007 2010 0.0984 0.0579 0.139 -198 2007 2013 0.2049 0.1622 0.2477 -198 2007 2016 0.1737 0.1322 0.2152 -198 2007 2019 0.1493 0.1066 0.1919 -198 2007 2022 0.0857 0.0346 0.1369 -198 2010 2013 0.1065 0.0644 0.1486 -198 2010 2016 0.0753 0.0332 0.1173 -198 2010 2019 0.0508 0.0076 0.094 -198 2010 2022 -0.0127 -0.0646 0.0392 -198 2013 2016 -0.0312 -0.0725 0.01 -198 2013 2019 -0.0557 -0.0987 -0.0126 -198 2013 2022 -0.1192 -0.171 -0.0674 -198 2016 2019 -0.0244 -0.0642 0.0154 -198 2016 2022 -0.088 -0.1373 -0.0386 -198 2019 2022 -0.0635 -0.1115 -0.0155 -199 2007 2010 0.0345 -0.0894 0.1584 -199 2007 2013 0.0398 -0.1095 0.1891 -199 2007 2016 0.0468 -0.1135 0.2072 -199 2007 2019 0.078 -0.0982 0.2543 -199 2007 2022 0.0643 -0.1336 0.2622 -199 2010 2013 0.0053 -0.1109 0.1214 -199 2010 2016 0.0122 -0.1272 0.1516 -199 2010 2019 0.0433 -0.1104 0.197 -199 2010 2022 0.0296 -0.1534 0.2126 -199 2013 2016 0.0071 -0.1079 0.122 -199 2013 2019 0.0381 -0.1039 0.1802 -199 2013 2022 0.0243 -0.1499 0.1985 -199 2016 2019 0.0311 -0.0863 0.1485 -199 2016 2022 0.0173 -0.1419 0.1765 -199 2019 2022 -0.0137 -0.1434 0.116 -200 2007 2010 -0.0374 -0.177 0.1021 -200 2007 2013 0.0033 -0.1535 0.1602 -200 2007 2016 0.1468 -0.0057 0.2993 -200 2007 2019 0.271 0.1092 0.4328 -200 2007 2022 0.2052 0.021 0.3893 -200 2010 2013 0.0408 -0.0933 0.1748 -200 2010 2016 0.1842 0.0346 0.3338 -200 2010 2019 0.3084 0.1383 0.4786 -200 2010 2022 0.2426 0.0612 0.4239 -200 2013 2016 0.1434 0.0066 0.2803 -200 2013 2019 0.2677 0.1006 0.4348 -200 2013 2022 0.2018 0.0223 0.3813 -200 2016 2019 0.1243 -0.0053 0.2539 -200 2016 2022 0.0584 -0.1092 0.226 -200 2019 2022 -0.0659 -0.2217 0.0898 -201 2007 2010 -0.0393 -0.0857 0.0071 -201 2007 2013 -0.2394 -0.2913 -0.1874 -201 2007 2016 -0.3645 -0.4155 -0.3136 -201 2007 2019 -0.2587 -0.3101 -0.2074 -201 2007 2022 -0.2741 -0.3378 -0.2104 -201 2010 2013 -0.2001 -0.2528 -0.1474 -201 2010 2016 -0.3252 -0.378 -0.2725 -201 2010 2019 -0.2194 -0.2725 -0.1663 -201 2010 2022 -0.2348 -0.3 -0.1697 -201 2013 2016 -0.1252 -0.1779 -0.0725 -201 2013 2019 -0.0193 -0.0741 0.0354 -201 2013 2022 -0.0348 -0.1008 0.0313 -201 2016 2019 0.1058 0.0548 0.1569 -201 2016 2022 0.0904 0.0266 0.1542 -201 2019 2022 -0.0154 -0.0767 0.0458 -202 2007 2008 0.1872 0.0865 0.288 -202 2007 2009 0.2625 0.1546 0.3704 -202 2007 2010 0.4029 0.3079 0.4979 -202 2007 2011 0.5026 0.3961 0.6092 -202 2007 2012 0.4955 0.3863 0.6047 -202 2007 2013 0.5871 0.4877 0.6864 -202 2007 2014 0.5027 0.3943 0.6112 -202 2007 2015 0.6488 0.5401 0.7576 -202 2007 2016 0.7007 0.6029 0.7985 -202 2007 2017 0.6604 0.5542 0.7667 -202 2007 2018 0.7149 0.6074 0.8224 -202 2007 2019 0.7779 0.6791 0.8768 -202 2007 2020 0.7067 0.5988 0.8147 -202 2007 2021 0.7677 0.6621 0.8734 -202 2007 2022 0.9565 0.852 1.061 -202 2007 2023 0.7526 0.6415 0.8636 -202 2008 2009 0.0753 -0.0246 0.1751 -202 2008 2010 0.2157 0.1151 0.3162 -202 2008 2011 0.3154 0.2234 0.4074 -202 2008 2012 0.3083 0.2031 0.4134 -202 2008 2013 0.3998 0.296 0.5037 -202 2008 2014 0.3155 0.2202 0.4109 -202 2008 2015 0.4616 0.3581 0.5652 -202 2008 2016 0.5135 0.4114 0.6156 -202 2008 2017 0.4732 0.3812 0.5652 -202 2008 2018 0.5277 0.4253 0.63 -202 2008 2019 0.5907 0.4879 0.6935 -202 2008 2020 0.5195 0.425 0.614 -202 2008 2021 0.5805 0.4811 0.68 -202 2008 2022 0.7693 0.6619 0.8767 -202 2008 2023 0.5653 0.4676 0.6631 -202 2009 2010 0.1404 0.0422 0.2386 -202 2009 2011 0.2401 0.1357 0.3445 -202 2009 2012 0.233 0.1361 0.3299 -202 2009 2013 0.3246 0.219 0.4301 -202 2009 2014 0.2402 0.1342 0.3462 -202 2009 2015 0.3863 0.2903 0.4823 -202 2009 2016 0.4382 0.335 0.5415 -202 2009 2017 0.3979 0.2943 0.5015 -202 2009 2018 0.4524 0.3585 0.5463 -202 2009 2019 0.5154 0.4111 0.6198 -202 2009 2020 0.4442 0.3388 0.5496 -202 2009 2021 0.5052 0.4117 0.5988 -202 2009 2022 0.694 0.5844 0.8036 -202 2009 2023 0.4901 0.3806 0.5995 -202 2010 2011 0.0997 0.0013 0.1982 -202 2010 2012 0.0926 -0.0116 0.1968 -202 2010 2013 0.1842 0.0916 0.2768 -202 2010 2014 0.0998 -0.0048 0.2045 -202 2010 2015 0.246 0.1426 0.3493 -202 2010 2016 0.2978 0.207 0.3887 -202 2010 2017 0.2575 0.1555 0.3596 -202 2010 2018 0.312 0.2102 0.4139 -202 2010 2019 0.3751 0.2818 0.4683 -202 2010 2020 0.3038 0.1999 0.4078 -202 2010 2021 0.3649 0.265 0.4647 -202 2010 2022 0.5536 0.455 0.6522 -202 2010 2023 0.3497 0.2423 0.457 -202 2011 2012 -0.0071 -0.1084 0.0941 -202 2011 2013 0.0845 -0.0199 0.1888 -202 2011 2014 1e-04 -0.0956 0.0959 -202 2011 2015 0.1462 0.0419 0.2506 -202 2011 2016 0.1981 0.0954 0.3008 -202 2011 2017 0.1578 0.0661 0.2495 -202 2011 2018 0.2123 0.1089 0.3156 -202 2011 2019 0.2753 0.1716 0.3791 -202 2011 2020 0.2041 0.1084 0.2998 -202 2011 2021 0.2651 0.1642 0.3661 -202 2011 2022 0.4539 0.3456 0.5622 -202 2011 2023 0.2499 0.1506 0.3492 -202 2012 2013 0.0916 -0.0106 0.1938 -202 2012 2014 0.0072 -0.0993 0.1137 -202 2012 2015 0.1533 0.057 0.2497 -202 2012 2016 0.2052 0.1006 0.3099 -202 2012 2017 0.1649 0.0605 0.2694 -202 2012 2018 0.2194 0.1244 0.3144 -202 2012 2019 0.2824 0.1774 0.3875 -202 2012 2020 0.2112 0.1051 0.3174 -202 2012 2021 0.2722 0.1772 0.3673 -202 2012 2022 0.461 0.3506 0.5714 -202 2012 2023 0.2571 0.1467 0.3674 -202 2013 2014 -0.0843 -0.1869 0.0182 -202 2013 2015 0.0618 -0.0431 0.1666 -202 2013 2016 0.1137 0.0221 0.2053 -202 2013 2017 0.0734 -0.0309 0.1776 -202 2013 2018 0.1278 0.0234 0.2323 -202 2013 2019 0.1909 0.0962 0.2856 -202 2013 2020 0.1197 0.0137 0.2256 -202 2013 2021 0.1807 0.0781 0.2833 -202 2013 2022 0.3694 0.2706 0.4682 -202 2013 2023 0.1655 0.0563 0.2747 -202 2014 2015 0.1461 0.0446 0.2477 -202 2014 2016 0.198 0.0937 0.3023 -202 2014 2017 0.1577 0.0654 0.25 -202 2014 2018 0.2122 0.1084 0.316 -202 2014 2019 0.2752 0.1695 0.381 -202 2014 2020 0.204 0.1095 0.2985 -202 2014 2021 0.265 0.1637 0.3663 -202 2014 2022 0.4538 0.3429 0.5646 -202 2014 2023 0.2498 0.1513 0.3484 -202 2015 2016 0.0519 -0.047 0.1508 -202 2015 2017 0.0116 -0.0909 0.1141 -202 2015 2018 0.0661 -0.0255 0.1576 -202 2015 2019 0.1291 0.0263 0.2319 -202 2015 2020 0.0579 -0.0469 0.1627 -202 2015 2021 0.1189 0.0265 0.2113 -202 2015 2022 0.3077 0.1997 0.4157 -202 2015 2023 0.1037 -0.0052 0.2126 -202 2016 2017 -0.0403 -0.1385 0.0579 -202 2016 2018 0.0142 -0.0872 0.1155 -202 2016 2019 0.0772 -0.0123 0.1668 -202 2016 2020 0.006 -0.0973 0.1093 -202 2016 2021 0.067 -0.0328 0.1668 -202 2016 2022 0.2558 0.161 0.3505 -202 2016 2023 0.0518 -0.0546 0.1583 -202 2017 2018 0.0545 -0.043 0.1519 -202 2017 2019 0.1175 0.0153 0.2198 -202 2017 2020 0.0463 -0.0435 0.1361 -202 2017 2021 0.1073 0.01 0.2047 -202 2017 2022 0.2961 0.1888 0.4034 -202 2017 2023 0.0921 -5e-04 0.1848 -202 2018 2019 0.063 -0.0353 0.1614 -202 2018 2020 -0.0082 -0.1102 0.0938 -202 2018 2021 0.0528 -0.0332 0.1389 -202 2018 2022 0.2416 0.1349 0.3483 -202 2018 2023 0.0377 -0.0692 0.1445 -202 2019 2020 -0.0712 -0.1714 0.029 -202 2019 2021 -0.0102 -0.1096 0.0892 -202 2019 2022 0.1785 0.0868 0.2703 -202 2019 2023 -0.0254 -0.1325 0.0818 -202 2020 2021 0.061 -0.035 0.1571 -202 2020 2022 0.2498 0.1416 0.358 -202 2020 2023 0.0458 -0.0486 0.1402 -202 2021 2022 0.1888 0.0881 0.2894 -202 2021 2023 -0.0152 -0.1177 0.0873 -202 2022 2023 -0.2039 -0.3109 -0.097 -203 2007 2008 -0.0084 -0.1019 0.0852 -203 2007 2009 0.0024 -0.113 0.1178 -203 2007 2010 0.0472 -0.0743 0.1688 -203 2007 2011 0.1086 -0.0222 0.2393 -203 2007 2012 0.0924 -0.042 0.2269 -203 2007 2013 0.0362 -0.0956 0.1681 -203 2007 2014 -0.0279 -0.1619 0.1061 -203 2007 2015 -0.0537 -0.1902 0.0828 -203 2007 2016 -0.0923 -0.2292 0.0445 -203 2007 2017 -0.0666 -0.2014 0.0681 -203 2007 2018 -0.0042 -0.1384 0.1301 -203 2007 2019 -0.0159 -0.1504 0.1187 -203 2007 2020 0.0028 -0.1336 0.1392 -203 2007 2021 0.0136 -0.1219 0.1491 -203 2007 2022 -0.0087 -0.1495 0.132 -203 2007 2023 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0.0453 -0.0499 0.1405 +87 2007 2010 0.1861 -0.0239 0.3961 +87 2007 2013 0.0537 -0.173 0.2804 +87 2007 2016 -0.0085 -0.2457 0.2287 +87 2007 2019 0.202 -0.0522 0.4562 +87 2007 2022 0.1478 -0.1022 0.3977 +87 2007 2025 0.1078 -0.1564 0.372 +87 2010 2013 -0.1324 -0.328 0.0632 +87 2010 2016 -0.1946 -0.4335 0.0444 +87 2010 2019 0.0159 -0.2009 0.2328 +87 2010 2022 -0.0383 -0.2741 0.1975 +87 2010 2025 -0.0783 -0.3425 0.1859 +87 2013 2016 -0.0622 -0.2597 0.1352 +87 2013 2019 0.1483 -0.0647 0.3613 +87 2013 2022 0.0941 -0.1357 0.3238 +87 2013 2025 0.0541 -0.2 0.3082 +87 2016 2019 0.2106 -0.0067 0.4278 +87 2016 2022 0.1563 -0.0777 0.3902 +87 2016 2025 0.1163 -0.145 0.3775 +87 2019 2022 -0.0543 -0.2584 0.1498 +87 2019 2025 -0.0942 -0.3545 0.166 +87 2022 2025 -0.04 -0.2647 0.1846 +88 2007 2008 -0.206 -1.5738 1.1617 +88 2007 2009 0.1352 -1.4304 1.7009 +88 2007 2010 -0.4856 -2.1354 1.1641 +88 2007 2011 -0.0726 -1.6104 1.4652 +88 2007 2012 -0.5729 -2.2326 1.0868 +88 2007 2013 0.3744 -1.2544 2.0033 +88 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Estimate of the different on the log-scale lower_confidence_limit: class: numeric + digits: 6 + description: 5% quantile of the distribution of the index upper_confidence_limit: class: numeric + digits: 6 + description: 95% quantile of the distribution of the index diff --git a/inst/results/linear_estimate.tsv b/inst/results/linear_estimate.tsv index 1d96783..98ed7b3 100644 --- a/inst/results/linear_estimate.tsv +++ b/inst/results/linear_estimate.tsv @@ -1,2209 +1,2757 @@ analysis year estimate lower_confidence_limit upper_confidence_limit -193 1 0.5002 0.3755 0.6248 -193 2 0.4505 0.3315 0.5696 -193 3 0.4008 0.2864 0.5153 -193 4 0.3512 0.2403 0.462 -193 5 0.3015 0.193 0.41 -193 6 0.2519 0.1445 0.3593 -193 7 0.2022 0.0946 0.3098 -193 8 0.1525 0.0434 0.2617 -193 9 0.1029 -0.009 0.2148 -193 10 0.0532 -0.0626 0.1691 -193 11 0.0036 -0.1173 0.1244 -193 12 -0.0461 -0.1729 0.0807 -193 13 -0.0958 -0.2293 0.0377 -193 14 -0.1454 -0.2863 -0.0045 -193 15 -0.1951 -0.344 -0.0462 -193 16 -0.2447 -0.4022 -0.0873 -193 17 -0.2944 -0.4607 -0.1281 -194 1 -0.7931 -0.9762 -0.6099 -194 2 -0.7888 -0.9641 -0.6135 -194 3 -0.7845 -0.9529 -0.6162 -194 4 -0.7803 -0.9426 -0.618 -194 5 -0.776 -0.9333 -0.6187 -194 6 -0.7717 -0.9253 -0.6182 -194 7 -0.7674 -0.9184 -0.6164 -194 8 -0.7632 -0.913 -0.6134 -194 9 -0.7589 -0.9088 -0.6089 -194 10 -0.7546 -0.9061 -0.6032 -194 11 -0.7503 -0.9046 -0.5961 -194 12 -0.7461 -0.9044 -0.5877 -194 13 -0.7418 -0.9053 -0.5783 -194 14 -0.7375 -0.9073 -0.5677 -194 15 -0.7332 -0.9102 -0.5563 -194 16 -0.729 -0.9139 -0.544 -194 17 -0.7247 -0.9184 -0.531 -195 1 -1.0469 -1.1741 -0.9197 -195 2 -1.1956 -1.3018 -1.0894 -195 3 -1.3443 -1.4452 -1.2433 -195 4 -1.4929 -1.6064 -1.3795 -195 5 -1.6416 -1.7808 -1.5025 -195 6 -1.7903 -1.9625 -1.6181 -196 1 0.1959 0.1407 0.2512 -196 2 0.222 0.169 0.2751 -196 3 0.2481 0.196 0.3003 -196 4 0.2742 0.2216 0.3268 -196 5 0.3003 0.2459 0.3548 -196 6 0.3264 0.269 0.3839 -197 1 -0.3701 -0.5071 -0.2332 -197 2 -0.528 -0.6495 -0.4064 -197 3 -0.6858 -0.8008 -0.5708 -197 4 -0.8436 -0.9622 -0.7249 -197 5 -1.0014 -1.1331 -0.8697 -197 6 -1.1592 -1.311 -1.0074 -198 1 1.1588 0.8849 1.4327 -198 2 1.1533 0.9153 1.3913 -198 3 1.1479 0.9299 1.3658 -198 4 1.1424 0.9242 1.3605 -198 5 1.1369 0.8984 1.3755 -198 6 1.1315 0.8568 1.4062 -199 1 0.1881 0.1319 0.2442 -199 2 0.1967 0.1415 0.2518 -199 3 0.2053 0.1511 0.2596 -199 4 0.214 0.1604 0.2675 -199 5 0.2226 0.1697 0.2755 -199 6 0.2312 0.1788 0.2837 -199 7 0.2399 0.1877 0.2921 -199 8 0.2485 0.1965 0.3006 -199 9 0.2572 0.2051 0.3092 -199 10 0.2658 0.2136 0.318 -199 11 0.2744 0.2219 0.327 -199 12 0.2831 0.2301 0.3361 -199 13 0.2917 0.2381 0.3453 -199 14 0.3004 0.246 0.3547 -199 15 0.309 0.2537 0.3643 -199 16 0.3176 0.2613 0.3739 -199 17 0.3263 0.2688 0.3837 -200 1 0.2227 -0.159 0.6043 -200 2 0.3015 -0.0496 0.6525 -200 3 0.3803 0.0475 0.713 -200 4 0.4591 0.1302 0.7879 -200 5 0.5379 0.198 0.8777 -200 6 0.6167 0.2523 0.981 -201 1 -1.9675 -2.4297 -1.5054 -201 2 -1.9546 -2.383 -1.5261 -201 3 -1.9416 -2.3374 -1.5458 -201 4 -1.9286 -2.2931 -1.5641 -201 5 -1.9156 -2.2507 -1.5806 -201 6 -1.9027 -2.2105 -1.5949 -201 7 -1.8897 -2.1731 -1.6063 -201 8 -1.8767 -2.1396 -1.6139 -201 9 -1.8638 -2.1106 -1.6169 -201 10 -1.8508 -2.0873 -1.6143 -201 11 -1.8378 -2.0703 -1.6054 -201 12 -1.8248 -2.0599 -1.5898 -201 13 -1.8119 -2.056 -1.5677 -201 14 -1.7989 -2.0579 -1.5399 -201 15 -1.7859 -2.0647 -1.5072 -201 16 -1.773 -2.0753 -1.4706 -201 17 -1.76 -2.089 -1.431 -202 1 -0.6821 -0.8342 -0.5299 -202 2 -0.6753 -0.8215 -0.5291 -202 3 -0.6685 -0.8093 -0.5276 -202 4 -0.6617 -0.7977 -0.5257 -202 5 -0.6548 -0.7867 -0.523 -202 6 -0.648 -0.7764 -0.5196 -202 7 -0.6412 -0.7669 -0.5155 -202 8 -0.6344 -0.7583 -0.5105 -202 9 -0.6276 -0.7505 -0.5047 -202 10 -0.6208 -0.7436 -0.498 -202 11 -0.614 -0.7375 -0.4904 -202 12 -0.6072 -0.7324 -0.482 -202 13 -0.6004 -0.728 -0.4727 -202 14 -0.5935 -0.7245 -0.4626 -202 15 -0.5867 -0.7217 -0.4518 -202 16 -0.5799 -0.7195 -0.4403 -202 17 -0.5731 -0.718 -0.4282 -203 1 -0.5113 -0.7233 -0.2993 -203 2 -0.5182 -0.7227 -0.3138 -203 3 -0.5252 -0.7229 -0.3275 -203 4 -0.5321 -0.7239 -0.3404 -203 5 -0.5391 -0.7258 -0.3524 -203 6 -0.546 -0.7287 -0.3633 -203 7 -0.553 -0.7327 -0.3732 -203 8 -0.5599 -0.7379 -0.3819 -203 9 -0.5668 -0.7442 -0.3895 -203 10 -0.5738 -0.7517 -0.3958 -203 11 -0.5807 -0.7604 -0.4011 -203 12 -0.5877 -0.7702 -0.4051 -203 13 -0.5946 -0.7811 -0.4081 -203 14 -0.6015 -0.793 -0.4101 -203 15 -0.6085 -0.8058 -0.4112 -203 16 -0.6154 -0.8195 -0.4113 -203 17 -0.6224 -0.834 -0.4108 -204 1 0.1995 -0.0081 0.4071 -204 2 0.2131 0.0143 0.4119 -204 3 0.2267 0.0359 0.4175 -204 4 0.2403 0.0565 0.4241 -204 5 0.2539 0.0761 0.4316 -204 6 0.2675 0.0946 0.4404 -204 7 0.2811 0.1118 0.4504 -204 8 0.2947 0.1277 0.4617 -204 9 0.3083 0.1422 0.4743 -204 10 0.3219 0.1553 0.4884 -204 11 0.3355 0.167 0.5039 -204 12 0.3491 0.1774 0.5208 -204 13 0.3627 0.1865 0.5389 -204 14 0.3763 0.1944 0.5582 -204 15 0.3899 0.2013 0.5785 -204 16 0.4035 0.2071 0.5998 -204 17 0.4171 0.2122 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10 -0.7677 -1.0668 -0.4686 -207 11 -0.8378 -1.1411 -0.5345 -207 12 -0.908 -1.2167 -0.5993 -207 13 -0.9781 -1.2934 -0.6629 -207 14 -1.0482 -1.3712 -0.7253 -207 15 -1.1184 -1.45 -0.7868 -207 16 -1.1885 -1.5298 -0.8473 -207 17 -1.2586 -1.6104 -0.9069 -208 1 0.0021 -0.2665 0.2706 -208 2 -0.0031 -0.2556 0.2493 -208 3 -0.0083 -0.246 0.2294 -208 4 -0.0135 -0.2381 0.2111 -208 5 -0.0187 -0.2321 0.1947 -208 6 -0.0239 -0.2284 0.1806 -208 7 -0.0291 -0.2273 0.1691 -208 8 -0.0343 -0.229 0.1604 -208 9 -0.0395 -0.2337 0.1546 -208 10 -0.0447 -0.2413 0.1519 -208 11 -0.0499 -0.2519 0.1521 -208 12 -0.0551 -0.2651 0.1549 -208 13 -0.0603 -0.2807 0.1601 -208 14 -0.0655 -0.2983 0.1673 -208 15 -0.0707 -0.3177 0.1763 -208 16 -0.0759 -0.3386 0.1868 -208 17 -0.0811 -0.3607 0.1985 -209 1 -1.3031 -1.4599 -1.1462 -209 2 -1.3901 -1.5224 -1.2577 -209 3 -1.4771 -1.5992 -1.355 -209 4 -1.5641 -1.6935 -1.4347 -209 5 -1.6511 -1.8029 -1.4994 -209 6 -1.7381 -1.922 -1.5543 -210 1 -0.0923 -0.1757 -0.0089 -210 2 0.0427 -0.0374 0.1227 -210 3 0.1776 0.0991 0.2561 -210 4 0.3126 0.2338 0.3913 -210 5 0.4475 0.3667 0.5283 -210 6 0.5825 0.498 0.6669 -211 1 -2.7714 -3.2506 -2.2921 -211 2 -2.2967 -2.6958 -1.8977 -211 3 -1.8221 -2.1569 -1.4873 -211 4 -1.3475 -1.6444 -1.0506 -211 5 -0.8729 -1.1683 -0.5774 -211 6 -0.3982 -0.7291 -0.0674 -212 1 -0.9501 -1.0571 -0.843 -212 2 -0.9798 -1.079 -0.8806 -212 3 -1.0095 -1.1052 -0.9138 -212 4 -1.0392 -1.1362 -0.9422 -212 5 -1.0689 -1.1718 -0.966 -212 6 -1.0986 -1.2112 -0.9859 -213 1 -0.5065 -0.7115 -0.3016 -213 2 -0.3863 -0.5683 -0.2043 -213 3 -0.2661 -0.4329 -0.0992 -213 4 -0.1458 -0.3076 0.016 -213 5 -0.0256 -0.1934 0.1422 -213 6 0.0947 -0.0889 0.2782 -214 1 -0.7772 -0.8343 -0.7201 -214 2 -0.721 -0.7744 -0.6676 -214 3 -0.6649 -0.7165 -0.6132 -214 4 -0.6087 -0.6608 -0.5567 -214 5 -0.5526 -0.6071 -0.498 -214 6 -0.4964 -0.5553 -0.4375 -215 1 -1.1409 -1.2061 -1.0757 -215 2 -1.1467 -1.2088 -1.0846 -215 3 -1.1525 -1.2119 -1.0931 -215 4 -1.1583 -1.2153 -1.1013 -215 5 -1.1641 -1.2191 -1.1092 -215 6 -1.1699 -1.2233 -1.1165 -215 7 -1.1757 -1.2281 -1.1234 -215 8 -1.1816 -1.2334 -1.1298 -215 9 -1.1874 -1.2391 -1.1356 -215 10 -1.1932 -1.2455 -1.1409 -215 11 -1.199 -1.2523 -1.1457 -215 12 -1.2048 -1.2596 -1.15 -215 13 -1.2106 -1.2674 -1.1538 -215 14 -1.2164 -1.2756 -1.1573 -215 15 -1.2222 -1.2841 -1.1604 -215 16 -1.228 -1.293 -1.1631 -215 17 -1.2339 -1.3021 -1.1656 -216 1 -0.8806 -1.1341 -0.6271 -216 2 -0.8721 -1.1145 -0.6298 -216 3 -0.8637 -1.0962 -0.6312 -216 4 -0.8552 -1.0794 -0.631 -216 5 -0.8468 -1.0643 -0.6292 -216 6 -0.8383 -1.051 -0.6256 -216 7 -0.8298 -1.0397 -0.62 -216 8 -0.8214 -1.0304 -0.6124 -216 9 -0.8129 -1.0231 -0.6027 -216 10 -0.8045 -1.0179 -0.591 -216 11 -0.796 -1.0147 -0.5773 -216 12 -0.7875 -1.0132 -0.5619 -216 13 -0.7791 -1.0134 -0.5448 -216 14 -0.7706 -1.015 -0.5262 -216 15 -0.7622 -1.0179 -0.5064 -216 16 -0.7537 -1.022 -0.4854 -216 17 -0.7452 -1.0271 -0.4634 -217 1 -1.3748 -1.6665 -1.0831 -217 2 -1.3276 -1.5998 -1.0553 -217 3 -1.2803 -1.5354 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0.1924 -220 8 0.1636 0.0901 0.2371 -220 9 0.2085 0.1351 0.2819 -220 10 0.2535 0.18 0.327 -220 11 0.2984 0.2246 0.3723 -220 12 0.3434 0.269 0.4178 -220 13 0.3884 0.3132 0.4635 -220 14 0.4333 0.3573 0.5094 -220 15 0.4783 0.4011 0.5555 -220 16 0.5233 0.4447 0.6018 -220 17 0.5682 0.4882 0.6483 -221 1 0.1634 -0.2354 0.5623 -221 2 0.1912 -0.1953 0.5776 -221 3 0.2189 -0.1563 0.5941 -221 4 0.2467 -0.1184 0.6117 -221 5 0.2744 -0.0818 0.6306 -221 6 0.3022 -0.0466 0.6509 -221 7 0.3299 -0.0128 0.6727 -221 8 0.3577 0.0193 0.696 -221 9 0.3854 0.0499 0.7209 -221 10 0.4132 0.0789 0.7475 -221 11 0.4409 0.1062 0.7757 -221 12 0.4687 0.1318 0.8056 -221 13 0.4964 0.1558 0.8371 -221 14 0.5242 0.1782 0.8701 -221 15 0.5519 0.1992 0.9047 -221 16 0.5797 0.2187 0.9407 -221 17 0.6074 0.2369 0.978 -222 1 -1.3242 -1.5618 -1.0867 -222 2 -1.2755 -1.4761 -1.0749 -222 3 -1.2268 -1.4043 -1.0493 -222 4 -1.178 -1.3518 -1.0043 -222 5 -1.1293 -1.32 -0.9387 -222 6 -1.0806 -1.304 -0.8571 -223 1 -0.6925 -0.8131 -0.572 -223 2 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b/inst/results/linear_estimate.yml index 281636b..87b9d34 100644 --- a/inst/results/linear_estimate.yml +++ b/inst/results/linear_estimate.yml @@ -1,208 +1,422 @@ ..generic: - git2rdata: 0.4.0 + git2rdata: 0.5.2 optimize: yes NA string: NA sorting: - analysis - year - hash: e01f6fbac586e6aa164783d5a69d8220ce68b0d2 - data_hash: 92fa793ab165c82bd8807748b853cf0b8f0f1356 + hash: b18b880db3b07241f3dd0e0880de0db8b3dddef0 + data_hash: 941128afd4b9ecc552bd60a33beeff83969f3e8d + name: linear_estimate + title: Estimates along a linear trend analysis: class: factor labels: - - 02650104e02b12a9a70a250a6b9d125f09fb1e16 - - 042bbc7494ef5cb0ca7e6465e9f6d6c0141647a7 - - 0531c19f799288421c7093bc1a7e8739d91a6780 - - 057e825aff074e168523706c03897e0777df9b94 - - 062854d7ba0b4411e1d472c2fd4d00ea44d9d6cc - - 06f8fbd96827ebcc2af6cd68eb9d01db513af0cc - - 072d449dc164e98c19805f67ee6a5519b780fd2d - - 07abe67565ef00ca65939290f57b7f8fed0ef3e9 - - 09522a037bb7d4eb4eb30d98b17266dc6982e402 - - 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6 + description: Estimate on the log-scale lower_confidence_limit: class: numeric + digits: 6 + description: 5% quantile of the distribution on the log-scale upper_confidence_limit: class: numeric + digits: 6 + description: 95% quantile of the distribution on the log-scale diff --git a/inst/results/linear_trend.tsv b/inst/results/linear_trend.tsv index 41d6125..8032817 100644 --- a/inst/results/linear_trend.tsv +++ b/inst/results/linear_trend.tsv @@ -1,193 +1,213 @@ analysis estimate lower_confidence_limit upper_confidence_limit -193 -0.0497 -0.0616 -0.0378 -194 0.0043 -0.01 0.0186 -195 -0.1487 -0.1922 -0.1051 -196 0.0261 0.0175 0.0347 -197 -0.1578 -0.1926 -0.123 -198 -0.0055 -0.0735 0.0625 -199 0.0086 0.0058 0.0115 -200 0.0788 0.0084 0.1492 -201 0.013 -0.0266 0.0525 -202 0.0068 -0.0036 0.0173 -203 -0.0069 -0.0214 0.0075 -204 0.0136 -0.0017 0.0289 -205 -0.0022 -0.0185 0.0141 -206 0.1859 0.0618 0.3101 -207 -0.0701 -0.0901 -0.0502 -208 -0.0052 -0.0294 0.019 -209 -0.087 -0.1342 -0.0398 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e035825ded33b5474dc53c024cceb78665f00a3e 1 NA +fa205799e4cf7cbb0d5ad3a36a1e70f2582c6f97 9 0 1 1 2 c2d296e659834bf6af80db60556f271e9c15a466 1 30683.9 +fa6e52586a6d67d538cd5b179fb8ea7e578a62b2 98 0 1 0 4 a9df8bb6c7bafc115d9746b337c7c19db356a90c 1 136649 +fb18ef4f69b9f05ac4dad817fa605a515e810dc2 71 0 1 1 4 1c0e9aa382c60dafef5fe940204c275f8f8ed35d 1 14903 +fb89e3e9449e537e52e8fb45d559faf3274c7f26 43 0 1 1 2 3f4302f38dff36cbc223de795880d96e05288855 1 1689.52 +fbdd47bc5a324f13dce24db29cc0f3ed5cd68a5c 20 0 0 1 4 7e2a4860b992f62bbed7e5067e9e975237cd32ee 1 2299.89 +fc4608a9294fe6e005d713b6a15f6bf67604fd06 100 1 0 0 NA 4d55856b3e9aa561bf0e7305e6a02a9aac5fe339 1 NA +fc48bd34232b627732e407f28b9842b408a5fb97 69 0 0 1 2 15bd3d88cdeae71e9c9e2bcb3b2dced540189480 1 1273.14 +fdae76b494dbd0612d322f92c38e50b39d96a347 109 0 1 0 1 dd0c77250581f9cd7597e756cf38da8a840b0702 1 162212 +fdb8661f101d181e2b3370da1993cba602b11a17 28 0 0 0 1 3ee8f86f9fda63ac153c2303daeb15afb43e0389 1 18233.1 +fed35b5ac3a447c9e052324d405ad042251c1efb 70 1 1 1 NA 6b2329a32c186d035b87bf53a6d8647ba94dd342 1 NA +fee66a08131b8425fcb126f2f7d604d995b9ca2c 4 0 0 0 2 29077cf6adc37618e68a0b1dcf573a63c66de0b1 1 5128.78 +ffb0f3581d8c7829b34504d014ae19216737b149 10 0 1 1 1 d4074edbbb179c48c4c177232d65bed1d17ea7fb 1 7097.21 diff --git a/inst/results/meta.yml b/inst/results/meta.yml index 538483f..fd925b0 100644 --- a/inst/results/meta.yml +++ b/inst/results/meta.yml @@ -1,159 +1,100 @@ ..generic: - git2rdata: 0.4.0 + git2rdata: 0.5.2 optimize: yes NA string: NA sorting: analysis - hash: e67aef470f6aed259ddfe60722fccc87abfa042e - data_hash: 485e6d3fc42509b00f8a4a1fd944bfed0d230b57 + hash: d5d627e772110659c5858415d21283355df3d99f + data_hash: 91cd3b3bd4c194a523daef6bf98e977dc255796d + name: meta + title: Metadata on the analyses analysis: class: character + description: Unique identifier of the analysis speciesgroup: class: factor labels: - Aalscholver - Appelvink - - Baardgrasmus - - Baardmannetje - Bergeend - Blauwborst - - Blauwe kiekendief - Blauwe reiger - Boerenzwaluw - - Bontbekplevier - - Bonte strandloper - Bonte vliegenvanger - Boomklever - Boomkruiper - Boomleeuwerik - Boompieper - - Boomvalk - Bos - Bosrietzanger - - Bosuil - Braamsluiper - Brandgans - Bruine kiekendief - - Buidelmees - Buizerd - Canadese gans - - Casarca - Cetti's zanger - - Common Redpoll - Dodaars - - Dwergstern - Ekster - - Engelse gele kwikstaart - - Europese kanarie - Fazant - Fitis - - Fluiter - Fuut - Gaai - Geelgors - Gekraagde roodstaart - Gele kwikstaart - Generalist - - Geoorde fuut - Gierzwaluw - Glanskop - Goudhaan - - Goudplevier - Goudvink - Grasmus - Graspieper - - Graszanger - Grauwe gans - - Grauwe gors - - Grauwe klauwier - - Grauwe vliegenvanger - Groene specht - Groenling - Grote bonte specht - - Grote gele kwikstaart - - Grote karekiet - Grote lijster - - Grote stern - Grutto - Halsbandparkiet - Havik - Heggenmus - Holenduif - - Hop - Houtduif - - Houtsnip - Huismus - Huiszwaluw - Ijsvogel - - Indische gans - Kauw - - Keep - - Kerkuil - Kievit - - Klapekster - Kleine bonte specht - Kleine karekiet - Kleine mantelmeeuw - - Kleine plevier - - Kleine zilverreiger - - Kluut - Kneu - Knobbelzwaan - Koekoek - Kokmeeuw - - Kolgans - Koolmees - Krakeend - - Kramsvogel - - Krooneend - - Kruisbek - Kuifeend - - Kuifleeuwerik - Kuifmees - Kwartel - Landbouw - - Lepelaar - - Lesser Redpoll - - Mandarijneend - Matkop - Meerkoet - Merel - Middelste Bonte Specht - - Monniksparkiet - - Muskuseend - Nachtegaal - - Nachtzwaluw - NARA2020 - Nijlgans - - Oehoe - - Oeverloper - Oeverzwaluw - - Ooievaar - - Orpheusspotvogel - - Paapje - Patrijs - - Pijlstaart - Pimpelmees - - Porseleinhoen - Putter - - Raaf - - Ransuil - Rietgors - Rietzanger - Ringmus - - Rode wouw - Roek - - Roerdomp - Roodborst - Roodborsttapuit - - Rouwkwikstaart - Scholekster - - Sijs - - Slechtvalk - - Slobeend - - Smient - - Snor - - Soepeend - - Soepgans - Sperwer - Spotvogel - Spreeuw @@ -161,47 +102,30 @@ speciesgroup: - Staartmees - Stadsduif - Standvogel - - Steenuil - - Stormmeeuw - SubSaharaMigrant - Tafeleend - - Taigaboomkruiper - - Tapuit - Tjiftjaf - Torenvalk - Tuinfluiter - Tureluur - Turkse tortel - Veldleeuwerik - - Velduil - Vink - - Visdief - - Vuurgoudhaantje - Waterhoen - - Waterral - - Watersnip - Watervogel - - Wespendief - Wielewaal - Wilde eend - Winterkoning - Wintertaling - Witte kwikstaart - - Woudaap - Wulp - Zanglijster - Zilvermeeuw - - Zomertaling - - Zomertortel - - Zwaangans - Zwarte kraai - Zwarte mees - Zwarte roodstaart - Zwarte specht - - Zwarte wouw - - Zwarte zwaan - Zwartkop - - Zwartkopmeeuw index: - 1 - 2 @@ -316,92 +240,17 @@ speciesgroup: - 111 - 112 - 113 - - 114 - - 115 - - 116 - - 117 - - 118 - - 119 - - 120 - - 121 - - 122 - - 123 - - 124 - - 125 - - 192 - - 126 - - 127 - - 128 - - 129 - - 130 - - 131 - - 132 - - 133 - - 134 - - 135 - - 136 - - 137 - - 138 - - 139 - - 140 - - 141 - - 142 - - 143 - - 144 - - 145 - - 146 - - 147 - - 148 - - 149 - - 150 - - 151 - - 152 - - 153 - - 154 - - 155 - - 156 - - 157 - - 158 - - 159 - - 160 - - 161 - - 162 - - 163 - - 164 - - 165 - - 166 - - 167 - - 168 - - 169 - - 170 - - 171 - - 172 - - 173 - - 174 - - 175 - - 176 - - 177 - - 178 - - 179 - - 180 - - 181 - - 182 - - 183 - - 184 - - 185 - - 186 - - 187 - - 188 - - 189 - - 190 - - 191 ordered: no + description: Name of the species or species group composite: class: logical + description: TRUE indicates a species group, FALSE a species cycle: class: logical + description: TRUE indicates a three year cycle, FALSE yearly cycle linear: class: logical + description: TRUE indicates a linear trend, FALSE a nonlinear trend family: class: factor labels: @@ -417,16 +266,17 @@ family: - 4 - 5 ordered: no + description: Statistical distribution used in the analysis status_fingerprint: class: character + description: Status fingerprint of the analysis status: class: factor - labels: - - converged - - insufficient_data - index: - - 1 - - 2 + labels: converged + index: 1 ordered: no + description: Status of the analysis waic: class: numeric + digits: 6 + description: Wantabe-Akaike Information Criterion diff --git a/inst/results/moving_trend.tsv b/inst/results/moving_trend.tsv index 9bba6e4..daa7920 100644 --- a/inst/results/moving_trend.tsv +++ b/inst/results/moving_trend.tsv @@ -1,2689 +1,3605 @@ analysis midpoint window estimate lower_confidence_limit upper_confidence_limit -193 2007.5 2 -0.4585 -1.0434 0.1264 -193 2008.5 2 -0.3266 -0.945 0.2919 -193 2009.5 2 0.8561 0.303 1.4091 -193 2010.5 2 1.087 0.7191 1.4549 -193 2011.5 2 0.2769 -0.0365 0.5903 -193 2008.5 4 -0.0113 -0.1904 0.1677 -193 2009.5 4 0.5706 0.3904 0.7508 -193 2010.5 4 0.7747 0.5889 0.9605 -193 2009.5 6 0.368 0.2774 0.4587 -194 2007.5 2 0.0465 -0.2593 0.3523 -194 2008.5 2 0.145 -0.1613 0.4512 -194 2009.5 2 0.2585 -0.096 0.6131 -194 2010.5 2 0.1656 -0.1059 0.437 -194 2011.5 2 0.0307 -0.2508 0.3123 -194 2008.5 4 0.1497 -0.0181 0.3174 -194 2009.5 4 0.197 0.0238 0.3702 -194 2010.5 4 0.1532 2e-04 0.3063 -194 2009.5 6 0.1487 0.0277 0.2697 -195 2007.5 2 0.2084 0.0817 0.3351 -195 2008.5 2 -0.1313 -0.2751 0.0124 -195 2009.5 2 -0.0185 -0.1671 0.1301 -195 2010.5 2 -0.3791 -0.5367 -0.2216 -195 2011.5 2 -0.0511 -0.2562 0.1541 -195 2008.5 4 0.0044 -0.0429 0.0518 -195 2009.5 4 -0.1605 -0.2136 -0.1074 -195 2010.5 4 -0.1725 -0.2428 -0.1022 -195 2009.5 6 -0.099 -0.1355 -0.0624 -196 2007.5 2 0.1383 0.0202 0.2564 -196 2008.5 2 0.1429 0.0225 0.2632 -196 2009.5 2 0.1171 0.0035 0.2308 -196 2010.5 2 -0.0083 -0.117 0.1004 -196 2011.5 2 0.0255 -0.0914 0.1425 -196 2008.5 4 0.1338 0.0835 0.184 -196 2009.5 4 0.0872 0.0419 0.1325 -196 2010.5 4 0.0395 -0.0082 0.0872 -196 2009.5 6 0.0843 0.054 0.1146 -197 2009.5 6 -0.002 -0.0166 0.0126 -197 2010.5 6 -0.0042 -0.0185 0.01 -197 2011.5 6 -0.0128 -0.0274 0.0017 -197 2012.5 6 -0.0049 -0.0197 0.0099 -197 2013.5 6 -0.025 -0.0399 -0.0101 -197 2014.5 6 -0.0336 -0.0488 -0.0184 -197 2015.5 6 -0.0415 -0.0562 -0.0268 -197 2016.5 6 -0.0348 -0.0499 -0.0197 -197 2017.5 6 -0.0244 -0.0393 -0.0095 -197 2018.5 6 -0.0069 -0.0214 0.0076 -197 2019.5 6 -0.0213 -0.0369 -0.0057 -197 2020.5 6 -0.0251 -0.041 -0.0092 -197 2012.5 12 -0.0175 -0.0226 -0.0125 -197 2013.5 12 -0.0231 -0.0283 -0.018 -197 2014.5 12 -0.0211 -0.0263 -0.0159 -197 2015.5 12 -0.0216 -0.0268 -0.0163 -197 2016.5 12 -0.027 -0.0326 -0.0214 -197 2017.5 12 -0.0274 -0.0331 -0.0216 -197 2015 17 -0.0207 -0.0239 -0.0174 -198 2007.5 2 0.0984 0.0579 0.139 -198 2008.5 2 0.1065 0.0644 0.1486 -198 2009.5 2 -0.0312 -0.0725 0.01 -198 2010.5 2 -0.0244 -0.0642 0.0154 -198 2011.5 2 -0.0635 -0.1115 -0.0155 -198 2008.5 4 0.0628 0.0493 0.0762 -198 2009.5 4 0.0121 -0.0018 0.026 -198 2010.5 4 -0.0382 -0.0545 -0.0219 -198 2009.5 6 0.0157 0.007 0.0245 -199 2007.5 2 0.0345 -0.0894 0.1584 -199 2008.5 2 0.0053 -0.1109 0.1214 -199 2009.5 2 0.0071 -0.1079 0.122 -199 2010.5 2 0.0311 -0.0863 0.1485 -199 2011.5 2 -0.0137 -0.1434 0.116 -199 2008.5 4 0.0146 -0.0388 0.0679 -199 2009.5 4 0.0137 -0.0376 0.065 -199 2010.5 4 0.0105 -0.0473 0.0683 -199 2009.5 6 0.0131 -0.0249 0.0511 -200 2007.5 2 -0.0374 -0.177 0.1021 -200 2008.5 2 0.0408 -0.0933 0.1748 -200 2009.5 2 0.1434 0.0066 0.2803 -200 2010.5 2 0.1243 -0.0053 0.2539 -200 2011.5 2 -0.0659 -0.2217 0.0898 -200 2008.5 4 0.0481 -0.0022 0.0984 -200 2009.5 4 0.1069 0.0495 0.1642 -200 2010.5 4 0.073 0.0143 0.1316 -200 2009.5 6 0.0598 0.0257 0.094 -201 2007.5 2 -0.0393 -0.0857 0.0071 -201 2008.5 2 -0.2001 -0.2528 -0.1474 -201 2009.5 2 -0.1252 -0.1779 -0.0725 -201 2010.5 2 0.1058 0.0548 0.1569 -201 2011.5 2 -0.0154 -0.0767 0.0458 -201 2008.5 4 -0.1294 -0.146 -0.1127 -201 2009.5 4 -0.0783 -0.0955 -0.0612 -201 2010.5 4 1e-04 -0.0207 0.021 -201 2009.5 6 -0.0615 -0.0725 -0.0506 -202 2009.5 6 0.1018 0.0838 0.1199 -202 2010.5 6 0.0799 0.0624 0.0975 -202 2011.5 6 0.0499 0.0322 0.0676 -202 2012.5 6 0.0378 0.0203 0.0552 -202 2013.5 6 0.039 0.0217 0.0564 -202 2014.5 6 0.0375 0.0201 0.0548 -202 2015.5 6 0.0333 0.016 0.0505 -202 2016.5 6 0.0438 0.0266 0.0611 -202 2017.5 6 0.0164 -7e-04 0.0336 -202 2018.5 6 0.0153 -0.0011 0.0318 -202 2019.5 6 0.0448 0.0277 0.0619 -202 2020.5 6 0.0224 0.0049 0.04 -202 2012.5 12 0.0577 0.0514 0.064 -202 2013.5 12 0.048 0.0419 0.0542 -202 2014.5 12 0.0387 0.0324 0.0449 -202 2015.5 12 0.0314 0.0252 0.0377 -202 2016.5 12 0.0343 0.0279 0.0407 -202 2017.5 12 0.03 0.0235 0.0365 -202 2015 17 0.0443 0.0403 0.0482 -203 2009.5 6 0.0245 -0.0023 0.0513 -203 2010.5 6 0.0158 -0.0094 0.041 -203 2011.5 6 -0.0057 -0.0301 0.0186 -203 2012.5 6 -0.0277 -0.0532 -0.0022 -203 2013.5 6 -0.0431 -0.071 -0.0152 -203 2014.5 6 -0.0345 -0.0609 -0.008 -203 2015.5 6 -0.0102 -0.0347 0.0143 -203 2016.5 6 0.0067 -0.0187 0.0321 -203 2017.5 6 0.0164 -0.0097 0.0425 -203 2018.5 6 0.0207 -0.0051 0.0466 -203 2019.5 6 0.0103 -0.0151 0.0358 -203 2020.5 6 9e-04 -0.0267 0.0284 -203 2012.5 12 -0.0077 -0.0186 0.0031 -203 2013.5 12 -0.0086 -0.019 0.0019 -203 2014.5 12 -0.0089 -0.0194 0.0015 -203 2015.5 12 -0.0085 -0.0191 0.002 -203 2016.5 12 -0.007 -0.0179 0.0039 -203 2017.5 12 -0.0021 -0.0136 0.0094 -203 2015 17 -0.0031 -0.0103 0.0041 -204 2007.5 2 0.0862 -0.1174 0.2898 -204 2008.5 2 0.0985 -0.0962 0.2932 -204 2009.5 2 0.2156 0.0348 0.3965 -204 2010.5 2 6e-04 -0.1679 0.1692 -204 2011.5 2 0.1128 -0.0838 0.3093 -204 2008.5 4 0.1299 0.0613 0.1986 -204 2009.5 4 0.116 0.0512 0.1808 -204 2010.5 4 0.0988 0.0275 0.1701 -204 2009.5 6 0.1065 0.0641 0.149 -205 2009.5 6 0.0544 0.0205 0.0884 -205 2010.5 6 0.0482 0.014 0.0825 -205 2011.5 6 0.0049 -0.0286 0.0385 -205 2012.5 6 -0.015 -0.0476 0.0177 -205 2013.5 6 -0.0303 -0.0633 0.0027 -205 2014.5 6 -0.0142 -0.0467 0.0184 -205 2015.5 6 0.0151 -0.0171 0.0474 -205 2016.5 6 0.0508 0.0185 0.0832 -205 2017.5 6 0.0617 0.0307 0.0927 -205 2018.5 6 0.0659 0.0359 0.0959 -205 2019.5 6 0.0623 0.0319 0.0928 -205 2020.5 6 0.0573 0.0258 0.0887 -205 2012.5 12 0.0118 -0.001 0.0245 -205 2013.5 12 0.0181 0.0059 0.0304 -205 2014.5 12 0.0185 0.0064 0.0305 -205 2015.5 12 0.0208 0.0088 0.0328 -205 2016.5 12 0.0266 0.0142 0.039 -205 2017.5 12 0.0366 0.0236 0.0496 -205 2015 17 0.028 0.0201 0.0359 -206 2007.5 2 0.2483 0.1965 0.3001 -206 2008.5 2 -0.0481 -0.1016 0.0054 -206 2009.5 2 0.0805 0.0278 0.1332 -206 2010.5 2 0.0436 -0.0055 0.0927 -206 2011.5 2 -0.0405 -0.1004 0.0193 -206 2008.5 4 0.0794 0.0621 0.0967 -206 2009.5 4 0.0309 0.0138 0.0479 -206 2010.5 4 0.0294 0.0089 0.0499 -206 2009.5 6 0.0494 0.0384 0.0603 -207 2007.5 2 0.1839 0.004 0.3638 -207 2008.5 2 -0.0496 -0.207 0.1077 -207 2009.5 2 0.0338 -0.1167 0.1843 -207 2010.5 2 -0.1524 -0.3119 0.0071 -207 2011.5 2 -0.0287 -0.201 0.1436 -207 2008.5 4 0.0454 -0.0169 0.1078 -207 2009.5 4 -0.0471 -0.1081 0.0139 -207 2010.5 4 -0.0594 -0.1272 0.0083 -207 2009.5 6 -0.0153 -0.0551 0.0245 -208 2007.5 2 -0.0426 -0.1578 0.0726 -208 2008.5 2 -0.0328 -0.1409 0.0754 -208 2009.5 2 -0.027 -0.1367 0.0828 -208 2010.5 2 0.1647 0.0308 0.2986 -208 2011.5 2 0.004 -0.1088 0.1167 -208 2008.5 4 -0.034 -0.0829 0.015 -208 2009.5 4 0.0288 -0.0129 0.0705 -208 2010.5 4 0.059 0.0096 0.1083 -208 2009.5 6 0.0177 -0.0099 0.0453 -209 2007.5 2 0.0246 -0.1567 0.2058 -209 2008.5 2 -0.1301 -0.3113 0.0511 -209 2009.5 2 0.0042 -0.1633 0.1716 -209 2010.5 2 0.1375 -0.0377 0.3126 -209 2011.5 2 -0.0357 -0.2185 0.147 -209 2008.5 4 -0.0434 -0.114 0.0272 -209 2009.5 4 0.0039 -0.0593 0.067 -209 2010.5 4 0.0455 -0.0278 0.1188 -209 2009.5 6 0.0012 -0.0417 0.044 -210 2009.5 6 0.0335 -0.0167 0.0838 -210 2010.5 6 0.0274 -0.0219 0.0767 -210 2011.5 6 0.0541 0.0049 0.1032 -210 2012.5 6 0.0606 0.0116 0.1095 -210 2013.5 6 0.0587 0.0091 0.1083 -210 2014.5 6 0.0374 -0.0122 0.0871 -210 2015.5 6 0.0404 -0.0101 0.0909 -210 2016.5 6 0.0474 -0.0033 0.0982 -210 2017.5 6 0.0566 0.004 0.1092 -210 2018.5 6 0.0584 0.0049 0.1119 -210 2019.5 6 0.02 -0.0307 0.0707 -210 2020.5 6 -0.0091 -0.0617 0.0436 -210 2012.5 12 0.0442 0.0172 0.0713 -210 2013.5 12 0.0422 0.0155 0.0689 -210 2014.5 12 0.0521 0.0236 0.0807 -210 2015.5 12 0.0532 0.0239 0.0825 -210 2016.5 12 0.0414 0.013 0.0697 -210 2017.5 12 0.0309 0.0023 0.0595 -210 2015 17 0.0385 0.0168 0.0601 -211 2007.5 2 0.0868 -0.0052 0.1788 -211 2008.5 2 0.0072 -0.079 0.0934 -211 2009.5 2 0.0176 -0.0681 0.1032 -211 2010.5 2 0.0751 -0.0117 0.1619 -211 2011.5 2 -0.0186 -0.1116 0.0744 -211 2008.5 4 0.0342 -5e-04 0.0689 -211 2009.5 4 0.0317 -0.0022 0.0657 -211 2010.5 4 0.0297 -0.0087 0.0682 -211 2009.5 6 0.0331 0.0095 0.0567 -212 2009.5 6 -0.0069 -0.0325 0.0188 -212 2010.5 6 -0.0151 -0.0402 0.0101 -212 2011.5 6 -0.0583 -0.0849 -0.0318 -212 2012.5 6 -0.0702 -0.0984 -0.0421 -212 2013.5 6 -0.0462 -0.0721 -0.0203 -212 2014.5 6 -0.0154 -0.0411 0.0102 -212 2015.5 6 0.0202 -0.0055 0.0459 -212 2016.5 6 0.0462 0.0176 0.0749 -212 2017.5 6 0.0291 0.0026 0.0557 -212 2018.5 6 -0.007 -0.0322 0.0182 -212 2019.5 6 -0.0071 -0.0324 0.0182 -212 2020.5 6 -0.0129 -0.0397 0.0139 -212 2012.5 12 -0.0194 -0.0288 -0.0099 -212 2013.5 12 -0.0161 -0.0254 -0.0068 -212 2014.5 12 -0.0115 -0.0209 -0.002 -212 2015.5 12 -0.0103 -0.0199 -7e-04 -212 2016.5 12 -0.0063 -0.0161 0.0036 -212 2017.5 12 0.0032 -0.0071 0.0135 -212 2015 17 -0.0102 -0.0162 -0.0041 -213 2007.5 2 0.069 -0.2516 0.3897 -213 2008.5 2 0.3661 0.05 0.6822 -213 2009.5 2 0.2194 -0.0633 0.502 -213 2010.5 2 0.0724 -0.2543 0.399 -213 2011.5 2 0.1491 -0.2272 0.5254 -213 2008.5 4 0.2329 0.106 0.3599 -213 2009.5 4 0.2193 0.0776 0.361 -213 2010.5 4 0.1395 -0.0204 0.2994 -213 2009.5 6 0.1878 0.0859 0.2897 -214 2007.5 2 0.0467 -0.1321 0.2255 -214 2008.5 2 0.0189 -0.1499 0.1877 -214 2009.5 2 0.0681 -0.1013 0.2374 -214 2010.5 2 0.0637 -0.1072 0.2345 -214 2011.5 2 -0.0298 -0.2173 0.1576 -214 2008.5 4 0.042 -0.0398 0.1238 -214 2009.5 4 0.052 -0.03 0.1341 -214 2010.5 4 0.0369 -0.049 0.1227 -214 2009.5 6 0.0388 -0.0213 0.0989 -215 2007.5 2 0.0411 -0.146 0.2282 -215 2008.5 2 0.0249 -0.1313 0.1811 -215 2009.5 2 0.0247 -0.1282 0.1776 -215 2010.5 2 -0.0147 -0.1697 0.1402 -215 2011.5 2 -0.0326 -0.2292 0.164 -215 2008.5 4 0.0303 -0.0578 0.1183 -215 2009.5 4 0.013 -0.0581 0.0841 -215 2010.5 4 -0.0085 -0.092 0.0751 -215 2009.5 6 0.0099 -0.0439 0.0637 -216 2009.5 6 0.0473 -0.0657 0.1604 -216 2010.5 6 0.0918 -0.0158 0.1994 -216 2011.5 6 0.0854 -0.0178 0.1886 -216 2012.5 6 0.0692 -0.0314 0.1698 -216 2013.5 6 0.0512 -0.0476 0.15 -216 2014.5 6 0.0823 -0.0119 0.1765 -216 2015.5 6 0.1069 0.0077 0.2061 -216 2016.5 6 0.1233 0.0121 0.2345 -216 2017.5 6 0.0902 -0.0161 0.1965 -216 2018.5 6 0.0204 -0.0709 0.1117 -216 2019.5 6 -0.0706 -0.1776 0.0364 -216 2020.5 6 -0.1 -0.2283 0.0283 -216 2012.5 12 0.0768 0.0229 0.1307 -216 2013.5 12 0.0863 0.0312 0.1415 -216 2014.5 12 0.0836 0.0287 0.1386 -216 2015.5 12 0.07 0.0187 0.1213 -216 2016.5 12 0.0457 -0.0025 0.0938 -216 2017.5 12 0.0259 -0.026 0.0777 -216 2015 17 0.0473 0.01 0.0845 -217 2009.5 6 0.0052 -0.1251 0.1356 -217 2010.5 6 -0.011 -0.1396 0.1177 -217 2011.5 6 0.0146 -0.1054 0.1347 -217 2012.5 6 0.0566 -0.0576 0.1708 -217 2013.5 6 0.0999 -0.0143 0.2141 -217 2014.5 6 0.1335 0.0132 0.2538 -217 2015.5 6 0.1696 0.0367 0.3025 -217 2016.5 6 0.1734 0.0427 0.3041 -217 2017.5 6 0.1335 0.0209 0.2462 -217 2018.5 6 0.0857 -0.0135 0.1848 -217 2019.5 6 0.0351 -0.0606 0.1307 -217 2020.5 6 -0.0178 -0.1265 0.091 -217 2012.5 12 0.0738 0.0084 0.1392 -217 2013.5 12 0.0905 0.0277 0.1532 -217 2014.5 12 0.1008 0.0381 0.1635 -217 2015.5 12 0.105 0.0423 0.1677 -217 2016.5 12 0.1056 0.0416 0.1696 -217 2017.5 12 0.0957 0.0322 0.1591 -217 2015 17 0.0761 0.0311 0.1212 -218 2009.5 6 0.0255 -0.0208 0.0719 -218 2010.5 6 0.0323 -0.0094 0.0739 -218 2011.5 6 0.0276 -0.0129 0.0682 -218 2012.5 6 0.0237 -0.0165 0.0639 -218 2013.5 6 0.0288 -0.0109 0.0685 -218 2014.5 6 0.0367 -0.0014 0.0747 -218 2015.5 6 0.0424 0.004 0.0809 -218 2016.5 6 0.0368 -0.0018 0.0753 -218 2017.5 6 0.0269 -0.0104 0.0642 -218 2018.5 6 0.0117 -0.0244 0.0479 -218 2019.5 6 0.0102 -0.0259 0.0463 -218 2020.5 6 0.011 -0.028 0.05 -218 2012.5 12 0.0307 0.0118 0.0496 -218 2013.5 12 0.0326 0.0147 0.0505 -218 2014.5 12 0.0312 0.0138 0.0486 -218 2015.5 12 0.0265 0.0098 0.0433 -218 2016.5 12 0.0265 0.0097 0.0433 -218 2017.5 12 0.0249 0.0078 0.0419 -218 2015 17 0.0264 0.0141 0.0387 -219 2007.5 2 0.0466 -0.2052 0.2984 -219 2008.5 2 -0.0954 -0.3511 0.1604 -219 2009.5 2 -0.1495 -0.4343 0.1352 -219 2010.5 2 0.0984 -0.1959 0.3927 -219 2011.5 2 0.0047 -0.2719 0.2812 -219 2008.5 4 -0.0688 -0.1947 0.0571 -219 2009.5 4 -0.0573 -0.1713 0.0568 -219 2010.5 4 -0.0038 -0.1294 0.1218 -219 2009.5 6 -0.0297 -0.1128 0.0533 -220 2007.5 2 0.0631 -0.0571 0.1834 -220 2008.5 2 0.1511 0.0276 0.2747 -220 2009.5 2 0.0968 -0.0232 0.2167 -220 2010.5 2 0.1177 -0.0074 0.2428 -220 2011.5 2 -0.1289 -0.271 0.0133 -220 2008.5 4 0.1084 0.0517 0.1651 -220 2009.5 4 0.1193 0.0594 0.1793 -220 2010.5 4 0.0374 -0.0214 0.0962 -220 2009.5 6 0.0769 0.0353 0.1186 -221 2007.5 2 -0.1272 -0.4788 0.2245 -221 2008.5 2 -0.1157 -0.4641 0.2326 -221 2009.5 2 0.1941 -0.1338 0.522 -221 2010.5 2 0.299 -0.0103 0.6082 -221 2011.5 2 -0.018 -0.3608 0.3247 -221 2008.5 4 -0.0262 -0.1471 0.0947 -221 2009.5 4 0.1326 0.019 0.2462 -221 2010.5 4 0.1724 0.0459 0.299 -221 2009.5 6 0.0711 -3e-04 0.1424 -222 2009.5 6 0.1193 0.0478 0.1909 -222 2010.5 6 0.1116 0.042 0.1812 -222 2011.5 6 0.1004 0.021 0.1798 -222 2012.5 6 -0.0166 -0.0897 0.0564 -222 2013.5 6 -0.1062 -0.1783 -0.034 -222 2014.5 6 -0.1149 -0.1876 -0.0422 -222 2015.5 6 -0.0393 -0.1114 0.0327 -222 2016.5 6 0.0574 -0.011 0.1259 -222 2017.5 6 0.1025 0.03 0.1749 -222 2018.5 6 0.0426 -0.0302 0.1155 -222 2019.5 6 -0.0957 -0.1746 -0.0167 -222 2020.5 6 -0.1712 -0.2587 -0.0836 -222 2012.5 12 0.0163 -0.014 0.0467 -222 2013.5 12 0.0216 -0.0079 0.051 -222 2014.5 12 0.0117 -0.0176 0.0409 -222 2015.5 12 -0.0089 -0.0369 0.0192 -222 2016.5 12 -0.0357 -0.065 -0.0064 -222 2017.5 12 -0.0505 -0.0824 -0.0186 -222 2015 17 -0.008 -0.0281 0.012 -223 2009.5 6 0.0296 0.016 0.0432 -223 2010.5 6 0.0078 -0.0056 0.0212 -223 2011.5 6 -0.0022 -0.0157 0.0113 -223 2012.5 6 -0.0167 -0.0303 -0.0031 -223 2013.5 6 -0.0346 -0.0483 -0.0209 -223 2014.5 6 -0.0408 -0.0549 -0.0267 -223 2015.5 6 -0.0264 -0.0402 -0.0127 -223 2016.5 6 -0.0126 -0.0263 0.0011 -223 2017.5 6 0.0039 -0.0104 0.0181 -223 2018.5 6 0.0123 -0.0012 0.0258 -223 2019.5 6 -0.0035 -0.0175 0.0105 -223 2020.5 6 -0.0127 -0.0272 0.0018 -223 2012.5 12 -0.0087 -0.0135 -0.0039 -223 2013.5 12 -0.0117 -0.0164 -0.007 -223 2014.5 12 -0.0151 -0.0201 -0.0102 -223 2015.5 12 -0.0148 -0.0197 -0.0099 -223 2016.5 12 -0.0149 -0.02 -0.0098 -223 2017.5 12 -0.0138 -0.019 -0.0085 -223 2015 17 -0.0085 -0.0115 -0.0055 -224 2009.5 6 -0.0187 -0.0393 0.002 -224 2010.5 6 -0.0214 -0.0418 -0.0011 -224 2011.5 6 -0.0075 -0.0281 0.0131 -224 2012.5 6 0.0187 -0.002 0.0394 -224 2013.5 6 0.0456 0.0253 0.0658 -224 2014.5 6 0.0546 0.0341 0.0751 -224 2015.5 6 0.0604 0.0399 0.0809 -224 2016.5 6 0.0412 0.0211 0.0614 -224 2017.5 6 0.0301 0.0101 0.0501 -224 2018.5 6 0.027 0.0087 0.0454 -224 2019.5 6 0.0392 0.0208 0.0577 -224 2020.5 6 0.046 0.0266 0.0654 -224 2012.5 12 0.0172 0.0099 0.0245 -224 2013.5 12 0.0225 0.0154 0.0297 -224 2014.5 12 0.0308 0.0234 0.0382 -224 2015.5 12 0.0369 0.0294 0.0443 -224 2016.5 12 0.0413 0.0337 0.0489 -224 2017.5 12 0.0426 0.0348 0.0504 -224 2015 17 0.0272 0.0227 0.0318 -225 2007.5 2 -0.2552 -0.3728 -0.1376 -225 2008.5 2 -0.0393 -0.1539 0.0753 -225 2009.5 2 -0.0365 -0.1471 0.0741 -225 2010.5 2 -0.0473 -0.1574 0.0629 -225 2011.5 2 -0.0342 -0.1608 0.0924 -225 2008.5 4 -0.1032 -0.1415 -0.065 -225 2009.5 4 -0.0406 -0.0811 -1e-04 -225 2010.5 4 -0.0401 -0.085 0.0047 -225 2009.5 6 -0.0705 -0.0951 -0.0459 -226 2009.5 6 0.0813 0.0648 0.0979 -226 2010.5 6 0.0369 0.0205 0.0534 -226 2011.5 6 0.0109 -0.0054 0.0271 -226 2012.5 6 5e-04 -0.0156 0.0166 -226 2013.5 6 -0.0036 -0.0196 0.0124 -226 2014.5 6 0.009 -0.0077 0.0256 -226 2015.5 6 0.037 0.0207 0.0533 -226 2016.5 6 0.0281 0.0122 0.044 -226 2017.5 6 0.0278 0.0114 0.0442 -226 2018.5 6 0.0141 -0.0012 0.0295 -226 2019.5 6 -0.003 -0.0189 0.0128 -226 2020.5 6 -0.0272 -0.0438 -0.0106 -226 2012.5 12 0.0297 0.0239 0.0354 -226 2013.5 12 0.0252 0.0196 0.0308 -226 2014.5 12 0.0173 0.0116 0.0231 -226 2015.5 12 0.0124 0.0067 0.018 -226 2016.5 12 0.0095 0.0036 0.0154 -226 2017.5 12 0.009 0.0028 0.0153 -226 2015 17 0.0187 0.0151 0.0223 -227 2009.5 6 -0.0727 -0.136 -0.0094 -227 2010.5 6 -0.0972 -0.1588 -0.0356 -227 2011.5 6 -0.1183 -0.1815 -0.055 -227 2012.5 6 -0.1183 -0.1795 -0.0571 -227 2013.5 6 -0.1049 -0.1662 -0.0436 -227 2014.5 6 -0.0844 -0.1447 -0.0241 -227 2015.5 6 -0.0593 -0.12 0.0014 -227 2016.5 6 -0.0432 -0.1046 0.0181 -227 2017.5 6 -0.0204 -0.0826 0.0417 -227 2018.5 6 -0.0139 -0.0773 0.0496 -227 2019.5 6 -0.0042 -0.0694 0.0609 -227 2020.5 6 -1e-04 -0.0698 0.0697 -227 2012.5 12 -0.0874 -0.1175 -0.0572 -227 2013.5 12 -0.0852 -0.1142 -0.0563 -227 2014.5 12 -0.0755 -0.1029 -0.0481 -227 2015.5 12 -0.062 -0.0885 -0.0356 -227 2016.5 12 -0.0492 -0.0763 -0.0222 -227 2017.5 12 -0.0367 -0.0654 -0.008 -227 2015 17 -0.0608 -0.0804 -0.0412 -228 2009.5 6 0.043 -0.0584 0.1445 -228 2010.5 6 0.0129 -0.082 0.1078 -228 2011.5 6 -0.0186 -0.106 0.0689 -228 2012.5 6 -0.0305 -0.1201 0.059 -228 2013.5 6 -0.0668 -0.1564 0.0228 -228 2014.5 6 -0.0229 -0.1087 0.0628 -228 2015.5 6 -0.0304 -0.1138 0.0531 -228 2016.5 6 -0.0684 -0.1597 0.023 -228 2017.5 6 -0.1134 -0.2011 -0.0258 -228 2018.5 6 -0.1407 -0.2205 -0.0609 -228 2019.5 6 -0.1879 -0.2845 -0.0913 -228 2020.5 6 -0.1531 -0.2559 -0.0502 -228 2012.5 12 -0.0131 -0.0476 0.0213 -228 2013.5 12 -0.0305 -0.0649 0.0039 -228 2014.5 12 -0.0573 -0.0906 -0.0241 -228 2015.5 12 -0.064 -0.098 -0.03 -228 2016.5 12 -0.1021 -0.1392 -0.065 -228 2017.5 12 -0.0972 -0.135 -0.0595 -228 2015 17 -0.0605 -0.0839 -0.0371 -229 2007.5 2 0.0344 -0.1732 0.242 -229 2008.5 2 0.0564 -0.1503 0.263 -229 2009.5 2 -0.0534 -0.2562 0.1495 -229 2010.5 2 -0.1181 -0.3499 0.1137 -229 2011.5 2 0.1583 -0.0845 0.4011 -229 2008.5 4 0.0169 -0.0622 0.096 -229 2009.5 4 -0.0398 -0.1357 0.0561 -229 2010.5 4 -0.0158 -0.1067 0.0751 -229 2009.5 6 -3e-04 -0.056 0.0554 -230 2007.5 2 0.2176 0.1203 0.3149 -230 2008.5 2 -0.0951 -0.1897 -5e-04 -230 2009.5 2 -0.0591 -0.151 0.0328 -230 2010.5 2 -0.0893 -0.1825 0.0039 -230 2011.5 2 -0.0723 -0.1877 0.043 -230 2008.5 4 0.0095 -0.0225 0.0415 -230 2009.5 4 -0.0789 -0.1126 -0.0453 -230 2010.5 4 -0.0751 -0.1158 -0.0345 -230 2009.5 6 -0.0366 -0.0585 -0.0147 -231 2009.5 6 -0.0151 -0.0304 3e-04 -231 2010.5 6 -0.0375 -0.0529 -0.0221 -231 2011.5 6 -0.0644 -0.0807 -0.0482 -231 2012.5 6 -0.0515 -0.0674 -0.0356 -231 2013.5 6 -0.0593 -0.0753 -0.0434 -231 2014.5 6 -0.0495 -0.0658 -0.0332 -231 2015.5 6 -0.0292 -0.0457 -0.0127 -231 2016.5 6 -0.0192 -0.0355 -0.0029 -231 2017.5 6 -0.0065 -0.023 0.01 -231 2018.5 6 0.0341 0.0182 0.0499 -231 2019.5 6 0.0351 0.0185 0.0516 -231 2020.5 6 0.016 -0.001 0.0329 -231 2012.5 12 -0.041 -0.0465 -0.0355 -231 2013.5 12 -0.0386 -0.0441 -0.033 -231 2014.5 12 -0.0348 -0.0405 -0.029 -231 2015.5 12 -0.0235 -0.0292 -0.0179 -231 2016.5 12 -0.0151 -0.021 -0.0091 -231 2017.5 12 -0.0072 -0.0134 -0.001 -231 2015 17 -0.0219 -0.0255 -0.0184 -232 2009.5 6 -0.0069 -0.0298 0.0159 -232 2010.5 6 -0.0262 -0.0473 -0.005 -232 2011.5 6 -0.0315 -0.0525 -0.0105 -232 2012.5 6 -0.0326 -0.0536 -0.0116 -232 2013.5 6 -0.0334 -0.0547 -0.0122 -232 2014.5 6 -0.0301 -0.0508 -0.0094 -232 2015.5 6 -0.0241 -0.0448 -0.0034 -232 2016.5 6 -0.0228 -0.0439 -0.0017 -232 2017.5 6 -0.0285 -0.0497 -0.0072 -232 2018.5 6 -0.0286 -0.0495 -0.0077 -232 2019.5 6 -0.0199 -0.0408 0.001 -232 2020.5 6 -0.0116 -0.0343 0.0111 -232 2012.5 12 -0.0227 -0.0314 -0.014 -232 2013.5 12 -0.0289 -0.0376 -0.0202 -232 2014.5 12 -0.0298 -0.0386 -0.021 -232 2015.5 12 -0.0283 -0.037 -0.0195 -232 2016.5 12 -0.0251 -0.0339 -0.0164 -232 2017.5 12 -0.0232 -0.0323 -0.014 -232 2015 17 -0.023 -0.0288 -0.0171 -233 2009.5 6 0.0391 -0.0436 0.1219 -233 2010.5 6 0.0429 -0.0333 0.1191 -233 2011.5 6 0.0261 -0.0375 0.0897 -233 2012.5 6 0.0161 -0.0446 0.0767 -233 2013.5 6 0.0094 -0.0512 0.07 -233 2014.5 6 0.0103 -0.0502 0.0709 -233 2015.5 6 0.0013 -0.0571 0.0598 -233 2016.5 6 -0.0053 -0.0644 0.0537 -233 2017.5 6 -0.0137 -0.0752 0.0478 -233 2018.5 6 -0.0127 -0.074 0.0486 -233 2019.5 6 -0.01 -0.073 0.053 -233 2020.5 6 -0.0026 -0.0717 0.0665 -233 2012.5 12 0.0197 -0.0168 0.0563 -233 2013.5 12 0.0138 -0.018 0.0456 -233 2014.5 12 0.0072 -0.0228 0.0372 -233 2015.5 12 0.0031 -0.0268 0.0329 -233 2016.5 12 -0.0023 -0.0335 0.0288 -233 2017.5 12 -0.0042 -0.0371 0.0286 -233 2015 17 0.008 -0.0161 0.0322 -234 2007.5 2 -0.0685 -0.2089 0.072 -234 2008.5 2 -0.2089 -0.3589 -0.0588 -234 2009.5 2 -0.1315 -0.2862 0.0231 -234 2010.5 2 -0.0744 -0.235 0.0863 -234 2011.5 2 0.0509 -0.1368 0.2386 -234 2008.5 4 -0.1435 -0.2024 -0.0846 -234 2009.5 4 -0.1376 -0.1982 -0.0769 -234 2010.5 4 -0.0539 -0.1248 0.017 -234 2009.5 6 -0.1011 -0.141 -0.0611 -235 2007.5 2 0.0832 0.0408 0.1255 -235 2008.5 2 -0.0605 -0.1047 -0.0163 -235 2009.5 2 -0.1037 -0.1484 -0.0589 -235 2010.5 2 0.0468 0.0032 0.0904 -235 2011.5 2 -0.0496 -0.1018 0.0026 -235 2008.5 4 -0.0303 -0.0447 -0.016 -235 2009.5 4 -0.0456 -0.0603 -0.0309 -235 2010.5 4 -0.0273 -0.0448 -0.0097 -235 2009.5 6 -0.025 -0.0342 -0.0158 -236 2007.5 2 0.1403 0.047 0.2336 -236 2008.5 2 0.0677 -0.0264 0.1618 -236 2009.5 2 -0.0275 -0.1206 0.0655 -236 2010.5 2 0.1675 0.0798 0.2552 -236 2011.5 2 0.1144 0.0165 0.2122 -236 2008.5 4 0.0609 0.0298 0.0921 -236 2009.5 4 0.0595 0.0285 0.0906 -236 2010.5 4 0.093 0.0585 0.1275 -236 2009.5 6 0.0831 0.0641 0.102 -237 2007.5 2 -0.2097 -0.2883 -0.1312 -237 2008.5 2 -0.2394 -0.3247 -0.154 -237 2009.5 2 -0.301 -0.3879 -0.2141 -237 2010.5 2 -0.2739 -0.3627 -0.185 -237 2011.5 2 -0.0895 -0.1968 0.0178 -237 2008.5 4 -0.249 -0.2757 -0.2223 -237 2009.5 4 -0.2744 -0.3038 -0.2449 -237 2010.5 4 -0.2267 -0.2611 -0.1923 -237 2009.5 6 -0.2375 -0.2555 -0.2194 -238 2009.5 6 0.0258 0.0157 0.036 -238 2010.5 6 0.0217 0.0116 0.0318 -238 2011.5 6 -0.0016 -0.0122 0.009 -238 2012.5 6 -0.0066 -0.017 0.0038 -238 2013.5 6 -0.0188 -0.0292 -0.0085 -238 2014.5 6 -0.0197 -0.0302 -0.0092 -238 2015.5 6 -0.0047 -0.0151 0.0058 -238 2016.5 6 0.0192 0.0086 0.0299 -238 2017.5 6 0.0126 0.0021 0.0231 -238 2018.5 6 -0.0046 -0.0147 0.0054 -238 2019.5 6 7e-04 -0.0098 0.0112 -238 2020.5 6 0.0056 -0.0052 0.0163 -238 2012.5 12 0.0031 -4e-04 0.0066 -238 2013.5 12 -0.0014 -0.0049 0.0021 -238 2014.5 12 0.0018 -0.0018 0.0055 -238 2015.5 12 -0.004 -0.0076 -4e-04 -238 2016.5 12 -0.0026 -0.0063 0.0012 -238 2017.5 12 -1e-04 -0.0039 0.0037 -238 2015 17 0.003 8e-04 0.0052 -239 2009.5 6 -0.0162 -0.0561 0.0237 -239 2010.5 6 -0.0053 -0.0452 0.0346 -239 2011.5 6 -0.006 -0.046 0.0341 -239 2012.5 6 -0.0151 -0.0551 0.0249 -239 2013.5 6 -0.0079 -0.0472 0.0313 -239 2014.5 6 0.0019 -0.0375 0.0414 -239 2015.5 6 0.0228 -0.017 0.0625 -239 2016.5 6 0.038 -0.0026 0.0786 -239 2017.5 6 0.0487 0.0081 0.0892 -239 2018.5 6 0.0552 0.0141 0.0964 -239 2019.5 6 0.0493 0.0072 0.0915 -239 2020.5 6 0.0233 -0.0202 0.0669 -239 2012.5 12 -0.0033 -0.0207 0.0141 -239 2013.5 12 0.0059 -0.0109 0.0227 -239 2014.5 12 0.0151 -0.0019 0.0321 -239 2015.5 12 0.0215 0.0043 0.0387 -239 2016.5 12 0.0262 0.0082 0.0443 -239 2017.5 12 0.028 0.0089 0.0472 -239 2015 17 0.013 0.0013 0.0248 -240 2007.5 2 0.0608 0.0013 0.1204 -240 2008.5 2 -0.1871 -0.2538 -0.1205 -240 2009.5 2 -0.1121 -0.182 -0.0422 -240 2010.5 2 -0.1217 -0.1939 -0.0494 -240 2011.5 2 0.03 -0.059 0.1191 -240 2008.5 4 -0.0902 -0.1114 -0.0691 -240 2009.5 4 -0.1375 -0.1608 -0.1142 -240 2010.5 4 -0.0733 -0.1019 -0.0446 -240 2009.5 6 -0.0864 -0.101 -0.0718 -241 2007.5 2 -0.0338 -0.1628 0.0952 -241 2008.5 2 -0.0338 -0.1604 0.0927 -241 2009.5 2 0.0568 -0.0709 0.1845 -241 2010.5 2 0.0301 -0.0942 0.1544 -241 2011.5 2 0.033 -0.1024 0.1684 -241 2008.5 4 -0.0062 -0.0692 0.0567 -241 2009.5 4 0.0224 -0.0403 0.0851 -241 2010.5 4 0.0402 -0.0299 0.1103 -241 2009.5 6 0.0134 -0.0333 0.0601 -242 2009.5 6 -0.0138 -0.0298 0.0023 -242 2010.5 6 -0.0387 -0.0547 -0.0227 -242 2011.5 6 -0.0498 -0.0665 -0.0331 -242 2012.5 6 -0.042 -0.0585 -0.0254 -242 2013.5 6 -0.0226 -0.0389 -0.0063 -242 2014.5 6 -0.0094 -0.026 0.0072 -242 2015.5 6 0.0014 -0.0152 0.0181 -242 2016.5 6 -0.0016 -0.0183 0.0151 -242 2017.5 6 -0.0103 -0.0272 0.0066 -242 2018.5 6 -0.0125 -0.0285 0.0035 -242 2019.5 6 -0.0025 -0.0189 0.0139 -242 2020.5 6 -0.0036 -0.0214 0.0141 -242 2012.5 12 -0.0211 -0.0271 -0.0152 -242 2013.5 12 -0.023 -0.029 -0.0171 -242 2014.5 12 -0.0212 -0.0275 -0.015 -242 2015.5 12 -0.0145 -0.0207 -0.0082 -242 2016.5 12 -0.0102 -0.0168 -0.0037 -242 2017.5 12 -0.0074 -0.0143 -5e-04 -242 2015 17 -0.0155 -0.0195 -0.0116 -243 2009.5 6 0.0427 -0.0151 0.1006 -243 2010.5 6 0.0424 -0.0097 0.0944 -243 2011.5 6 0.04 -0.011 0.0911 -243 2012.5 6 0.0358 -0.0143 0.0859 -243 2013.5 6 0.0414 -0.0067 0.0895 -243 2014.5 6 0.0489 1e-04 0.0978 -243 2015.5 6 0.0505 0.0016 0.0993 -243 2016.5 6 0.0504 0.0015 0.0992 -243 2017.5 6 0.0204 -0.0259 0.0666 -243 2018.5 6 -0.0014 -0.0468 0.0441 -243 2019.5 6 0.0087 -0.038 0.0555 -243 2020.5 6 0.0101 -0.0409 0.061 -243 2012.5 12 0.0431 0.0213 0.0649 -243 2013.5 12 0.0427 0.0223 0.0632 -243 2014.5 12 0.0379 0.0177 0.0582 -243 2015.5 12 0.0334 0.0133 0.0535 -243 2016.5 12 0.0299 0.0096 0.0502 -243 2017.5 12 0.0273 0.0062 0.0483 -243 2015 17 0.0335 0.0195 0.0475 -244 2007.5 2 0.1152 -0.0327 0.2631 -244 2008.5 2 0.0565 -0.0751 0.1882 -244 2009.5 2 0.1098 -0.017 0.2366 -244 2010.5 2 0.0168 -0.1053 0.1388 -244 2011.5 2 0.1033 -0.0381 0.2447 -244 2008.5 4 0.0901 0.0341 0.1461 -244 2009.5 4 0.0659 0.0162 0.1157 -244 2010.5 4 0.0706 0.0159 0.1254 -244 2009.5 6 0.0762 0.04 0.1124 -245 2009.5 6 -4e-04 -0.0227 0.0219 -245 2010.5 6 -0.0063 -0.028 0.0154 -245 2011.5 6 -0.0123 -0.0354 0.0108 -245 2012.5 6 0.002 -0.0193 0.0233 -245 2013.5 6 0.0105 -0.0108 0.0319 -245 2014.5 6 0.019 -0.0024 0.0403 -245 2015.5 6 0.023 0.0018 0.0442 -245 2016.5 6 0.0358 0.0137 0.058 -245 2017.5 6 0.0364 0.015 0.0577 -245 2018.5 6 0.0364 0.0154 0.0574 -245 2019.5 6 0.0269 0.0059 0.0478 -245 2020.5 6 0.0187 -0.0037 0.041 -245 2012.5 12 0.0072 -0.0017 0.0162 -245 2013.5 12 0.0115 0.0028 0.0202 -245 2014.5 12 0.016 0.0072 0.0248 -245 2015.5 12 0.0212 0.0121 0.0303 -245 2016.5 12 0.0245 0.015 0.034 -245 2017.5 12 0.0253 0.0155 0.0352 -245 2015 17 0.016 0.01 0.022 -246 2007.5 2 0.1862 0.0083 0.3642 -246 2008.5 2 -0.006 -0.1756 0.1635 -246 2009.5 2 0.1189 -0.0529 0.2907 -246 2010.5 2 0.2322 0.0631 0.4012 -246 2011.5 2 0.0072 -0.1794 0.1939 -246 2008.5 4 0.0891 0.0248 0.1534 -246 2009.5 4 0.1154 0.0538 0.177 -246 2010.5 4 0.1307 0.0617 0.1997 -246 2009.5 6 0.1099 0.0692 0.1506 -247 2009.5 6 0.0057 -0.0038 0.0152 -247 2010.5 6 -0.0073 -0.0169 0.0022 -247 2011.5 6 -0.0137 -0.0236 -0.0038 -247 2012.5 6 -0.0033 -0.0131 0.0065 -247 2013.5 6 -0.0125 -0.0224 -0.0026 -247 2014.5 6 -0.0259 -0.0361 -0.0157 -247 2015.5 6 -0.0315 -0.0416 -0.0214 -247 2016.5 6 -0.0967 -0.1076 -0.0857 -247 2017.5 6 -0.1428 -0.1544 -0.1313 -247 2018.5 6 -0.126 -0.1367 -0.1153 -247 2019.5 6 -0.0873 -0.0986 -0.0759 -247 2020.5 6 -0.0176 -0.0292 -0.006 -247 2012.5 12 -0.0149 -0.0181 -0.0116 -247 2013.5 12 -0.0354 -0.0389 -0.032 -247 2014.5 12 -0.0536 -0.0573 -0.0498 -247 2015.5 12 -0.0609 -0.0646 -0.0571 -247 2016.5 12 -0.064 -0.068 -0.0601 -247 2017.5 12 -0.0627 -0.0667 -0.0588 -247 2015 17 -0.0415 -0.0437 -0.0393 -248 2009.5 6 -0.1154 -0.3001 0.0693 -248 2010.5 6 -0.2171 -0.3894 -0.0449 -248 2011.5 6 -0.2045 -0.3829 -0.0262 -248 2012.5 6 -0.1153 -0.298 0.0673 -248 2013.5 6 -0.0301 -0.2172 0.157 -248 2014.5 6 0.0807 -0.0893 0.2507 -248 2015.5 6 0.2742 0.1171 0.4313 -248 2016.5 6 0.4008 0.2492 0.5524 -248 2017.5 6 0.4892 0.3411 0.6374 -248 2018.5 6 0.4237 0.2937 0.5538 -248 2019.5 6 0.3118 0.202 0.4217 -248 2020.5 6 0.1679 0.0753 0.2605 -248 2012.5 12 -0.0131 -0.0764 0.0502 -248 2013.5 12 0.0431 -0.0141 0.1002 -248 2014.5 12 0.132 0.0737 0.1903 -248 2015.5 12 0.1966 0.1355 0.2576 -248 2016.5 12 0.2381 0.176 0.3001 -248 2017.5 12 0.2678 0.2056 0.3299 -248 2015 17 0.1227 0.09 0.1554 -249 2007.5 2 -0.0684 -0.1371 4e-04 -249 2008.5 2 0.0184 -0.0552 0.0921 -249 2009.5 2 0.2046 0.1333 0.276 -249 2010.5 2 0.0742 0.0107 0.1377 -249 2011.5 2 0.1519 0.0794 0.2245 -249 2008.5 4 0.0482 0.0261 0.0704 -249 2009.5 4 0.1096 0.0865 0.1328 -249 2010.5 4 0.1366 0.1106 0.1627 -249 2009.5 6 0.0857 0.072 0.0994 -250 2009.5 6 -0.0612 -0.1171 -0.0053 -250 2010.5 6 -0.0649 -0.1214 -0.0083 -250 2011.5 6 -0.0744 -0.1331 -0.0156 -250 2012.5 6 -0.0624 -0.1206 -0.0042 -250 2013.5 6 -0.0569 -0.1139 1e-04 -250 2014.5 6 -0.0531 -0.1095 0.0034 -250 2015.5 6 -0.0717 -0.1315 -0.012 -250 2016.5 6 -0.1155 -0.1782 -0.0528 -250 2017.5 6 -0.1513 -0.2136 -0.089 -250 2018.5 6 -0.1788 -0.2418 -0.1159 -250 2019.5 6 -0.182 -0.2512 -0.1127 -250 2020.5 6 -0.1447 -0.2229 -0.0666 -250 2012.5 12 -0.0661 -0.089 -0.0431 -250 2013.5 12 -0.0778 -0.1013 -0.0543 -250 2014.5 12 -0.0901 -0.1146 -0.0656 -250 2015.5 12 -0.1027 -0.128 -0.0773 -250 2016.5 12 -0.1133 -0.1405 -0.086 -250 2017.5 12 -0.1241 -0.1551 -0.0931 -250 2015 17 -0.0986 -0.1172 -0.08 -251 2009.5 6 0.0356 -4e-04 0.0717 -251 2010.5 6 0.0531 0.0175 0.0888 -251 2011.5 6 0.0655 0.0286 0.1024 -251 2012.5 6 0.0644 0.0283 0.1005 -251 2013.5 6 0.057 0.0225 0.0916 -251 2014.5 6 0.0536 0.0191 0.088 -251 2015.5 6 0.0495 0.0157 0.0832 -251 2016.5 6 0.0289 -0.0039 0.0616 -251 2017.5 6 0.0074 -0.0266 0.0414 -251 2018.5 6 0.0025 -0.0295 0.0346 -251 2019.5 6 -0.0049 -0.0363 0.0265 -251 2020.5 6 -0.0072 -0.0408 0.0265 -251 2012.5 12 0.0495 0.0333 0.0658 -251 2013.5 12 0.0508 0.0348 0.0667 -251 2014.5 12 0.0443 0.0287 0.06 -251 2015.5 12 0.0354 0.0202 0.0507 -251 2016.5 12 0.0279 0.0128 0.0431 -251 2017.5 12 0.0208 0.0053 0.0363 -251 2015 17 0.0333 0.0229 0.0436 -252 2007.5 2 0.1576 -0.046 0.3612 -252 2008.5 2 0.1081 -0.0845 0.3008 -252 2009.5 2 -0.1323 -0.3409 0.0763 -252 2010.5 2 -0.0364 -0.2367 0.1638 -252 2011.5 2 -0.0422 -0.2768 0.1923 -252 2008.5 4 0.0508 -0.03 0.1317 -252 2009.5 4 -0.0314 -0.1116 0.0488 -252 2010.5 4 -0.0669 -0.1651 0.0312 -252 2009.5 6 -0.0011 -0.057 0.0547 -253 2007.5 2 0.1826 0.1258 0.2394 -253 2008.5 2 -0.1104 -0.1692 -0.0516 -253 2009.5 2 -0.1324 -0.1928 -0.0719 -253 2010.5 2 0.0678 0.0083 0.1272 -253 2011.5 2 0.0637 -0.0045 0.1318 -253 2008.5 4 -0.0291 -0.0483 -0.0099 -253 2009.5 4 -0.0657 -0.0854 -0.046 -253 2010.5 4 0.0065 -0.0163 0.0293 -253 2009.5 6 -0.0086 -0.0206 0.0034 -254 2009.5 6 0.0399 -0.0211 0.1009 -254 2010.5 6 0.0719 0.0033 0.1405 -254 2011.5 6 0.0676 0.0021 0.1331 -254 2012.5 6 0.037 -0.0208 0.0948 -254 2013.5 6 -0.0034 -0.0633 0.0565 -254 2014.5 6 -0.0346 -0.0995 0.0303 -254 2015.5 6 -0.0307 -0.0905 0.0291 -254 2016.5 6 -0.0238 -0.0828 0.0351 -254 2017.5 6 -0.0173 -0.0788 0.0442 -254 2018.5 6 -0.0035 -0.0662 0.0592 -254 2019.5 6 -0.017 -0.0797 0.0457 -254 2020.5 6 -0.0138 -0.0809 0.0533 -254 2012.5 12 0.0173 -0.0101 0.0446 -254 2013.5 12 0.0149 -0.0117 0.0416 -254 2014.5 12 0.0042 -0.0218 0.0302 -254 2015.5 12 -0.0065 -0.0335 0.0205 -254 2016.5 12 -0.0147 -0.0439 0.0144 -254 2017.5 12 -0.0171 -0.0477 0.0136 -254 2015 17 0.0036 -0.0157 0.0229 -255 2007.5 2 0.085 -0.0069 0.1769 -255 2008.5 2 0.0016 -0.0917 0.0948 -255 2009.5 2 0.0069 -0.0835 0.0973 -255 2010.5 2 0.1379 0.0524 0.2235 -255 2011.5 2 -0.1241 -0.2264 -0.0217 -255 2008.5 4 0.0282 -0.0024 0.0588 -255 2009.5 4 0.0446 0.0147 0.0745 -255 2010.5 4 0.02 -0.015 0.0551 -255 2009.5 6 0.0281 0.0092 0.047 -256 2007.5 2 0.0834 -0.1103 0.2771 -256 2008.5 2 0.1103 -0.0717 0.2922 -256 2009.5 2 0.1015 -0.0733 0.2762 -256 2010.5 2 0.0623 -0.109 0.2335 -256 2011.5 2 0.0435 -0.1425 0.2296 -256 2008.5 4 0.0996 -0.0069 0.2061 -256 2009.5 4 0.0924 -0.0067 0.1915 -256 2010.5 4 0.0684 -0.0288 0.1656 -256 2009.5 6 0.0837 0.0014 0.166 -257 2007.5 2 0.3115 0.0652 0.5579 -257 2008.5 2 -0.1196 -0.3314 0.0923 -257 2009.5 2 -0.0845 -0.3039 0.1349 -257 2010.5 2 0.1878 -0.0457 0.4214 -257 2011.5 2 -0.4032 -0.7396 -0.0668 -257 2008.5 4 0.0203 -0.0646 0.1051 -257 2009.5 4 -0.0133 -0.0904 0.0638 -257 2010.5 4 -0.0711 -0.1752 0.0329 -257 2009.5 6 -0.0192 -0.0774 0.0389 -258 2009.5 6 0.015 -0.0151 0.0451 -258 2010.5 6 -0.0043 -0.0334 0.0248 -258 2011.5 6 -0.0377 -0.067 -0.0084 -258 2012.5 6 -0.0686 -0.1002 -0.0371 -258 2013.5 6 -0.0707 -0.1016 -0.0398 -258 2014.5 6 -0.0609 -0.0912 -0.0306 -258 2015.5 6 -0.0352 -0.0644 -0.006 -258 2016.5 6 0.0036 -0.0257 0.0329 -258 2017.5 6 0.0371 0.0077 0.0665 -258 2018.5 6 0.0586 0.029 0.0881 -258 2019.5 6 0.0531 0.0245 0.0817 -258 2020.5 6 0.0358 0.0061 0.0656 -258 2012.5 12 -0.0324 -0.044 -0.0207 -258 2013.5 12 -0.0301 -0.0416 -0.0187 -258 2014.5 12 -0.0232 -0.0343 -0.0121 -258 2015.5 12 -0.014 -0.0247 -0.0033 -258 2016.5 12 -0.006 -0.017 0.005 -258 2017.5 12 0.0051 -0.0064 0.0165 -258 2015 17 -0.0088 -0.0156 -0.002 -259 2007.5 2 -0.1494 -0.2273 -0.0715 -259 2008.5 2 -0.0118 -0.0951 0.0715 -259 2009.5 2 -0.127 -0.2098 -0.0443 -259 2010.5 2 0.0346 -0.0454 0.1146 -259 2011.5 2 0.0296 -0.0651 0.1242 -259 2008.5 4 -0.0876 -0.114 -0.0613 -259 2009.5 4 -0.044 -0.0716 -0.0163 -259 2010.5 4 -0.0154 -0.0474 0.0166 -259 2009.5 6 -0.0446 -0.0616 -0.0276 -260 2009.5 6 0.0603 0.01 0.1106 -260 2010.5 6 0.0557 0.0062 0.1052 -260 2011.5 6 0.0442 -0.0051 0.0936 -260 2012.5 6 0.0192 -0.028 0.0664 -260 2013.5 6 -0.001 -0.0496 0.0477 -260 2014.5 6 0.0063 -0.0413 0.0538 -260 2015.5 6 0.044 -0.0036 0.0915 -260 2016.5 6 0.0666 0.0181 0.1151 -260 2017.5 6 0.0836 0.0354 0.1319 -260 2018.5 6 0.0736 0.0264 0.1208 -260 2019.5 6 0.0537 0.006 0.1014 -260 2020.5 6 0.0067 -0.044 0.0575 -260 2012.5 12 0.0348 0.0139 0.0557 -260 2013.5 12 0.0395 0.0189 0.0601 -260 2014.5 12 0.0447 0.0243 0.0651 -260 2015.5 12 0.0425 0.0227 0.0624 -260 2016.5 12 0.0415 0.0213 0.0616 -260 2017.5 12 0.038 0.0174 0.0587 -260 2015 17 0.0389 0.025 0.0528 -261 2009.5 6 0.0177 0.0067 0.0286 -261 2010.5 6 0.0342 0.0233 0.0451 -261 2011.5 6 0.0294 0.0182 0.0405 -261 2012.5 6 0.0132 0.0019 0.0244 -261 2013.5 6 -0.0079 -0.0193 0.0036 -261 2014.5 6 -0.0243 -0.0359 -0.0127 -261 2015.5 6 -0.0119 -0.0231 -8e-04 -261 2016.5 6 0.0102 -0.0011 0.0216 -261 2017.5 6 0.0238 0.0122 0.0354 -261 2018.5 6 0.0261 0.0149 0.0372 -261 2019.5 6 0.0116 1e-04 0.0232 -261 2020.5 6 0.005 -0.0069 0.0168 -261 2012.5 12 0.0102 0.0063 0.0141 -261 2013.5 12 0.0068 0.0028 0.0107 -261 2014.5 12 0.0078 0.0038 0.0119 -261 2015.5 12 0.0071 0.003 0.0112 -261 2016.5 12 0.0061 0.0019 0.0103 -261 2017.5 12 0.0047 4e-04 0.0091 -261 2015 17 0.0096 0.0071 0.0122 -262 2009.5 6 -0.1058 -0.1562 -0.0554 -262 2010.5 6 -0.0899 -0.1405 -0.0393 -262 2011.5 6 -0.0387 -0.0927 0.0153 -262 2012.5 6 -0.0617 -0.1161 -0.0072 -262 2013.5 6 -0.1021 -0.1596 -0.0446 -262 2014.5 6 -0.1251 -0.1801 -0.0701 -262 2015.5 6 -0.0288 -0.0811 0.0236 -262 2016.5 6 0.0406 -0.0133 0.0945 -262 2017.5 6 0.0765 0.0208 0.1323 -262 2018.5 6 0.1066 0.052 0.1612 -262 2019.5 6 -0.0397 -0.0953 0.0159 -262 2020.5 6 -0.0984 -0.156 -0.0407 -262 2012.5 12 -0.0767 -0.0952 -0.0583 -262 2013.5 12 -0.0515 -0.0701 -0.0329 -262 2014.5 12 -0.0285 -0.0475 -0.0094 -262 2015.5 12 -0.008 -0.0267 0.0108 -262 2016.5 12 -0.0217 -0.0419 -0.0016 -262 2017.5 12 -0.031 -0.052 -0.01 -262 2015 17 -0.0472 -0.0588 -0.0356 -263 2009.5 6 0.0219 -0.0471 0.091 -263 2010.5 6 0.0178 -0.0467 0.0822 -263 2011.5 6 0.0055 -0.0543 0.0653 -263 2012.5 6 -0.0071 -0.0645 0.0504 -263 2013.5 6 -0.0198 -0.0793 0.0396 -263 2014.5 6 -0.0281 -0.0909 0.0346 -263 2015.5 6 -0.0282 -0.091 0.0345 -263 2016.5 6 -0.0232 -0.084 0.0375 -263 2017.5 6 -0.0137 -0.0719 0.0445 -263 2018.5 6 0.0022 -0.0562 0.0606 -263 2019.5 6 0.007 -0.0534 0.0674 -263 2020.5 6 0.0051 -0.0575 0.0677 -263 2012.5 12 -0.0055 -0.0365 0.0255 -263 2013.5 12 -0.0091 -0.039 0.0208 -263 2014.5 12 -0.0118 -0.0419 0.0182 -263 2015.5 12 -0.014 -0.0446 0.0167 -263 2016.5 12 -0.0137 -0.0447 0.0173 -263 2017.5 12 -0.0108 -0.0419 0.0204 -263 2015 17 -0.0059 -0.0277 0.0159 -264 2007.5 2 -0.0625 -0.1284 0.0034 -264 2008.5 2 0.0186 -0.0465 0.0837 -264 2009.5 2 -0.0512 -0.1147 0.0122 -264 2010.5 2 -0.0077 -0.0686 0.0531 -264 2011.5 2 0.0236 -0.0483 0.0955 -264 2008.5 4 -0.0267 -0.0495 -0.0039 -264 2009.5 4 -0.0172 -0.0403 0.0058 -264 2010.5 4 -0.0114 -0.0381 0.0154 -264 2009.5 6 -0.0163 -0.0314 -0.0011 -265 2007.5 2 0.1113 -0.27 0.4927 -265 2008.5 2 -0.148 -0.5265 0.2305 -265 2009.5 2 -0.0192 -0.4064 0.368 -265 2010.5 2 -0.4324 -0.9087 0.0439 -265 2011.5 2 -0.1809 -0.6295 0.2678 -265 2008.5 4 -0.0315 -0.1928 0.1297 -265 2009.5 4 -0.1817 -0.3631 -3e-04 -265 2010.5 4 -0.2329 -0.4639 -0.002 -265 2009.5 6 -0.1474 -0.2852 -0.0097 -266 2007.5 2 0.1059 0.0313 0.1804 -266 2008.5 2 -0.0381 -0.1109 0.0347 -266 2009.5 2 -0.0728 -0.1443 -0.0013 -266 2010.5 2 -0.0185 -0.0898 0.0529 -266 2011.5 2 -0.0313 -0.1199 0.0573 -266 2008.5 4 -0.0053 -0.0305 0.0199 -266 2009.5 4 -0.0461 -0.0717 -0.0204 -266 2010.5 4 -0.0386 -0.0697 -0.0076 -266 2009.5 6 -0.021 -0.0379 -0.0041 -267 2009.5 6 -0.0792 -0.112 -0.0465 -267 2010.5 6 -0.0715 -0.1018 -0.0411 -267 2011.5 6 -0.0437 -0.0724 -0.0149 -267 2012.5 6 -0.0167 -0.046 0.0127 -267 2013.5 6 -0.0032 -0.0335 0.027 -267 2014.5 6 0.0019 -0.0279 0.0317 -267 2015.5 6 -4e-04 -0.0295 0.0287 -267 2016.5 6 -0.009 -0.038 0.0201 -267 2017.5 6 -0.0161 -0.0451 0.0129 -267 2018.5 6 -0.0143 -0.0427 0.0142 -267 2019.5 6 -0.0171 -0.0463 0.012 -267 2020.5 6 -0.0166 -0.048 0.0148 -267 2012.5 12 -0.0322 -0.0441 -0.0202 -267 2013.5 12 -0.0249 -0.0367 -0.0132 -267 2014.5 12 -0.0163 -0.0286 -0.004 -267 2015.5 12 -0.0115 -0.024 0.0011 -267 2016.5 12 -0.0092 -0.0219 0.0036 -267 2017.5 12 -0.0092 -0.0222 0.0039 -267 2015 17 -0.0232 -0.0312 -0.0152 -268 2007.5 2 0.0011 -0.0532 0.0553 -268 2008.5 2 0.0033 -0.0501 0.0568 -268 2009.5 2 0.0066 -0.0476 0.0607 -268 2010.5 2 -0.0015 -0.0554 0.0523 -268 2011.5 2 -0.0056 -0.0632 0.0521 -268 2008.5 4 0.0037 -0.024 0.0313 -268 2009.5 4 0.0032 -0.0241 0.0304 -268 2010.5 4 -3e-04 -0.0292 0.0285 -268 2009.5 6 0.0015 -0.019 0.022 -269 2009.5 6 0.0633 0.0429 0.0836 -269 2010.5 6 0.0467 0.0271 0.0663 -269 2011.5 6 0.0183 -0.0015 0.0381 -269 2012.5 6 -5e-04 -0.0203 0.0193 -269 2013.5 6 -0.0147 -0.0342 0.0048 -269 2014.5 6 -0.0063 -0.0258 0.0132 -269 2015.5 6 0.0182 -0.0013 0.0376 -269 2016.5 6 0.0145 -0.0049 0.034 -269 2017.5 6 0.0063 -0.0134 0.026 -269 2018.5 6 -0.0051 -0.0236 0.0135 -269 2019.5 6 -0.0105 -0.0294 0.0084 -269 2020.5 6 -0.0075 -0.0276 0.0126 -269 2012.5 12 0.0207 0.0134 0.028 -269 2013.5 12 0.0151 0.008 0.0223 -269 2014.5 12 0.0076 3e-04 0.015 -269 2015.5 12 0.0016 -0.0057 0.0089 -269 2016.5 12 -0.002 -0.0094 0.0055 -269 2017.5 12 1e-04 -0.0077 0.0078 -269 2015 17 0.0107 0.0061 0.0154 -270 2007.5 2 -0.01 -0.1244 0.1045 -270 2008.5 2 0.0172 -0.0978 0.1323 -270 2009.5 2 -0.0206 -0.1336 0.0925 -270 2010.5 2 -0.0104 -0.1243 0.1034 -270 2011.5 2 0.011 -0.1082 0.1302 -270 2008.5 4 -0.0023 -0.06 0.0555 -270 2009.5 4 -0.0062 -0.0641 0.0518 -270 2010.5 4 -0.007 -0.0677 0.0536 -270 2009.5 6 -0.0036 -0.047 0.0399 -271 2009.5 6 0.0897 0.0221 0.1573 -271 2010.5 6 0.0644 5e-04 0.1282 -271 2011.5 6 0.0949 0.033 0.1568 -271 2012.5 6 0.0762 0.0192 0.1332 -271 2013.5 6 0.0154 -0.0463 0.0771 -271 2014.5 6 -0.0764 -0.1401 -0.0127 -271 2015.5 6 -0.0753 -0.1349 -0.0157 -271 2016.5 6 -0.0536 -0.1101 0.0028 -271 2017.5 6 0.006 -0.0501 0.0622 -271 2018.5 6 0.0269 -0.0325 0.0864 -271 2019.5 6 0.0284 -0.0351 0.0919 -271 2020.5 6 -0.0654 -0.13 -7e-04 -271 2012.5 12 0.0206 -0.0029 0.0441 -271 2013.5 12 0.0189 -0.0044 0.0422 -271 2014.5 12 0.008 -0.015 0.031 -271 2015.5 12 -0.0058 -0.0284 0.0167 -271 2016.5 12 -0.0138 -0.0371 0.0096 -271 2017.5 12 -0.032 -0.0553 -0.0087 -271 2015 17 0.0044 -0.0103 0.0191 -272 2009.5 6 0.1683 0.0843 0.2522 -272 2010.5 6 0.1702 0.0895 0.2509 -272 2011.5 6 0.1558 0.0778 0.2338 -272 2012.5 6 0.1278 0.0516 0.2041 -272 2013.5 6 0.0807 0.011 0.1505 -272 2014.5 6 0.0456 -0.0235 0.1147 -272 2015.5 6 0.026 -0.0475 0.0994 -272 2016.5 6 0.0219 -0.0552 0.0989 -272 2017.5 6 0.0164 -0.0589 0.0917 -272 2018.5 6 0.0214 -0.0533 0.0961 -272 2019.5 6 0.0308 -0.0515 0.1131 -272 2020.5 6 0.0445 -0.048 0.1369 -272 2012.5 12 0.1047 0.0667 0.1428 -272 2013.5 12 0.0906 0.0551 0.126 -272 2014.5 12 0.0731 0.04 0.1063 -272 2015.5 12 0.0559 0.0225 0.0892 -272 2016.5 12 0.0393 0.003 0.0756 -272 2017.5 12 0.0335 -0.0072 0.0742 -272 2015 17 0.0739 0.0459 0.1019 -273 2007.5 2 0.4305 0.0452 0.8157 -273 2008.5 2 0.1564 -0.1822 0.495 -273 2009.5 2 0.5953 0.2784 0.9121 -273 2010.5 2 0.0712 -0.2115 0.3539 -273 2011.5 2 0.3216 0.0275 0.6158 -273 2008.5 4 0.3703 0.2383 0.5022 -273 2009.5 4 0.3064 0.1961 0.4167 -273 2010.5 4 0.3035 0.1841 0.423 -273 2009.5 6 0.3125 0.2355 0.3896 -274 2009.5 6 0.0165 -0.0149 0.0479 -274 2010.5 6 0.0186 -0.0117 0.0489 -274 2011.5 6 -0.0052 -0.0339 0.0234 -274 2012.5 6 -0.0263 -0.0563 0.0037 -274 2013.5 6 -0.0229 -0.0532 0.0074 -274 2014.5 6 -0.0241 -0.0541 0.0059 -274 2015.5 6 -0.0255 -0.0557 0.0047 -274 2016.5 6 -0.023 -0.0543 0.0083 -274 2017.5 6 -0.016 -0.0462 0.0142 -274 2018.5 6 -0.0146 -0.0444 0.0152 -274 2019.5 6 -0.0088 -0.0398 0.0222 -274 2020.5 6 -7e-04 -0.0337 0.0322 -274 2012.5 12 -0.0089 -0.0212 0.0034 -274 2013.5 12 -0.0141 -0.0263 -0.0019 -274 2014.5 12 -0.0158 -0.028 -0.0036 -274 2015.5 12 -0.0201 -0.0326 -0.0075 -274 2016.5 12 -0.0193 -0.0321 -0.0065 -274 2017.5 12 -0.016 -0.029 -0.003 -274 2015 17 -0.0113 -0.0195 -0.0031 -275 2009.5 6 -0.0341 -0.0837 0.0155 -275 2010.5 6 -0.0406 -0.0909 0.0096 -275 2011.5 6 -0.0461 -0.0977 0.0055 -275 2012.5 6 -0.0596 -0.1116 -0.0075 -275 2013.5 6 -0.0815 -0.1337 -0.0292 -275 2014.5 6 -0.079 -0.1345 -0.0235 -275 2015.5 6 -0.0813 -0.1396 -0.023 -275 2016.5 6 -0.0894 -0.149 -0.0297 -275 2017.5 6 -0.1231 -0.1892 -0.057 -275 2018.5 6 -0.13 -0.201 -0.0591 -275 2019.5 6 -0.1077 -0.1798 -0.0357 -275 2020.5 6 -0.0884 -0.1684 -0.0085 -275 2012.5 12 -0.0598 -0.0813 -0.0382 -275 2013.5 12 -0.0697 -0.092 -0.0475 -275 2014.5 12 -0.0813 -0.1054 -0.0572 -275 2015.5 12 -0.0912 -0.1175 -0.0648 -275 2016.5 12 -0.0957 -0.1243 -0.0671 -275 2017.5 12 -0.0944 -0.1263 -0.0624 -275 2015 17 -0.0779 -0.0962 -0.0596 -276 2007.5 2 -0.1078 -0.2335 0.0179 -276 2008.5 2 -0.0965 -0.2244 0.0314 -276 2009.5 2 -0.1356 -0.2715 3e-04 -276 2010.5 2 -0.1785 -0.3191 -0.0379 -276 2011.5 2 -0.1886 -0.352 -0.0252 -276 2008.5 4 -0.1116 -0.1638 -0.0595 -276 2009.5 4 -0.1368 -0.1929 -0.0807 -276 2010.5 4 -0.1687 -0.2359 -0.1014 -276 2009.5 6 -0.1401 -0.1797 -0.1004 -277 2009.5 6 -0.0395 -0.0818 0.0028 -277 2010.5 6 -0.0381 -0.0776 0.0013 -277 2011.5 6 -0.0513 -0.0898 -0.0128 -277 2012.5 6 -0.054 -0.0944 -0.0135 -277 2013.5 6 -0.0431 -0.0851 -0.0011 -277 2014.5 6 -0.0272 -0.0703 0.0159 -277 2015.5 6 -0.0429 -0.0867 0.001 -277 2016.5 6 -0.038 -0.0826 0.0065 -277 2017.5 6 -0.0468 -0.0933 -4e-04 -277 2018.5 6 -0.0676 -0.1135 -0.0218 -277 2019.5 6 -0.0934 -0.142 -0.0447 -277 2020.5 6 -0.0989 -0.1528 -0.045 -277 2012.5 12 -0.0387 -0.0572 -0.0202 -277 2013.5 12 -0.0399 -0.0583 -0.0215 -277 2014.5 12 -0.0468 -0.0658 -0.0278 -277 2015.5 12 -0.0513 -0.0709 -0.0317 -277 2016.5 12 -0.0533 -0.0737 -0.0328 -277 2017.5 12 -0.0598 -0.0817 -0.0379 -277 2015 17 -0.0518 -0.0655 -0.0382 -278 2009.5 6 0.0663 0.0375 0.0951 -278 2010.5 6 0.0381 0.012 0.0641 -278 2011.5 6 -3e-04 -0.0261 0.0255 -278 2012.5 6 -0.026 -0.0522 2e-04 -278 2013.5 6 -0.0382 -0.0649 -0.0115 -278 2014.5 6 -0.0341 -0.0606 -0.0077 -278 2015.5 6 -0.0194 -0.045 0.0063 -278 2016.5 6 -0.0166 -0.043 0.0098 -278 2017.5 6 -0.0293 -0.0565 -0.0021 -278 2018.5 6 -0.0237 -0.0498 0.0024 -278 2019.5 6 -0.0266 -0.0538 5e-04 -278 2020.5 6 -0.0308 -0.0608 -8e-04 -278 2012.5 12 0.0029 -0.0075 0.0133 -278 2013.5 12 -0.009 -0.0191 0.0012 -278 2014.5 12 -0.0196 -0.0301 -0.009 -278 2015.5 12 -0.0253 -0.0359 -0.0146 -278 2016.5 12 -0.0276 -0.0386 -0.0165 -278 2017.5 12 -0.0277 -0.0395 -0.0158 -278 2015 17 -0.0111 -0.0182 -0.004 -279 2009.5 6 -0.0703 -0.1162 -0.0245 -279 2010.5 6 -0.0762 -0.1209 -0.0316 -279 2011.5 6 -0.0519 -0.0962 -0.0076 -279 2012.5 6 -0.0398 -0.0844 0.0048 -279 2013.5 6 -0.0384 -0.0833 0.0065 -279 2014.5 6 -0.0528 -0.0986 -0.007 -279 2015.5 6 -0.0513 -0.0973 -0.0053 -279 2016.5 6 -0.0509 -0.0978 -0.004 -279 2017.5 6 -0.0289 -0.0754 0.0176 -279 2018.5 6 -0.0151 -0.0627 0.0325 -279 2019.5 6 -0.0132 -0.0627 0.0363 -279 2020.5 6 -0.0093 -0.0618 0.0432 -279 2012.5 12 -0.0533 -0.0772 -0.0294 -279 2013.5 12 -0.0534 -0.0769 -0.0299 -279 2014.5 12 -0.045 -0.0683 -0.0217 -279 2015.5 12 -0.0387 -0.0622 -0.0152 -279 2016.5 12 -0.0334 -0.0576 -0.0091 -279 2017.5 12 -0.0295 -0.0549 -0.0041 -279 2015 17 -0.0407 -0.0584 -0.023 -280 2007.5 2 0.0408 0.0076 0.074 -280 2008.5 2 0.0813 0.046 0.1166 -280 2009.5 2 -0.0374 -0.0729 -0.0018 -280 2010.5 2 0.0402 0.0059 0.0744 -280 2011.5 2 0.0345 -0.0063 0.0753 -280 2008.5 4 0.0335 0.0219 0.0452 -280 2009.5 4 0.0215 0.0094 0.0335 -280 2010.5 4 0.0152 0.0011 0.0293 -280 2009.5 6 0.0289 0.0212 0.0366 -281 2009.5 6 0.0261 -0.0346 0.0867 -281 2010.5 6 0.0496 -0.01 0.1092 -281 2011.5 6 0.0593 5e-04 0.1181 -281 2012.5 6 0.0511 -0.002 0.1042 -281 2013.5 6 0.045 -0.0063 0.0964 -281 2014.5 6 0.0382 -0.0118 0.0883 -281 2015.5 6 0.0228 -0.0247 0.0704 -281 2016.5 6 -0.0034 -0.0499 0.043 -281 2017.5 6 -0.0241 -0.0724 0.0241 -281 2018.5 6 -0.0378 -0.0903 0.0146 -281 2019.5 6 -0.0313 -0.0831 0.0205 -281 2020.5 6 -0.0121 -0.0644 0.0401 -281 2012.5 12 0.0359 0.0031 0.0687 -281 2013.5 12 0.0315 0.0021 0.0609 -281 2014.5 12 0.0238 -0.0022 0.0497 -281 2015.5 12 0.0148 -0.0088 0.0384 -281 2016.5 12 0.004 -0.0191 0.027 -281 2017.5 12 -0.0037 -0.028 0.0207 -281 2015 17 0.0149 -0.0044 0.0341 -282 2009.5 6 -0.0645 -0.0897 -0.0394 -282 2010.5 6 -0.0438 -0.0686 -0.019 -282 2011.5 6 -0.0876 -0.1141 -0.0611 -282 2012.5 6 -0.0899 -0.1157 -0.0641 -282 2013.5 6 -0.1029 -0.1295 -0.0764 -282 2014.5 6 -0.1224 -0.1495 -0.0953 -282 2015.5 6 -0.0949 -0.1216 -0.0682 -282 2016.5 6 -0.0751 -0.1033 -0.047 -282 2017.5 6 -0.0998 -0.1275 -0.0722 -282 2018.5 6 -0.0731 -0.0999 -0.0463 -282 2019.5 6 -0.0564 -0.0848 -0.028 -282 2020.5 6 -0.0591 -0.0884 -0.0298 -282 2012.5 12 -0.0832 -0.092 -0.0744 -282 2013.5 12 -0.0894 -0.0986 -0.0802 -282 2014.5 12 -0.091 -0.1006 -0.0814 -282 2015.5 12 -0.0889 -0.0984 -0.0793 -282 2016.5 12 -0.088 -0.0981 -0.0778 -282 2017.5 12 -0.0819 -0.0921 -0.0716 -282 2015 17 -0.081 -0.0868 -0.0752 -283 2009.5 6 0 -0.0539 0.0539 -283 2010.5 6 0.0179 -0.0322 0.068 -283 2011.5 6 0.0085 -0.0403 0.0574 -283 2012.5 6 -0.0063 -0.0572 0.0446 -283 2013.5 6 -0.0042 -0.0552 0.0469 -283 2014.5 6 0.0069 -0.0418 0.0555 -283 2015.5 6 0.0169 -0.031 0.0648 -283 2016.5 6 0.0348 -0.0144 0.0841 -283 2017.5 6 0.0413 -0.0081 0.0908 -283 2018.5 6 0.0356 -0.0115 0.0827 -283 2019.5 6 0.019 -0.0299 0.0679 -283 2020.5 6 0.0109 -0.0418 0.0637 -283 2012.5 12 0.0067 -0.0157 0.0291 -283 2013.5 12 0.0132 -0.0081 0.0345 -283 2014.5 12 0.0158 -0.0054 0.037 -283 2015.5 12 0.0182 -0.0028 0.0391 -283 2016.5 12 0.0182 -0.0031 0.0395 -283 2017.5 12 0.02 -0.0022 0.0422 -283 2015 17 0.0137 -9e-04 0.0282 -284 2009.5 6 0.0274 -0.0269 0.0816 -284 2010.5 6 0.0254 -0.0258 0.0767 -284 2011.5 6 0.0212 -0.0291 0.0716 -284 2012.5 6 0.018 -0.0314 0.0675 -284 2013.5 6 0.0179 -0.031 0.0669 -284 2014.5 6 0.0231 -0.0252 0.0713 -284 2015.5 6 0.033 -0.0172 0.0832 -284 2016.5 6 0.0338 -0.0173 0.085 -284 2017.5 6 0.0341 -0.0178 0.0861 -284 2018.5 6 0.0297 -0.0216 0.081 -284 2019.5 6 0.0217 -0.0297 0.0731 -284 2020.5 6 0.0167 -0.0367 0.0702 -284 2012.5 12 0.025 -0.0034 0.0533 -284 2013.5 12 0.0257 -0.0019 0.0532 -284 2014.5 12 0.0263 -0.0014 0.054 -284 2015.5 12 0.0262 -0.0018 0.0541 -284 2016.5 12 0.0261 -0.0029 0.055 -284 2017.5 12 0.0259 -0.0045 0.0563 -284 2015 17 0.0251 0.0018 0.0484 -285 2009.5 6 -0.019 -0.0656 0.0276 -285 2010.5 6 -0.0218 -0.0675 0.024 -285 2011.5 6 -0.0198 -0.0649 0.0253 -285 2012.5 6 -0.0112 -0.0548 0.0324 -285 2013.5 6 0.0012 -0.0413 0.0436 -285 2014.5 6 0.0168 -0.0254 0.059 -285 2015.5 6 0.0283 -0.0161 0.0728 -285 2016.5 6 0.0317 -0.0145 0.0779 -285 2017.5 6 0.0202 -0.0228 0.0633 -285 2018.5 6 0.0116 -0.0302 0.0535 -285 2019.5 6 0.0069 -0.0351 0.0489 -285 2020.5 6 0.0095 -0.0346 0.0535 -285 2012.5 12 -0.002 -0.0268 0.0227 -285 2013.5 12 0.0039 -0.0199 0.0278 -285 2014.5 12 0.0074 -0.0162 0.0311 -285 2015.5 12 0.0106 -0.0132 0.0344 -285 2016.5 12 0.0136 -0.0111 0.0383 -285 2017.5 12 0.0173 -0.0093 0.0438 -285 2015 17 0.0057 -0.0127 0.0241 -286 2007.5 2 0.0075 -0.1611 0.176 -286 2008.5 2 -0.0336 -0.1994 0.1321 -286 2009.5 2 -0.0015 -0.1671 0.164 -286 2010.5 2 0.0221 -0.1421 0.1863 -286 2011.5 2 -0.0102 -0.1807 0.1602 -286 2008.5 4 -0.0117 -0.0922 0.0689 -286 2009.5 4 -0.0041 -0.0836 0.0755 -286 2010.5 4 0.0053 -0.0779 0.0885 -286 2009.5 6 -0.0034 -0.0607 0.0538 -287 2007.5 2 0.6318 0.2003 1.0634 -287 2008.5 2 -0.1608 -0.5707 0.2492 -287 2009.5 2 0.0307 -0.336 0.3973 -287 2010.5 2 -0.735 -1.121 -0.349 -287 2011.5 2 0.0925 -0.4215 0.6065 -287 2008.5 4 0.1344 -0.006 0.2748 -287 2009.5 4 -0.2565 -0.3949 -0.118 -287 2010.5 4 -0.257 -0.4212 -0.0929 -287 2009.5 6 -0.0934 -0.1881 0.0014 -288 2007.5 2 -0.1005 -0.3199 0.1189 -288 2008.5 2 0.0584 -0.1504 0.2673 -288 2009.5 2 0.0589 -0.1451 0.2629 -288 2010.5 2 -0.0296 -0.2217 0.1626 -288 2011.5 2 -0.1203 -0.3548 0.1142 -288 2008.5 4 0.0109 -0.0721 0.0939 -288 2009.5 4 0.0322 -0.0503 0.1147 -288 2010.5 4 -0.0302 -0.1284 0.0679 -288 2009.5 6 -0.0098 -0.0677 0.0481 -289 2009.5 6 0.3679 0.1907 0.545 -289 2010.5 6 0.2621 0.1424 0.3818 -289 2011.5 6 0.0425 -0.0815 0.1664 -289 2012.5 6 0.0144 -0.1199 0.1488 -289 2013.5 6 -0.014 -0.1312 0.1031 -289 2014.5 6 -0.0929 -0.1964 0.0107 -289 2015.5 6 -0.0417 -0.1502 0.0668 -289 2016.5 6 -0.0975 -0.2215 0.0265 -289 2017.5 6 -0.2146 -0.3414 -0.0879 -289 2018.5 6 -0.2165 -0.3253 -0.1077 -289 2019.5 6 -0.1049 -0.2214 0.0117 -289 2020.5 6 -0.0444 -0.178 0.0892 -289 2012.5 12 0.0913 0.0358 0.1468 -289 2013.5 12 0.0059 -0.0404 0.0521 -289 2014.5 12 -0.0498 -0.0982 -0.0014 -289 2015.5 12 -0.0734 -0.1204 -0.0265 -289 2016.5 12 -0.1089 -0.1538 -0.064 -289 2017.5 12 -0.1042 -0.1507 -0.0578 -289 2015 17 -0.0027 -0.037 0.0316 -290 2009.5 6 -0.0392 -0.0824 0.0039 -290 2010.5 6 -0.0466 -0.0878 -0.0053 -290 2011.5 6 -0.0305 -0.0689 0.0078 -290 2012.5 6 -0.001 -0.0378 0.0358 -290 2013.5 6 0.026 -0.0168 0.0688 -290 2014.5 6 0.0053 -0.0321 0.0428 -290 2015.5 6 -0.0061 -0.0438 0.0315 -290 2016.5 6 -0.0035 -0.0414 0.0343 -290 2017.5 6 0.0273 -0.0073 0.062 -290 2018.5 6 0.0506 0.0146 0.0866 -290 2019.5 6 0.0587 0.0202 0.0972 -290 2020.5 6 0.0374 -5e-04 0.0754 -290 2012.5 12 -0.0157 -0.0322 8e-04 -290 2013.5 12 -0.0088 -0.0243 0.0067 -290 2014.5 12 0.005 -0.0102 0.0203 -290 2015.5 12 0.0137 -0.0014 0.0289 -290 2016.5 12 0.022 0.0061 0.0378 -290 2017.5 12 0.0228 0.0073 0.0383 -290 2015 17 0.0051 -0.0048 0.015 -291 2009.5 6 -0.0055 -0.0719 0.061 -291 2010.5 6 0.002 -0.0602 0.0642 -291 2011.5 6 0.0074 -0.0539 0.0687 -291 2012.5 6 0.0151 -0.0461 0.0764 -291 2013.5 6 0.0161 -0.0443 0.0766 -291 2014.5 6 0.0131 -0.0459 0.072 -291 2015.5 6 0.0182 -0.0404 0.0769 -291 2016.5 6 0.0245 -0.0343 0.0832 -291 2017.5 6 0.0266 -0.0316 0.0848 -291 2018.5 6 0.0225 -0.0354 0.0803 -291 2019.5 6 0.021 -0.0378 0.0799 -291 2020.5 6 0.0204 -0.0409 0.0818 -291 2012.5 12 0.0085 -0.0222 0.0392 -291 2013.5 12 0.0132 -0.0165 0.0429 -291 2014.5 12 0.0173 -0.0125 0.0471 -291 2015.5 12 0.0192 -0.0107 0.0492 -291 2016.5 12 0.019 -0.0108 0.0488 -291 2017.5 12 0.0205 -0.0102 0.0512 -291 2015 17 0.0147 -0.007 0.0364 -292 2007.5 2 -0.0801 -0.1441 -0.0161 -292 2008.5 2 -0.0835 -0.1598 -0.0071 -292 2009.5 2 -0.0634 -0.142 0.0151 -292 2010.5 2 -0.0349 -0.1139 0.0441 -292 2011.5 2 0.0119 -0.0873 0.1111 -292 2008.5 4 -0.0764 -0.1003 -0.0526 -292 2009.5 4 -0.0609 -0.0864 -0.0353 -292 2010.5 4 -0.0294 -0.0619 0.003 -292 2009.5 6 -0.0531 -0.0696 -0.0366 -293 2009.5 6 -0.0168 -0.0737 0.04 -293 2010.5 6 -0.0246 -0.0778 0.0286 -293 2011.5 6 -0.0373 -0.0906 0.016 -293 2012.5 6 -0.0471 -0.1022 0.008 -293 2013.5 6 -0.0353 -0.0901 0.0194 -293 2014.5 6 -0.0186 -0.0703 0.033 -293 2015.5 6 0.0063 -0.0444 0.057 -293 2016.5 6 0.0283 -0.0235 0.08 -293 2017.5 6 0.0495 -0.0051 0.1041 -293 2018.5 6 0.0498 -0.0039 0.1035 -293 2019.5 6 0.0277 -0.0224 0.0778 -293 2020.5 6 0.0105 -0.0427 0.0636 -293 2012.5 12 -0.0201 -0.0441 0.004 -293 2013.5 12 -0.0129 -0.0352 0.0093 -293 2014.5 12 -0.0041 -0.0256 0.0174 -293 2015.5 12 0.0029 -0.0185 0.0243 -293 2016.5 12 0.0107 -0.0114 0.0329 -293 2017.5 12 0.0153 -0.0077 0.0382 -293 2015 17 -0.0017 -0.0164 0.0131 -294 2007.5 2 0.1075 0.009 0.2059 -294 2008.5 2 -0.1112 -0.2107 -0.0117 -294 2009.5 2 -0.0424 -0.1402 0.0554 -294 2010.5 2 0.0513 -0.0464 0.1489 -294 2011.5 2 0.0103 -0.1024 0.1229 -294 2008.5 4 -0.025 -0.0596 0.0096 -294 2009.5 4 -0.0349 -0.0696 -3e-04 -294 2010.5 4 0.0109 -0.0302 0.0519 -294 2009.5 6 -0.0078 -0.0299 0.0144 -295 2007.5 2 -0.0216 -0.1374 0.0942 -295 2008.5 2 0.2332 0.1177 0.3487 -295 2009.5 2 0.0948 -0.01 0.1996 -295 2010.5 2 0.1625 0.0632 0.2617 -295 2011.5 2 0.0526 -0.0584 0.1637 -295 2008.5 4 0.1152 0.0767 0.1538 -295 2009.5 4 0.1566 0.1182 0.195 -295 2010.5 4 0.1092 0.0686 0.1498 -295 2009.5 6 0.1192 0.0953 0.1432 -296 2009.5 6 0.0505 0.0376 0.0634 -296 2010.5 6 0.0431 0.0303 0.0558 -296 2011.5 6 0.0253 0.0122 0.0384 -296 2012.5 6 0.0373 0.0243 0.0504 -296 2013.5 6 0.0243 0.0115 0.0371 -296 2014.5 6 -0.0044 -0.0174 0.0087 -296 2015.5 6 -0.0175 -0.0305 -0.0044 -296 2016.5 6 -0.0018 -0.015 0.0113 -296 2017.5 6 -0.0149 -0.0279 -0.0019 -296 2018.5 6 -0.0234 -0.0358 -0.011 -296 2019.5 6 -0.0048 -0.0182 0.0085 -296 2020.5 6 -0.0243 -0.0379 -0.0107 -296 2012.5 12 0.0214 0.0169 0.0259 -296 2013.5 12 0.0143 0.0098 0.0187 -296 2014.5 12 0.0096 0.005 0.0142 -296 2015.5 12 0.0018 -0.0028 0.0064 -296 2016.5 12 -0.0056 -0.0104 -9e-04 -296 2017.5 12 -0.0128 -0.0177 -0.0079 -296 2015 17 0.0067 0.0039 0.0096 -297 2007.5 2 -0.3706 -0.5339 -0.2072 -297 2008.5 2 -0.2364 -0.413 -0.0598 -297 2009.5 2 -0.1485 -0.3278 0.0309 -297 2010.5 2 0.3057 0.1296 0.4819 -297 2011.5 2 -0.5483 -0.7683 -0.3283 -297 2008.5 4 -0.2503 -0.3072 -0.1933 -297 2009.5 4 -0.0386 -0.0952 0.0181 -297 2010.5 4 -0.0867 -0.158 -0.0155 -297 2009.5 6 -0.1536 -0.1902 -0.117 -298 2009.5 6 0.0099 -0.0233 0.0432 -298 2010.5 6 0.011 -0.0206 0.0425 -298 2011.5 6 0.0307 3e-04 0.0611 -298 2012.5 6 0.0646 0.0332 0.0959 -298 2013.5 6 0.073 0.0418 0.1043 -298 2014.5 6 0.0582 0.0288 0.0876 -298 2015.5 6 0.0498 0.0208 0.0788 -298 2016.5 6 0.0493 0.0211 0.0775 -298 2017.5 6 0.0421 0.0141 0.0701 -298 2018.5 6 0.0441 0.0168 0.0714 -298 2019.5 6 0.0376 0.0098 0.0653 -298 2020.5 6 0.0201 -0.0093 0.0495 -298 2012.5 12 0.0416 0.0291 0.0542 -298 2013.5 12 0.0453 0.0335 0.0572 -298 2014.5 12 0.0483 0.0365 0.0602 -298 2015.5 12 0.0514 0.0393 0.0634 -298 2016.5 12 0.0501 0.038 0.0623 -298 2017.5 12 0.0444 0.0321 0.0566 -298 2015 17 0.0415 0.0335 0.0495 -299 2007.5 2 -0.0167 -0.0965 0.063 -299 2008.5 2 0.0258 -0.0551 0.1067 -299 2009.5 2 0.0333 -0.0457 0.1123 -299 2010.5 2 0.1476 0.0629 0.2323 -299 2011.5 2 0.0145 -0.073 0.1021 -299 2008.5 4 0.0153 -0.0127 0.0433 -299 2009.5 4 0.0653 0.0357 0.095 -299 2010.5 4 0.0734 0.0393 0.1075 -299 2009.5 6 0.0479 0.0292 0.0666 -300 2007.5 2 0.0106 -0.0768 0.0979 -300 2008.5 2 0.0358 -0.0564 0.1281 -300 2009.5 2 0.0383 -0.0557 0.1323 -300 2010.5 2 -0.0347 -0.1298 0.0604 -300 2011.5 2 -0.0029 -0.0919 0.086 -300 2008.5 4 0.0293 -0.0244 0.083 -300 2009.5 4 0.0156 -0.029 0.0601 -300 2010.5 4 -0.0031 -0.0477 0.0415 -300 2009.5 6 0.0111 -0.0225 0.0448 -301 2009.5 6 0.0028 -0.0454 0.051 -301 2010.5 6 8e-04 -0.0461 0.0477 -301 2011.5 6 -0.0011 -0.0474 0.0452 -301 2012.5 6 -0.0037 -0.0495 0.042 -301 2013.5 6 -0.0062 -0.0515 0.0392 -301 2014.5 6 -0.0051 -0.0505 0.0403 -301 2015.5 6 1e-04 -0.0456 0.0458 -301 2016.5 6 0.0032 -0.0425 0.0488 -301 2017.5 6 0.0051 -0.0408 0.0509 -301 2018.5 6 0.0059 -0.0402 0.052 -301 2019.5 6 0.0033 -0.043 0.0497 -301 2020.5 6 -8e-04 -0.0488 0.0472 -301 2012.5 12 -0.0014 -0.0282 0.0255 -301 2013.5 12 -9e-04 -0.0273 0.0254 -301 2014.5 12 -2e-04 -0.0265 0.0262 -301 2015.5 12 2e-04 -0.0263 0.0267 -301 2016.5 12 3e-04 -0.0263 0.027 -301 2017.5 12 5e-04 -0.0268 0.0277 -301 2015 17 0 -0.0205 0.0204 -302 2009.5 6 -0.0827 -0.1396 -0.0257 -302 2010.5 6 -0.0594 -0.1165 -0.0023 -302 2011.5 6 0.0082 -0.0485 0.0648 -302 2012.5 6 0.0568 -8e-04 0.1145 -302 2013.5 6 0.0857 0.0304 0.1409 -302 2014.5 6 0.0929 0.0359 0.15 -302 2015.5 6 0.0555 -0.003 0.114 -302 2016.5 6 -0.0309 -0.0867 0.0248 -302 2017.5 6 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-0.0535 0.0174 -303 2017.5 12 -0.0131 -0.0476 0.0214 -303 2015 17 -6e-04 -0.0227 0.0216 -304 2009.5 6 -0.0018 -0.0361 0.0325 -304 2010.5 6 7e-04 -0.0324 0.0339 -304 2011.5 6 0.0029 -0.03 0.0358 -304 2012.5 6 0.0048 -0.0283 0.0379 -304 2013.5 6 0.0034 -0.0296 0.0363 -304 2014.5 6 2e-04 -0.0323 0.0328 -304 2015.5 6 -0.0037 -0.0362 0.0289 -304 2016.5 6 -0.0062 -0.0392 0.0267 -304 2017.5 6 -0.0062 -0.0396 0.0272 -304 2018.5 6 -0.0036 -0.0369 0.0297 -304 2019.5 6 -6e-04 -0.0342 0.0331 -304 2020.5 6 0.0019 -0.0328 0.0365 -304 2012.5 12 1e-04 -0.0198 0.02 -304 2013.5 12 -3e-04 -0.0199 0.0193 -304 2014.5 12 -8e-04 -0.0205 0.0188 -304 2015.5 12 -0.0012 -0.021 0.0186 -304 2016.5 12 -0.0015 -0.0216 0.0185 -304 2017.5 12 -0.0017 -0.0221 0.0187 -304 2015 17 -7e-04 -0.0164 0.015 -305 2007.5 2 0.1321 -0.1304 0.3945 -305 2008.5 2 -0.037 -0.2557 0.1816 -305 2009.5 2 -0.1423 -0.3903 0.1057 -305 2010.5 2 0.0318 -0.194 0.2575 -305 2011.5 2 -0.0171 -0.2559 0.2217 -305 2008.5 4 -0.0168 -0.1145 0.0808 -305 2009.5 4 -0.0571 -0.156 0.0417 -305 2010.5 4 -0.0339 -0.1371 0.0694 -305 2009.5 6 -0.0203 -0.0855 0.0448 -306 2007.5 2 -0.1592 -0.1951 -0.1233 -306 2008.5 2 0.0502 0.0118 0.0885 -306 2009.5 2 0.0766 0.0404 0.1128 -306 2010.5 2 -0.1715 -0.2068 -0.1362 -306 2011.5 2 -0.0262 -0.0696 0.0171 -306 2008.5 4 -0.0047 -0.0158 0.0064 -306 2009.5 4 -0.0057 -0.0182 0.0067 -306 2010.5 4 -0.0535 -0.0678 -0.0392 -306 2009.5 6 -0.0345 -0.0419 -0.0271 -307 2007.5 2 0.0813 -0.0978 0.2604 -307 2008.5 2 0.0314 -0.1354 0.1981 -307 2009.5 2 0.0761 -0.0931 0.2452 -307 2010.5 2 0.0399 -0.1233 0.2031 -307 2011.5 2 0.0364 -0.1457 0.2185 -307 2008.5 4 0.0598 -0.0239 0.1434 -307 2009.5 4 0.0518 -0.0266 0.1302 -307 2010.5 4 0.0497 -0.0389 0.1384 -307 2009.5 6 0.0527 -0.0121 0.1174 -308 2009.5 6 -0.0317 -0.0739 0.0105 -308 2010.5 6 -0.06 -0.104 -0.016 -308 2011.5 6 -0.0874 -0.1341 -0.0408 -308 2012.5 6 -0.1017 -0.1488 -0.0545 -308 2013.5 6 -0.1093 -0.1593 -0.0594 -308 2014.5 6 -0.0533 -0.1019 -0.0047 -308 2015.5 6 -0.019 -0.0673 0.0292 -308 2016.5 6 0.0089 -0.0408 0.0586 -308 2017.5 6 0.0114 -0.0384 0.0613 -308 2018.5 6 0.0028 -0.0473 0.053 -308 2019.5 6 -0.0427 -0.0949 0.0095 -308 2020.5 6 -0.0861 -0.1459 -0.0263 -308 2012.5 12 -0.0584 -0.0766 -0.0403 -308 2013.5 12 -0.0528 -0.0707 -0.0349 -308 2014.5 12 -0.0451 -0.0631 -0.0271 -308 2015.5 12 -0.0386 -0.0568 -0.0204 -308 2016.5 12 -0.0419 -0.0613 -0.0225 -308 2017.5 12 -0.0383 -0.0598 -0.0168 -308 2015 17 -0.0492 -0.0618 -0.0366 -309 2009.5 6 -0.0626 -0.1387 0.0134 -309 2010.5 6 -0.0319 -0.1059 0.0421 -309 2011.5 6 -0.0073 -0.0798 0.0652 -309 2012.5 6 0.0105 -0.0576 0.0785 -309 2013.5 6 0.029 -0.042 0.1 -309 2014.5 6 0.0499 -0.0241 0.1238 -309 2015.5 6 0.0496 -0.0227 0.122 -309 2016.5 6 0.0491 -0.0211 0.1192 -309 2017.5 6 0.0318 -0.0315 0.095 -309 2018.5 6 -0.0181 -0.0855 0.0494 -309 2019.5 6 -0.0694 -0.1445 0.0056 -309 2020.5 6 -0.0748 -0.1519 0.0022 -309 2012.5 12 0.0061 -0.024 0.0362 -309 2013.5 12 0.0164 -0.0134 0.0463 -309 2014.5 12 0.0222 -0.0076 0.0521 -309 2015.5 12 0.0187 -0.0118 0.0493 -309 2016.5 12 0.0117 -0.0211 0.0445 -309 2017.5 12 3e-04 -0.0342 0.0349 -309 2015 17 -0.0025 -0.0238 0.0187 -310 2007.5 2 -0.0996 -0.247 0.0478 -310 2008.5 2 -0.3353 -0.5062 -0.1643 -310 2009.5 2 -0.066 -0.2416 0.1096 -310 2010.5 2 0.0412 -0.1384 0.2207 -310 2011.5 2 -0.3932 -0.637 -0.1494 -310 2008.5 4 -0.1838 -0.2406 -0.127 -310 2009.5 4 -0.1146 -0.1712 -0.0581 -310 2010.5 4 -0.1213 -0.1972 -0.0454 -310 2009.5 6 -0.1546 -0.1951 -0.1141 -311 2007.5 2 -0.2115 -0.4641 0.0411 -311 2008.5 2 -0.4451 -0.7623 -0.1279 -311 2009.5 2 -0.0145 -0.3147 0.2856 -311 2010.5 2 -0.1626 -0.4915 0.1663 -311 2011.5 2 0.0236 -0.328 0.3753 -311 2008.5 4 -0.2458 -0.3593 -0.1323 -311 2009.5 4 -0.1881 -0.3196 -0.0566 -311 2010.5 4 -0.0623 -0.2093 0.0847 -311 2009.5 6 -0.1695 -0.2582 -0.0807 -312 2007.5 2 -0.1306 -0.3963 0.135 -312 2008.5 2 0.0168 -0.236 0.2696 -312 2009.5 2 -0.0607 -0.3102 0.1888 -312 2010.5 2 -0.1108 -0.3707 0.1492 -312 2011.5 2 -0.0477 -0.3266 0.2312 -312 2008.5 4 -0.0507 -0.1762 0.0749 -312 2009.5 4 -0.0525 -0.1742 0.0693 -312 2010.5 4 -0.0768 -0.2134 0.0597 -312 2009.5 6 -0.0626 -0.1566 0.0315 -313 2007.5 2 0.2231 -0.0717 0.5178 -313 2008.5 2 -0.0149 -0.2959 0.2662 -313 2009.5 2 -0.6091 -0.9853 -0.2329 -313 2010.5 2 -0.2385 -0.6014 0.1245 -313 2011.5 2 -0.0763 -0.4997 0.3471 -313 2008.5 4 -0.1217 -0.2443 8e-04 -313 2009.5 4 -0.3196 -0.4672 -0.1719 -313 2010.5 4 -0.301 -0.476 -0.1259 -313 2009.5 6 -0.1935 -0.2905 -0.0966 -314 2009.5 6 -0.0333 -0.0453 -0.0214 -314 2010.5 6 -0.0628 -0.0747 -0.0509 -314 2011.5 6 -0.024 -0.0358 -0.0123 -314 2012.5 6 0.0422 0.0302 0.0543 -314 2013.5 6 0.0518 0.0399 0.0637 -314 2014.5 6 0.0373 0.0252 0.0494 -314 2015.5 6 0.0437 0.0314 0.056 -314 2016.5 6 -0.0295 -0.0414 -0.0177 -314 2017.5 6 -0.0629 -0.0752 -0.0506 -314 2018.5 6 -0.0405 -0.0515 -0.0294 -314 2019.5 6 -0.0433 -0.055 -0.0317 -314 2020.5 6 -0.0303 -0.0425 -0.0181 -314 2012.5 12 0.0032 -6e-04 0.007 -314 2013.5 12 -0.0025 -0.0064 0.0013 -314 2014.5 12 -0.0025 -0.0067 0.0017 -314 2015.5 12 0.0025 -0.0017 0.0067 -314 2016.5 12 -0.0027 -0.007 0.0017 -314 2017.5 12 -0.0141 -0.0185 -0.0096 -314 2015 17 -0.01 -0.0124 -0.0076 -315 2007.5 2 -0.008 -0.0366 0.0206 -315 2008.5 2 -0.0369 -0.068 -0.0058 -315 2009.5 2 -0.0972 -0.1286 -0.0657 -315 2010.5 2 -0.4741 -0.5094 -0.4387 -315 2011.5 2 0.1544 0.1097 0.199 -315 2008.5 4 -0.0463 -0.0559 -0.0368 -315 2009.5 4 -0.1922 -0.2035 -0.1808 -315 2010.5 4 -0.1725 -0.1858 -0.1591 -315 2009.5 6 -0.1209 -0.1278 -0.114 -316 2007.5 2 0.0463 -0.09 0.1826 -316 2008.5 2 0.0079 -0.1205 0.1362 -316 2009.5 2 0.0925 -0.0417 0.2268 -316 2010.5 2 -0.0629 -0.1952 0.0695 -316 2011.5 2 0.0292 -0.1072 0.1656 -316 2008.5 4 0.0447 -0.0099 0.0992 -316 2009.5 4 0.0204 -0.0278 0.0687 -316 2010.5 4 0.0113 -0.0413 0.064 -316 2009.5 6 0.0219 -0.0113 0.0551 -317 2007.5 2 0.2984 -0.2252 0.822 -317 2008.5 2 0.7061 0.1891 1.2231 -317 2009.5 2 0.354 -0.0302 0.7381 -317 2010.5 2 -0.0052 -0.4276 0.4171 -317 2011.5 2 -0.4032 -0.9598 0.1535 -317 2008.5 4 0.4781 0.283 0.6731 -317 2009.5 4 0.3518 0.1528 0.5508 -317 2010.5 4 -0.0168 -0.2233 0.1896 -317 2009.5 6 0.2362 0.106 0.3665 -318 2007.5 2 0.0489 -0.298 0.3957 -318 2008.5 2 0.1665 -0.1734 0.5065 -318 2009.5 2 0.1965 -0.131 0.524 -318 2010.5 2 0.1395 -0.1725 0.4515 -318 2011.5 2 0.0448 -0.3104 0.4 -318 2008.5 4 0.1402 -0.0146 0.295 -318 2009.5 4 0.1704 0.0145 0.3263 -318 2010.5 4 0.1282 -0.0393 0.2957 -318 2009.5 6 0.1338 0.0201 0.2476 -319 2007.5 2 0.0532 -0.0426 0.1489 -319 2008.5 2 0.0368 -0.0523 0.1259 -319 2009.5 2 -0.0211 -0.1131 0.0708 -319 2010.5 2 -0.047 -0.1401 0.0461 -319 2011.5 2 -0.0329 -0.1344 0.0687 -319 2008.5 4 0.0243 -0.0196 0.0683 -319 2009.5 4 -0.0115 -0.0538 0.0308 -319 2010.5 4 -0.035 -0.0806 0.0106 -319 2009.5 6 -0.0049 -0.036 0.0263 -320 2007.5 2 -0.1957 -0.3958 0.0045 -320 2008.5 2 -0.2118 -0.4306 0.007 -320 2009.5 2 -0.178 -0.3964 0.0404 -320 2010.5 2 -0.6096 -0.8475 -0.3718 -320 2011.5 2 -0.2881 -0.6059 0.0298 -320 2008.5 4 -0.1968 -0.2685 -0.1252 -320 2009.5 4 -0.3176 -0.3986 -0.2367 -320 2010.5 4 -0.3837 -0.4976 -0.2697 -320 2009.5 6 -0.3026 -0.3629 -0.2423 -321 2009.5 6 0.0216 -0.0222 0.0653 -321 2010.5 6 0.0503 0.0071 0.0936 -321 2011.5 6 0.0314 -0.0104 0.0732 -321 2012.5 6 0.0094 -0.0309 0.0497 -321 2013.5 6 0.0013 -0.0391 0.0418 -321 2014.5 6 1e-04 -0.0414 0.0415 -321 2015.5 6 -0.0083 -0.0502 0.0337 -321 2016.5 6 -0.0177 -0.0606 0.0251 -321 2017.5 6 -0.0158 -0.0591 0.0275 -321 2018.5 6 0.0017 -0.0409 0.0442 -321 2019.5 6 0.0042 -0.0392 0.0475 -321 2020.5 6 -0.0093 -0.0542 0.0355 -321 2012.5 12 0.0134 -0.0069 0.0336 -321 2013.5 12 0.01 -0.0098 0.0298 -321 2014.5 12 0.0033 -0.0165 0.023 -321 2015.5 12 -0.001 -0.0205 0.0186 -321 2016.5 12 -0.0034 -0.0233 0.0165 -321 2017.5 12 -0.006 -0.0264 0.0144 -321 2015 17 0.0036 -0.0105 0.0177 -322 2009.5 6 -0.0292 -0.0582 -3e-04 -322 2010.5 6 -0.0374 -0.0663 -0.0085 -322 2011.5 6 -0.0779 -0.1079 -0.0479 -322 2012.5 6 -0.0647 -0.0947 -0.0348 -322 2013.5 6 0.0017 -0.0274 0.0309 -322 2014.5 6 0.0481 0.0188 0.0773 -322 2015.5 6 0.0853 0.0563 0.1143 -322 2016.5 6 0.1031 0.0735 0.1327 -322 2017.5 6 0.0707 0.0422 0.0993 -322 2018.5 6 0.009 -0.0184 0.0365 -322 2019.5 6 -0.0211 -0.0498 0.0077 -322 2020.5 6 -0.0218 -0.0514 0.0077 -322 2012.5 12 -0.0025 -0.0128 0.0077 -322 2013.5 12 0.0139 0.0035 0.0242 -322 2014.5 12 0.0193 0.0088 0.0298 -322 2015.5 12 0.0218 0.0113 0.0323 -322 2016.5 12 0.0255 0.0147 0.0364 -322 2017.5 12 0.0354 0.0242 0.0466 -322 2015 17 0.0098 0.0032 0.0164 -323 2007.5 2 -0.0533 -0.2802 0.1735 -323 2008.5 2 0.0286 -0.1753 0.2326 -323 2009.5 2 0.0612 -0.137 0.2594 -323 2010.5 2 0.0824 -0.1146 0.2794 -323 2011.5 2 0.0472 -0.1558 0.2502 -323 2008.5 4 0.0138 -0.0751 0.1027 -323 2009.5 4 0.0578 -0.033 0.1487 -323 2010.5 4 0.0655 -0.0301 0.1612 -323 2009.5 6 0.0402 -0.0201 0.1006 -324 2009.5 6 -0.0446 -0.0679 -0.0213 -324 2010.5 6 -0.0274 -0.0502 -0.0045 -324 2011.5 6 -0.0188 -0.0422 0.0045 -324 2012.5 6 -0.0154 -0.0391 0.0084 -324 2013.5 6 -0.0169 -0.0405 0.0067 -324 2014.5 6 -0.0317 -0.0554 -0.008 -324 2015.5 6 -0.0419 -0.0659 -0.0179 -324 2016.5 6 -0.0239 -0.048 1e-04 -324 2017.5 6 -0.0097 -0.0336 0.0142 -324 2018.5 6 0.0028 -0.02 0.0257 -324 2019.5 6 0.0086 -0.0152 0.0325 -324 2020.5 6 0.0244 -7e-04 0.0494 -324 2012.5 12 -0.0296 -0.0382 -0.021 -324 2013.5 12 -0.0247 -0.0333 -0.0161 -324 2014.5 12 -0.023 -0.0318 -0.0141 -324 2015.5 12 -0.0166 -0.0255 -0.0077 -324 2016.5 12 -0.0127 -0.0219 -0.0035 -324 2017.5 12 -0.0079 -0.0173 0.0015 -324 2015 17 -0.0165 -0.0221 -0.011 -325 2007.5 2 0.2218 0.0146 0.429 -325 2008.5 2 0.144 -0.0413 0.3293 -325 2009.5 2 -0.2662 -0.4637 -0.0688 -325 2010.5 2 0.1657 -0.0298 0.3612 -325 2011.5 2 -0.1441 -0.3613 0.0732 -325 2008.5 4 0.0443 -0.0251 0.1136 -325 2009.5 4 -0.0136 -0.0787 0.0515 -325 2010.5 4 -0.0568 -0.1301 0.0165 -325 2009.5 6 0.0134 -0.029 0.0558 -326 2007.5 2 -0.0962 -0.2268 0.0344 -326 2008.5 2 -0.0486 -0.1799 0.0826 -326 2009.5 2 -0.097 -0.2349 0.0409 -326 2010.5 2 0.098 -0.0431 0.2391 -326 2011.5 2 0.0054 -0.1385 0.1494 -326 2008.5 4 -0.0774 -0.1314 -0.0233 -326 2009.5 4 -0.024 -0.0732 0.0253 -326 2010.5 4 0.0117 -0.0444 0.0679 -326 2009.5 6 -0.0266 -0.0583 0.0051 -327 2009.5 6 -0.002 -0.058 0.0541 -327 2010.5 6 -0.0125 -0.0663 0.0414 -327 2011.5 6 -0.0264 -0.0799 0.0272 -327 2012.5 6 -0.0327 -0.0863 0.0208 -327 2013.5 6 -0.0369 -0.0909 0.0171 -327 2014.5 6 -0.0368 -0.0915 0.018 -327 2015.5 6 -0.0283 -0.0827 0.0261 -327 2016.5 6 -0.0139 -0.0679 0.0401 -327 2017.5 6 0.0023 -0.0524 0.057 -327 2018.5 6 0.0189 -0.0389 0.0767 -327 2019.5 6 0.034 -0.0292 0.0973 -327 2020.5 6 0.037 -0.0281 0.102 -327 2012.5 12 -0.0225 -0.0532 0.0082 -327 2013.5 12 -0.0229 -0.0529 0.0071 -327 2014.5 12 -0.0208 -0.0504 0.0087 -327 2015.5 12 -0.0156 -0.0449 0.0138 -327 2016.5 12 -0.0073 -0.0373 0.0228 -327 2017.5 12 0.0017 -0.0303 0.0336 -327 2015 17 -0.0081 -0.0312 0.0149 -328 2009.5 6 -0.0192 -0.0692 0.0307 -328 2010.5 6 -0.0619 -0.1081 -0.0156 -328 2011.5 6 -0.0782 -0.1252 -0.0312 -328 2012.5 6 -0.0934 -0.1426 -0.0442 -328 2013.5 6 -0.0761 -0.1248 -0.0273 -328 2014.5 6 -0.0682 -0.1196 -0.0169 -328 2015.5 6 -0.0598 -0.113 -0.0065 -328 2016.5 6 -0.0565 -0.1109 -0.0022 -328 2017.5 6 -0.0261 -0.0818 0.0297 -328 2018.5 6 -0.0074 -0.0617 0.0469 -328 2019.5 6 0.0144 -0.0427 0.0714 -328 2020.5 6 0.0219 -0.0409 0.0847 -328 2012.5 12 -0.056 -0.0765 -0.0356 -328 2013.5 12 -0.0657 -0.0861 -0.0453 -328 2014.5 12 -0.0642 -0.0854 -0.0429 -328 2015.5 12 -0.0531 -0.0741 -0.0321 -328 2016.5 12 -0.0365 -0.0579 -0.015 -328 2017.5 12 -0.0259 -0.0491 -0.0026 -328 2015 17 -0.0399 -0.0537 -0.0261 -329 2009.5 6 -0.0085 -0.0516 0.0346 -329 2010.5 6 -0.0071 -0.0493 0.035 -329 2011.5 6 0.0021 -0.0401 0.0443 -329 2012.5 6 0.0265 -0.0139 0.067 -329 2013.5 6 0.042 0.0016 0.0823 -329 2014.5 6 0.0475 0.0069 0.088 -329 2015.5 6 0.0495 0.0082 0.0908 -329 2016.5 6 0.044 0.0044 0.0835 -329 2017.5 6 0.0434 0.0053 0.0815 -329 2018.5 6 0.0292 -0.0079 0.0663 -329 2019.5 6 0.01 -0.0307 0.0507 -329 2020.5 6 0.0031 -0.0395 0.0457 -329 2012.5 12 0.0195 0.0024 0.0367 -329 2013.5 12 0.0263 0.0095 0.0431 -329 2014.5 12 0.0353 0.0172 0.0535 -329 2015.5 12 0.0371 0.0188 0.0554 -329 2016.5 12 0.032 0.0146 0.0495 -329 2017.5 12 0.0295 0.0113 0.0477 -329 2015 17 0.0229 0.0114 0.0345 -330 2009.5 6 0.0409 -0.0596 0.1415 -330 2010.5 6 0.0812 -0.0123 0.1747 -330 2011.5 6 0.1108 0.0158 0.2058 -330 2012.5 6 0.098 0.0011 0.195 -330 2013.5 6 0.0796 -0.0158 0.175 -330 2014.5 6 0.0768 -0.0156 0.1691 -330 2015.5 6 0.0641 -0.0206 0.1488 -330 2016.5 6 0.0612 -0.0315 0.1539 -330 2017.5 6 0.0597 -0.0414 0.1608 -330 2018.5 6 0.062 -0.0418 0.1659 -330 2019.5 6 0.0671 -0.0397 0.1738 -330 2020.5 6 0.0609 -0.0579 0.1798 -330 2012.5 12 0.0743 0.0307 0.1179 -330 2013.5 12 0.0747 0.0315 0.1178 -330 2014.5 12 0.0778 0.0329 0.1226 -330 2015.5 12 0.0762 0.0291 0.1234 -330 2016.5 12 0.0717 0.0223 0.121 -330 2017.5 12 0.0639 0.01 0.1179 -330 2015 17 0.0691 0.0336 0.1045 -331 2009.5 6 0.0595 0.0319 0.0871 -331 2010.5 6 0.0477 0.0219 0.0736 -331 2011.5 6 0.0336 0.0078 0.0594 -331 2012.5 6 0.0224 -0.0034 0.0482 -331 2013.5 6 5e-04 -0.0253 0.0263 -331 2014.5 6 -0.0181 -0.0447 0.0084 -331 2015.5 6 -0.0019 -0.0275 0.0237 -331 2016.5 6 0.0293 0.004 0.0546 -331 2017.5 6 0.0428 0.0173 0.0684 -331 2018.5 6 0.0424 0.0179 0.0669 -331 2019.5 6 0.0376 0.0129 0.0622 -331 2020.5 6 0.0274 0.0018 0.0531 -331 2012.5 12 0.0233 0.0132 0.0334 -331 2013.5 12 0.021 0.0113 0.0307 -331 2014.5 12 0.0212 0.0115 0.031 -331 2015.5 12 0.019 0.0092 0.0288 -331 2016.5 12 0.0186 0.0086 0.0285 -331 2017.5 12 0.0208 0.0106 0.0311 -331 2015 17 0.0261 0.0198 0.0324 -332 2009.5 6 0.0208 -0.0384 0.0801 -332 2010.5 6 0.0209 -0.0344 0.0762 -332 2011.5 6 0.0143 -0.0354 0.064 -332 2012.5 6 0.0091 -0.0407 0.059 -332 2013.5 6 0.0087 -0.041 0.0584 -332 2014.5 6 0.0136 -0.0342 0.0614 -332 2015.5 6 0.0201 -0.0296 0.0697 -332 2016.5 6 0.0278 -0.0303 0.086 -332 2017.5 6 0.0209 -0.0327 0.0745 -332 2018.5 6 0.0087 -0.0405 0.0579 -332 2019.5 6 -0.0023 -0.0553 0.0508 -332 2020.5 6 -0.0089 -0.0673 0.0496 -332 2012.5 12 0.0163 -0.0126 0.0453 -332 2013.5 12 0.0181 -0.0112 0.0474 -332 2014.5 12 0.0161 -0.0112 0.0435 -332 2015.5 12 0.0141 -0.0124 0.0406 -332 2016.5 12 0.0114 -0.015 0.0378 -332 2017.5 12 0.0096 -0.0177 0.0369 -332 2015 17 0.0127 -0.0084 0.0338 -333 2007.5 2 0.0749 -0.0284 0.1782 -333 2008.5 2 -0.2272 -0.3375 -0.1168 -333 2009.5 2 -0.087 -0.1894 0.0155 -333 2010.5 2 0.2121 0.1048 0.3193 -333 2011.5 2 -0.0541 -0.1634 0.0551 -333 2008.5 4 -0.0945 -0.1304 -0.0585 -333 2009.5 4 -0.0393 -0.0728 -0.0058 -333 2010.5 4 0.0425 0.0033 0.0817 -333 2009.5 6 -0.0228 -0.0434 -0.0023 -334 2009.5 6 0.087 -0.0061 0.1801 -334 2010.5 6 0.0945 0.0058 0.1832 -334 2011.5 6 0.0794 -0.013 0.1717 -334 2012.5 6 0.0107 -0.0791 0.1004 -334 2013.5 6 -0.0929 -0.1844 -0.0014 -334 2014.5 6 -0.1794 -0.2814 -0.0773 -334 2015.5 6 -0.2199 -0.3357 -0.1042 -334 2016.5 6 -0.1875 -0.2952 -0.0799 -334 2017.5 6 -0.1757 -0.2874 -0.0641 -334 2018.5 6 -0.1396 -0.2587 -0.0205 -334 2019.5 6 -0.1043 -0.236 0.0274 -334 2020.5 6 -0.1029 -0.2511 0.0452 -334 2012.5 12 -0.0387 -0.0816 0.0041 -334 2013.5 12 -0.0674 -0.1113 -0.0234 -334 2014.5 12 -0.0926 -0.1389 -0.0464 -334 2015.5 12 -0.1241 -0.1741 -0.0741 -334 2016.5 12 -0.1431 -0.1969 -0.0893 -334 2017.5 12 -0.1502 -0.2098 -0.0905 -334 2015 17 -0.082 -0.1175 -0.0464 -335 2007.5 2 -0.2399 -0.4265 -0.0533 -335 2008.5 2 0.0371 -0.1465 0.2208 -335 2009.5 2 0.1966 0.0017 0.3914 -335 2010.5 2 -0.3198 -0.516 -0.1236 -335 2011.5 2 -0.1726 -0.3938 0.0486 -335 2008.5 4 0.0018 -0.0619 0.0656 -335 2009.5 4 -0.0062 -0.0759 0.0635 -335 2010.5 4 -0.1207 -0.2074 -0.034 -335 2009.5 6 -0.073 -0.1196 -0.0263 -336 2007.5 2 0.1784 0.0608 0.296 -336 2008.5 2 -0.0996 -0.22 0.0209 -336 2009.5 2 0.0342 -0.0826 0.151 -336 2010.5 2 0.2539 0.1453 0.3625 -336 2011.5 2 0.1309 0.0123 0.2494 -336 2008.5 4 0.024 -0.0158 0.0637 -336 2009.5 4 0.06 0.0225 0.0974 -336 2010.5 4 0.1511 0.1055 0.1966 -336 2009.5 6 0.0882 0.0644 0.1121 -337 2009.5 6 0.0267 0.0037 0.0496 -337 2010.5 6 0.0084 -0.0135 0.0302 -337 2011.5 6 -0.0012 -0.0228 0.0205 -337 2012.5 6 -0.0168 -0.0384 0.0048 -337 2013.5 6 -0.0223 -0.0444 -2e-04 -337 2014.5 6 -0.0154 -0.0375 0.0067 -337 2015.5 6 -0.0203 -0.042 0.0014 -337 2016.5 6 -0.0288 -0.0508 -0.0067 -337 2017.5 6 -0.0083 -0.0301 0.0135 -337 2018.5 6 0.0028 -0.0188 0.0243 -337 2019.5 6 0.0011 -0.0215 0.0237 -337 2020.5 6 -0.0084 -0.0338 0.0169 -337 2012.5 12 -0.0031 -0.0115 0.0054 -337 2013.5 12 -0.0108 -0.0191 -0.0025 -337 2014.5 12 -0.0134 -0.0218 -0.005 -337 2015.5 12 -0.0142 -0.0227 -0.0058 -337 2016.5 12 -0.0118 -0.0206 -0.003 -337 2017.5 12 -0.0122 -0.0216 -0.0028 -337 2015 17 -0.0071 -0.0127 -0.0014 -338 2007.5 2 -0.0696 -0.3835 0.2443 -338 2008.5 2 -0.0569 -0.3434 0.2295 -338 2009.5 2 -0.0352 -0.3041 0.2337 -338 2010.5 2 -0.4776 -0.7539 -0.2013 -338 2011.5 2 -0.3878 -0.7578 -0.0178 -338 2008.5 4 -0.0542 -0.1558 0.0474 -338 2009.5 4 -0.1744 -0.2771 -0.0717 -338 2010.5 4 -0.3179 -0.4467 -0.1891 -338 2009.5 6 -0.1966 -0.2702 -0.1229 -339 2009.5 6 -0.013 -0.0342 0.0083 -339 2010.5 6 0.0049 -0.018 0.0278 -339 2011.5 6 -0.0194 -0.0448 0.0059 -339 2012.5 6 -0.0186 -0.0415 0.0043 -339 2013.5 6 -0.0119 -0.0348 0.011 -339 2014.5 6 -0.046 -0.0701 -0.022 -339 2015.5 6 -0.0394 -0.0639 -0.0149 -339 2016.5 6 -0.0329 -0.0584 -0.0075 -339 2017.5 6 -0.0396 -0.0639 -0.0153 -339 2018.5 6 -0.0163 -0.0399 0.0072 -339 2019.5 6 0.0157 -0.0097 0.041 -339 2020.5 6 0.0197 -0.0079 0.0473 -339 2012.5 12 -0.026 -0.0341 -0.018 -339 2013.5 12 -0.0236 -0.0321 -0.0151 -339 2014.5 12 -0.0247 -0.0334 -0.0159 -339 2015.5 12 -0.0227 -0.0311 -0.0143 -339 2016.5 12 -0.0242 -0.0332 -0.0152 -339 2017.5 12 -0.0183 -0.028 -0.0087 -339 2015 17 -0.0186 -0.024 -0.0132 -340 2009.5 6 0.0016 -0.0141 0.0174 -340 2010.5 6 7e-04 -0.0144 0.0159 -340 2011.5 6 9e-04 -0.0141 0.0159 -340 2012.5 6 0.0018 -0.0132 0.0168 -340 2013.5 6 0.0025 -0.0128 0.0178 -340 2014.5 6 0.0021 -0.0131 0.0172 -340 2015.5 6 0.0019 -0.0133 0.0171 -340 2016.5 6 0.0011 -0.0139 0.0161 -340 2017.5 6 2e-04 -0.0149 0.0153 -340 2018.5 6 -7e-04 -0.0161 0.0147 -340 2019.5 6 -0.0013 -0.0172 0.0145 -340 2020.5 6 -0.0018 -0.0183 0.0148 -340 2012.5 12 0.0017 -0.0077 0.0112 -340 2013.5 12 0.0015 -0.0077 0.0106 -340 2014.5 12 0.0013 -0.0078 0.0103 -340 2015.5 12 0.0011 -0.008 0.0101 -340 2016.5 12 7e-04 -0.0085 0.0099 -340 2017.5 12 3e-04 -0.0092 0.0097 -340 2015 17 9e-04 -0.0064 0.0082 -341 2009.5 6 0.0774 0.0171 0.1376 -341 2010.5 6 0.0431 -0.005 0.0913 -341 2011.5 6 0.0046 -0.0405 0.0496 -341 2012.5 6 -0.0255 -0.0738 0.0227 -341 2013.5 6 -0.0262 -0.0729 0.0204 -341 2014.5 6 -0.0247 -0.0707 0.0213 -341 2015.5 6 -5e-04 -0.0454 0.0444 -341 2016.5 6 0 -0.0456 0.0457 -341 2017.5 6 -0.0015 -0.0473 0.0442 -341 2018.5 6 -0.0284 -0.0727 0.0158 -341 2019.5 6 -0.0383 -0.0849 0.0083 -341 2020.5 6 -0.0538 -0.1074 -3e-04 -341 2012.5 12 0.0157 -0.0057 0.0371 -341 2013.5 12 0.0017 -0.0177 0.0211 -341 2014.5 12 -0.0087 -0.0282 0.0107 -341 2015.5 12 -0.0171 -0.0372 0.0029 -341 2016.5 12 -0.0201 -0.0404 2e-04 -341 2017.5 12 -0.023 -0.0442 -0.0018 -341 2015 17 -0.0041 -0.0178 0.0097 -342 2009.5 6 0.1535 0.0341 0.2729 -342 2010.5 6 0.0972 -0.0061 0.2006 -342 2011.5 6 0.0526 -0.0527 0.1579 -342 2012.5 6 0.0847 -0.0129 0.1822 -342 2013.5 6 0.1421 0.0514 0.2329 -342 2014.5 6 0.1859 0.0987 0.2731 -342 2015.5 6 0.1832 0.092 0.2745 -342 2016.5 6 0.1371 0.0451 0.2291 -342 2017.5 6 0.0578 -0.0277 0.1433 -342 2018.5 6 0.0316 -0.0489 0.1121 -342 2019.5 6 0.0604 -0.0167 0.1376 -342 2020.5 6 0.0869 0.0038 0.17 -342 2012.5 12 0.1314 0.088 0.1749 -342 2013.5 12 0.118 0.0794 0.1566 -342 2014.5 12 0.1141 0.077 0.1513 -342 2015.5 12 0.1078 0.0721 0.1436 -342 2016.5 12 0.1117 0.0755 0.1479 -342 2017.5 12 0.1031 0.0664 0.1398 -342 2015 17 0.1109 0.0844 0.1375 -343 2007.5 2 -0.0053 -0.1374 0.1268 -343 2008.5 2 -0.0639 -0.2035 0.0757 -343 2009.5 2 0.0855 -0.0514 0.2225 -343 2010.5 2 0.1359 -0.0037 0.2756 -343 2011.5 2 -0.0069 -0.1594 0.1456 -343 2008.5 4 -0.0015 -0.0531 0.0502 -343 2009.5 4 0.0558 0.0035 0.1081 -343 2010.5 4 0.0779 0.0121 0.1437 -343 2009.5 6 0.0367 0.0018 0.0716 -344 2007.5 2 -0.0239 -0.0698 0.022 -344 2008.5 2 -0.0066 -0.0557 0.0425 -344 2009.5 2 -0.1473 -0.1958 -0.0988 -344 2010.5 2 -0.017 -0.0648 0.0307 -344 2011.5 2 -0.1187 -0.1784 -0.0591 -344 2008.5 4 -0.054 -0.0692 -0.0388 -344 2009.5 4 -0.066 -0.0821 -0.0499 -344 2010.5 4 -0.0866 -0.1063 -0.067 -344 2009.5 6 -0.0636 -0.0739 -0.0534 -345 2007.5 2 0.0347 -0.1579 0.2272 -345 2008.5 2 0.0169 -0.17 0.2039 -345 2009.5 2 0.0719 -0.1103 0.254 -345 2010.5 2 0.1066 -0.0698 0.2829 -345 2011.5 2 -0.0618 -0.2673 0.1437 -345 2008.5 4 0.0387 -0.0309 0.1084 -345 2009.5 4 0.0658 -0.0015 0.1331 -345 2010.5 4 0.0456 -0.0301 0.1213 -345 2009.5 6 0.0428 -0.0014 0.087 -346 2009.5 6 0.0106 -0.0297 0.0509 -346 2010.5 6 0.0041 -0.0344 0.0426 -346 2011.5 6 0.0071 -0.0301 0.0443 -346 2012.5 6 0.0157 -0.0196 0.051 -346 2013.5 6 0.0291 -0.0093 0.0675 -346 2014.5 6 0.0358 -0.0062 0.0778 -346 2015.5 6 0.0258 -0.0119 0.0636 -346 2016.5 6 0.0035 -0.031 0.038 -346 2017.5 6 -0.0138 -0.0507 0.023 -346 2018.5 6 -0.0218 -0.0606 0.0171 -346 2019.5 6 -0.0155 -0.0516 0.0205 -346 2020.5 6 0.0015 -0.0353 0.0383 -346 2012.5 12 0.0169 -0.0012 0.0349 -346 2013.5 12 0.0146 -0.0022 0.0314 -346 2014.5 12 0.0109 -0.0052 0.0271 -346 2015.5 12 0.0081 -0.0078 0.024 -346 2016.5 12 0.0062 -0.0098 0.0223 -346 2017.5 12 0.005 -0.0114 0.0214 -346 2015 17 0.0086 -0.0029 0.0201 -347 2007.5 2 -0.1207 -0.2826 0.0412 -347 2008.5 2 -0.1734 -0.3461 -6e-04 -347 2009.5 2 -0.2275 -0.4142 -0.0408 -347 2010.5 2 -0.349 -0.5732 -0.1248 -347 2011.5 2 -0.0168 -0.2625 0.2289 -347 2008.5 4 -0.1738 -0.2381 -0.1095 -347 2009.5 4 -0.2477 -0.3267 -0.1687 -347 2010.5 4 -0.2129 -0.3079 -0.1178 -347 2009.5 6 -0.1975 -0.2495 -0.1455 -348 2007.5 2 0.232 -0.1394 0.6033 -348 2008.5 2 0.1177 -0.2459 0.4812 -348 2009.5 2 0.2197 -0.1715 0.6109 -348 2010.5 2 -0.1058 -0.4846 0.273 -348 2011.5 2 -0.2392 -0.7596 0.2813 -348 2008.5 4 0.1826 0.0257 0.3394 -348 2009.5 4 0.0914 -0.0781 0.261 -348 2010.5 4 -0.0481 -0.2599 0.1636 -348 2009.5 6 0.0582 -0.0702 0.1866 -349 2007.5 2 0.0534 0.0226 0.0842 -349 2008.5 2 -0.0086 -0.0411 0.0238 -349 2009.5 2 -0.0167 -0.0486 0.0153 -349 2010.5 2 -0.0085 -0.0395 0.0225 -349 2011.5 2 0.0698 0.0329 0.1067 -349 2008.5 4 0.0076 -0.0027 0.0178 -349 2009.5 4 -0.0118 -0.0225 -0.0011 -349 2010.5 4 0.0125 1e-04 0.025 -349 2009.5 6 0.0094 0.0028 0.016 -350 2007.5 2 -0.0342 -0.2468 0.1783 -350 2008.5 2 0.0571 -0.1515 0.2657 -350 2009.5 2 -0.0566 -0.2582 0.145 -350 2010.5 2 -0.0562 -0.2592 0.1468 -350 2011.5 2 -0.0613 -0.2858 0.1632 -350 2008.5 4 -0.0044 -0.0915 0.0827 -350 2009.5 4 -0.0224 -0.1073 0.0625 -350 2010.5 4 -0.0579 -0.1543 0.0385 -350 2009.5 6 -0.028 -0.0871 0.0311 -351 2007.5 2 0.3077 0.2499 0.3655 -351 2008.5 2 0.1191 0.061 0.1773 -351 2009.5 2 0.1062 0.05 0.1625 -351 2010.5 2 0.0597 0.0059 0.1136 -351 2011.5 2 0.1191 0.055 0.1831 -351 2008.5 4 0.1718 0.1529 0.1908 -351 2009.5 4 0.0962 0.0773 0.115 -351 2010.5 4 0.0915 0.0696 0.1133 -351 2009.5 6 0.1292 0.1171 0.1412 -352 2007.5 2 0.1571 0.1134 0.2008 -352 2008.5 2 0.0209 -0.022 0.0638 -352 2009.5 2 -0.0267 -0.0694 0.0159 -352 2010.5 2 -0.036 -0.0779 0.006 -352 2011.5 2 0.101 0.0512 0.1508 -352 2008.5 4 0.0475 0.033 0.0619 -352 2009.5 4 -0.0152 -0.0302 -2e-04 -352 2010.5 4 0.0079 -0.0088 0.0246 -352 2009.5 6 0.0265 0.0174 0.0357 -353 2007.5 2 0.0988 -0.2315 0.429 -353 2008.5 2 0.239 -0.0866 0.5646 -353 2009.5 2 0.245 -0.0708 0.5609 -353 2010.5 2 0.0189 -0.2618 0.2996 -353 2011.5 2 -0.2331 -0.6273 0.1612 -353 2008.5 4 0.1986 0.0341 0.3632 -353 2009.5 4 0.1753 0.0218 0.3288 -353 2010.5 4 0.0112 -0.1453 0.1677 -353 2009.5 6 0.1028 -0.0016 0.2071 -354 2009.5 6 -0.0268 -0.0697 0.0161 -354 2010.5 6 -0.0215 -0.0621 0.0191 -354 2011.5 6 -0.0172 -0.0582 0.0237 -354 2012.5 6 -0.0168 -0.0595 0.0258 -354 2013.5 6 -0.0331 -0.077 0.0108 -354 2014.5 6 -0.0459 -0.0913 -5e-04 -354 2015.5 6 -0.036 -0.08 0.0081 -354 2016.5 6 -0.0126 -0.0555 0.0302 -354 2017.5 6 0.0261 -0.0194 0.0716 -354 2018.5 6 0.0361 -0.0088 0.081 -354 2019.5 6 0.034 -0.0108 0.0787 -354 2020.5 6 0.0104 -0.0351 0.0558 -354 2012.5 12 -0.0282 -0.0464 -0.01 -354 2013.5 12 -0.0231 -0.0403 -0.0058 -354 2014.5 12 -0.0147 -0.0318 0.0024 -354 2015.5 12 -0.0081 -0.0252 0.009 -354 2016.5 12 -0.0032 -0.0207 0.0143 -354 2017.5 12 -0.0031 -0.0212 0.015 -354 2015 17 -0.0117 -0.0228 -7e-04 -355 2007.5 2 -0.0629 -0.115 -0.0109 -355 2008.5 2 -0.1429 -0.2021 -0.0838 -355 2009.5 2 0.0415 -0.0169 0.0999 -355 2010.5 2 -0.0442 -0.0992 0.0107 -355 2011.5 2 -0.0176 -0.0839 0.0487 -355 2008.5 4 -0.0636 -0.0818 -0.0454 -355 2009.5 4 -0.0395 -0.0588 -0.0203 -355 2010.5 4 -0.0105 -0.0339 0.0128 -355 2009.5 6 -0.0436 -0.0559 -0.0313 -356 2009.5 6 -0.0394 -0.1317 0.0529 -356 2010.5 6 -0.0223 -0.1103 0.0657 -356 2011.5 6 -0.0072 -0.0925 0.0781 -356 2012.5 6 0.0028 -0.0806 0.0861 -356 2013.5 6 0.0034 -0.0792 0.086 -356 2014.5 6 -0.0044 -0.0865 0.0778 -356 2015.5 6 -0.0174 -0.0988 0.064 -356 2016.5 6 -0.0273 -0.1081 0.0535 -356 2017.5 6 -0.0372 -0.1197 0.0453 -356 2018.5 6 -0.0483 -0.1338 0.0372 -356 2019.5 6 -0.0481 -0.1359 0.0396 -356 2020.5 6 -0.0435 -0.1372 0.0502 -356 2012.5 12 -0.0155 -0.0645 0.0335 -356 2013.5 12 -0.0143 -0.0612 0.0326 -356 2014.5 12 -0.0155 -0.0613 0.0303 -356 2015.5 12 -0.0197 -0.0652 0.0258 -356 2016.5 12 -0.0248 -0.071 0.0215 -356 2017.5 12 -0.0297 -0.0786 0.0192 -356 2015 17 -0.0239 -0.0609 0.013 -357 2007.5 2 -0.2576 -0.4813 -0.0339 -357 2008.5 2 -0.4004 -0.6327 -0.1682 -357 2009.5 2 -0.0935 -0.3297 0.1427 -357 2010.5 2 -0.0643 -0.3063 0.1777 -357 2011.5 2 -0.0327 -0.308 0.2426 -357 2008.5 4 -0.2655 -0.3593 -0.1716 -357 2009.5 4 -0.1768 -0.2616 -0.092 -357 2010.5 4 -0.0636 -0.1651 0.038 -357 2009.5 6 -0.1717 -0.2311 -0.1124 -358 2009.5 6 0.0243 -0.0363 0.0849 -358 2010.5 6 0.0515 -0.0095 0.1124 -358 2011.5 6 0.0416 -0.0165 0.0997 -358 2012.5 6 0.0073 -0.0487 0.0632 -358 2013.5 6 -0.0334 -0.0897 0.0228 -358 2014.5 6 -0.0364 -0.0936 0.0208 -358 2015.5 6 -0.0022 -0.056 0.0516 -358 2016.5 6 0.0284 -0.0266 0.0833 -358 2017.5 6 0.047 -0.0116 0.1056 -358 2018.5 6 0.0356 -0.0189 0.0901 -358 2019.5 6 0.0089 -0.0459 0.0638 -358 2020.5 6 -0.0236 -0.0846 0.0374 -358 2012.5 12 0.0066 -0.018 0.0312 -358 2013.5 12 0.0142 -0.0091 0.0376 -358 2014.5 12 0.0161 -0.0074 0.0397 -358 2015.5 12 0.0098 -0.0126 0.0322 -358 2016.5 12 0.001 -0.0217 0.0236 -358 2017.5 12 0.0029 -0.0213 0.027 -358 2015 17 0.0079 -0.0082 0.024 -359 2009.5 6 0.0751 0.0335 0.1167 -359 2010.5 6 0.0021 -0.0413 0.0456 -359 2011.5 6 -0.0405 -0.088 0.007 -359 2012.5 6 -0.0166 -0.0598 0.0267 -359 2013.5 6 0.0138 -0.0262 0.0538 -359 2014.5 6 0.0064 -0.0384 0.0511 -359 2015.5 6 -0.0349 -0.0838 0.014 -359 2016.5 6 -0.0848 -0.1331 -0.0364 -359 2017.5 6 -0.1792 -0.2333 -0.1251 -359 2018.5 6 -0.1486 -0.1972 -0.1001 -359 2019.5 6 -0.0687 -0.1221 -0.0153 -359 2020.5 6 -0.0272 -0.0862 0.0317 -359 2012.5 12 -0.0018 -0.0177 0.0141 -359 2013.5 12 -0.0284 -0.0447 -0.0122 -359 2014.5 12 -0.0505 -0.0687 -0.0323 -359 2015.5 12 -0.0622 -0.0802 -0.0442 -359 2016.5 12 -0.0693 -0.0889 -0.0498 -359 2017.5 12 -0.0647 -0.086 -0.0434 -359 2015 17 -0.0357 -0.0477 -0.0238 -360 2007.5 2 -0.034 -0.1293 0.0613 -360 2008.5 2 -0.2037 -0.3077 -0.0998 -360 2009.5 2 0.2915 0.191 0.392 -360 2010.5 2 0.0567 -0.0357 0.149 -360 2011.5 2 -0.0384 -0.1484 0.0716 -360 2008.5 4 -0.0042 -0.0358 0.0273 -360 2009.5 4 0.0725 0.04 0.105 -360 2010.5 4 0.0986 0.0604 0.1368 -360 2009.5 6 0.031 0.0108 0.0512 -361 2009.5 6 -0.0307 -0.0523 -0.0091 -361 2010.5 6 -0.0293 -0.0513 -0.0073 -361 2011.5 6 -0.0086 -0.0298 0.0127 -361 2012.5 6 0.0248 0.0026 0.0471 -361 2013.5 6 0.0248 0.0031 0.0464 -361 2014.5 6 0.0154 -0.0064 0.0372 -361 2015.5 6 -0.0026 -0.024 0.0189 -361 2016.5 6 -0.0247 -0.0464 -0.003 -361 2017.5 6 -0.0233 -0.0445 -0.0021 -361 2018.5 6 -0.0125 -0.0331 0.0081 -361 2019.5 6 -0.0016 -0.023 0.0198 -361 2020.5 6 0.008 -0.0151 0.0311 -361 2012.5 12 -0.0067 -0.0146 0.0012 -361 2013.5 12 -0.0043 -0.0122 0.0037 -361 2014.5 12 1e-04 -0.0083 0.0085 -361 2015.5 12 -0.0015 -0.0099 0.0069 -361 2016.5 12 -0.0018 -0.0104 0.0069 -361 2017.5 12 -0.002 -0.011 0.007 -361 2015 17 -0.0049 -0.0102 4e-04 -362 2009.5 6 0.101 -0.0262 0.2283 -362 2010.5 6 0.0475 -0.0735 0.1685 -362 2011.5 6 -0.0456 -0.1646 0.0734 -362 2012.5 6 -0.1194 -0.2524 0.0137 -362 2013.5 6 -0.1812 -0.3317 -0.0307 -362 2014.5 6 -0.1575 -0.2999 -0.0152 -362 2015.5 6 -0.0969 -0.2259 0.0321 -362 2016.5 6 0.0209 -0.11 0.1517 -362 2017.5 6 0.0918 -0.06 0.2436 -362 2018.5 6 0.0857 -0.0552 0.2265 -362 2019.5 6 0.0605 -0.0754 0.1964 -362 2020.5 6 0.0384 -0.105 0.1819 -362 2012.5 12 -0.0443 -0.1044 0.0157 -362 2013.5 12 -0.0463 -0.1032 0.0106 -362 2014.5 12 -0.0536 -0.1107 0.0035 -362 2015.5 12 -0.0433 -0.0989 0.0123 -362 2016.5 12 -0.0308 -0.0872 0.0256 -362 2017.5 12 -0.005 -0.0677 0.0577 -362 2015 17 -0.0138 -0.056 0.0284 -363 2007.5 2 0.4182 0.129 0.7073 -363 2008.5 2 0.3201 0.0599 0.5802 -363 2009.5 2 0.1081 -0.1427 0.3589 -363 2010.5 2 0.1342 -0.1267 0.3951 -363 2011.5 2 0.0919 -0.2342 0.418 -363 2008.5 4 0.2859 0.1626 0.4091 -363 2009.5 4 0.1795 0.0783 0.2807 -363 2010.5 4 0.1137 -0.0184 0.2458 -363 2009.5 6 0.2045 0.1181 0.2908 -364 2009.5 6 0.035 -0.0739 0.1439 -364 2010.5 6 0.0037 -0.0976 0.105 -364 2011.5 6 -0.0265 -0.1279 0.0748 -364 2012.5 6 -0.0427 -0.1418 0.0563 -364 2013.5 6 -0.0409 -0.1351 0.0534 -364 2014.5 6 -0.0357 -0.132 0.0606 -364 2015.5 6 -0.0384 -0.1389 0.0621 -364 2016.5 6 -0.06 -0.1632 0.0433 -364 2017.5 6 -0.0918 -0.2039 0.0203 -364 2018.5 6 -0.1198 -0.2468 0.0071 -364 2019.5 6 -0.1249 -0.2602 0.0104 -364 2020.5 6 -0.1079 -0.2444 0.0286 -364 2012.5 12 -0.0166 -0.0696 0.0365 -364 2013.5 12 -0.0342 -0.0854 0.0169 -364 2014.5 12 -0.05 -0.1038 0.0038 -364 2015.5 12 -0.0644 -0.1237 -0.0051 -364 2016.5 12 -0.0746 -0.1403 -0.009 -364 2017.5 12 -0.0796 -0.1516 -0.0076 -364 2015 17 -0.0505 -0.0969 -0.0041 -365 2009.5 6 -0.014 -0.0381 0.0101 -365 2010.5 6 -0.0204 -0.0442 0.0035 -365 2011.5 6 -0.0154 -0.0392 0.0083 -365 2012.5 6 -0.017 -0.0407 0.0066 -365 2013.5 6 -0.0153 -0.0392 0.0086 -365 2014.5 6 -0.021 -0.0452 0.0033 -365 2015.5 6 -0.0197 -0.0444 0.0051 -365 2016.5 6 -0.0255 -0.0503 -7e-04 -365 2017.5 6 -0.0268 -0.0524 -0.0012 -365 2018.5 6 -0.0224 -0.0485 0.0037 -365 2019.5 6 -0.0257 -0.0535 0.002 -365 2020.5 6 -0.0245 -0.0546 0.0057 -365 2012.5 12 -0.0178 -0.0302 -0.0054 -365 2013.5 12 -0.0197 -0.0319 -0.0074 -365 2014.5 12 -0.0198 -0.0322 -0.0073 -365 2015.5 12 -0.0212 -0.0339 -0.0084 -365 2016.5 12 -0.0225 -0.0359 -0.0091 -365 2017.5 12 -0.0229 -0.0373 -0.0085 -365 2015 17 -0.0204 -0.0303 -0.0105 -366 2009.5 6 0.0268 -0.0013 0.0549 -366 2010.5 6 0.0053 -0.0221 0.0328 -366 2011.5 6 -0.0032 -0.0306 0.0242 -366 2012.5 6 4e-04 -0.0265 0.0272 -366 2013.5 6 0.0058 -0.0213 0.033 -366 2014.5 6 0.0114 -0.0162 0.0389 -366 2015.5 6 0.0014 -0.0255 0.0283 -366 2016.5 6 6e-04 -0.0258 0.027 -366 2017.5 6 0.0252 -8e-04 0.0512 -366 2018.5 6 0.0469 0.0225 0.0714 -366 2019.5 6 0.0463 0.0203 0.0723 -366 2020.5 6 -0.0097 -0.0381 0.0188 -366 2012.5 12 0.009 -0.001 0.019 -366 2013.5 12 0.0055 -0.0043 0.0154 -366 2014.5 12 0.0081 -0.0019 0.0181 -366 2015.5 12 0.0156 0.0057 0.0254 -366 2016.5 12 0.0195 0.0093 0.0297 -366 2017.5 12 0.0093 -0.0014 0.02 -366 2015 17 0.0102 0.0039 0.0164 -367 2009.5 6 0.0628 0.0488 0.0769 -367 2010.5 6 0.0319 0.0175 0.0463 -367 2011.5 6 0.0446 0.0301 0.059 -367 2012.5 6 0.0391 0.0249 0.0532 -367 2013.5 6 0.0192 0.0055 0.0329 -367 2014.5 6 0.0043 -0.0099 0.0184 -367 2015.5 6 -0.0069 -0.0216 0.0077 -367 2016.5 6 -0.0476 -0.0618 -0.0334 -367 2017.5 6 -0.053 -0.0678 -0.0383 -367 2018.5 6 -0.0253 -0.0388 -0.0118 -367 2019.5 6 0.0128 -0.0013 0.027 -367 2020.5 6 0.0432 0.0285 0.0579 -367 2012.5 12 0.0185 0.0136 0.0235 -367 2013.5 12 0.0105 0.0055 0.0154 -367 2014.5 12 0.0033 -0.0019 0.0085 -367 2015.5 12 -0.0048 -0.0099 3e-04 -367 2016.5 12 -0.0079 -0.0131 -0.0028 -367 2017.5 12 -0.0067 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2016.5 6 -0.0177 -0.109 0.0736 -369 2017.5 6 -0.0336 -0.1283 0.0611 -369 2018.5 6 -0.0469 -0.144 0.0502 -369 2019.5 6 -0.0418 -0.1364 0.0528 -369 2020.5 6 -0.0235 -0.1254 0.0785 -369 2012.5 12 -0.0823 -0.1196 -0.0451 -369 2013.5 12 -0.0832 -0.1224 -0.044 -369 2014.5 12 -0.0742 -0.1156 -0.0329 -369 2015.5 12 -0.0651 -0.1089 -0.0213 -369 2016.5 12 -0.0529 -0.0985 -0.0074 -369 2017.5 12 -0.0391 -0.0878 0.0096 -369 2015 17 -0.0636 -0.0951 -0.0321 -370 2007.5 2 -0.2477 -0.4034 -0.092 -370 2008.5 2 -0.0803 -0.2396 0.079 -370 2009.5 2 -0.1863 -0.3517 -0.0209 -370 2010.5 2 -0.0743 -0.2415 0.0929 -370 2011.5 2 -0.0169 -0.2097 0.1759 -370 2008.5 4 -0.1623 -0.2338 -0.0909 -370 2009.5 4 -0.1209 -0.1923 -0.0495 -370 2010.5 4 -0.0907 -0.1711 -0.0103 -370 2009.5 6 -0.1211 -0.1719 -0.0702 -371 2007.5 2 -0.0207 -0.1036 0.0623 -371 2008.5 2 -0.1789 -0.2713 -0.0865 -371 2009.5 2 0.0949 0.0054 0.1843 -371 2010.5 2 -0.0323 -0.115 0.0503 -371 2011.5 2 0.0044 -0.0913 0.1 -371 2008.5 4 -0.0493 -0.0778 -0.0208 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0.0548 0.0328 0.0767 -373 2009.5 4 0.0028 -0.0194 0.025 -373 2010.5 4 0.0155 -0.0099 0.0408 -373 2009.5 6 0.0297 0.0158 0.0436 -374 2009.5 6 -0.0401 -0.1421 0.0619 -374 2010.5 6 0.0027 -0.0908 0.0963 -374 2011.5 6 0.0258 -0.0728 0.1243 -374 2012.5 6 -0.0103 -0.1078 0.0873 -374 2013.5 6 -0.026 -0.1201 0.0682 -374 2014.5 6 -0.0167 -0.1047 0.0712 -374 2015.5 6 0.0467 -0.0411 0.1346 -374 2016.5 6 0.0969 0.0068 0.187 -374 2017.5 6 0.1235 0.0308 0.2161 -374 2018.5 6 0.0925 0.0062 0.1789 -374 2019.5 6 0.0437 -0.0434 0.1308 -374 2020.5 6 0.0294 -0.0606 0.1193 -374 2012.5 12 -0.0034 -0.0433 0.0364 -374 2013.5 12 0.0209 -0.0163 0.0582 -374 2014.5 12 0.0378 6e-04 0.075 -374 2015.5 12 0.0462 0.0101 0.0824 -374 2016.5 12 0.0452 0.0091 0.0812 -374 2017.5 12 0.0464 0.0098 0.083 -374 2015 17 0.0251 8e-04 0.0493 -375 2009.5 6 0.1145 -0.0499 0.279 -375 2010.5 6 0.2668 0.1039 0.4298 -375 2011.5 6 0.2832 0.1208 0.4455 -375 2012.5 6 0.2374 0.0736 0.4012 -375 2013.5 6 0.1659 0.0222 0.3096 -375 2014.5 6 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08674a0e0f85623d4da72d469faa72d36287032f c0321cebfb67b63a1fda1e0c3f0664a666fef3d0 converged 316 -29 09e6375bd0ace29b879b739c21ff7640cc2d9048 87aad7cb691c2080682ad7e4f3110a5bb64983cb converged 319 -29 0a56eb8b37e7679cba2221f874905bb997678b38 fc0c9be4fccfc590996f339329ea21c294917d8a converged 320 -29 15f0fb655471f3272e07fd2e4c078ce671d84ef1 840c7d900bad653ed5acbe4441934a6b8a6ee579 converged 341 -29 18e1729f3b166edb687dc93cb09a978e641f70b2 94b7c729d7f2396c4dd9061056722f79f1337089 converged 347 -29 31c42076cad601a24eeeb60ba52e3947334cd0f2 66552cf682483e0dd8e2dd5c282cec91905e35f7 converged 375 -29 34a10292a7aeede12aaf0ada14f6e0dfb6c87b61 c7bc7a01a30f0c41dd3488ccf331856528002fdb converged 378 -29 43ec6cf9f9e284417842d43e46ad3c96f71a51f4 eacb87b0b465d61f0a28a078f8034f0deed7708a converged 396 -29 4ac1db11145bc5622a889fd7155a3b3a943ab7ad a3d5806aef70cf565b6804832eecf3f070b2a3cf converged 407 -29 5851d88b1ec0b1412a280c2605e919ab85eae26d f48260f71b2fc2ebdc86fc6df67bc5f58583b749 converged 420 -29 5be7d38257523a516109ee17da70be6b849bb4d2 02c38c0280bf7b429b226fc0e488fe606a2f26b4 converged 426 -29 5c8f6d1fb307a42de2f1f5475ef68aa95c61fd5f 9fe4d7eed066827e75926a46531a4dc63ea84201 converged 427 -29 612e355d6e0b948e7552858e256a3483fd343e6f 4024e2747ae829640390fe2586beafc1cd29d715 converged 431 -29 630a0debf01296e254447b1781d530268935bef4 adf6f5179f16bd2bdda444870dd6738f6b84a3c6 converged 433 -29 770e20de1e7c78ad852e0c0b4bb9860505be1d9a a1ac62eb9b68f494992f03b7c8a8809118bee9d7 converged 456 -29 8c299a02d9e8d20d2c28a5bc1acb67becf678d2c d5d93404e06a3af99679610ed172cc961ea775b7 converged 473 -29 8c5946c97abdaadd483711e34b11d509b1265fad 5a4a48cc331e510dd8356e338539ac5caa974ad4 converged 474 -29 9bffc6c309b836298fa0db1bc7eb4504c5302290 65935fdf2ab14f7daae2e248a295d8da703bb4ce converged 499 -29 9fbbc7c4a4b58c5f46b522e676aa6d25c1351f26 caeaca52899d1c25d1feed523d1491165180b5ee converged 505 -29 a6383108066814d4f3641a96f4dca690c595a3d3 4946c87290876d79f96844df0d3f25843e505a52 converged 512 -29 b1d8dde6f5b24daaa1612db2678889d5b763e3d9 aa27acd44048481f2facfeb94c982f96ceaa2edd converged 522 -29 c95739f0f351d9979093a0d9686985b4898b4a44 16ac10f09dd230de1b31fe48e038d4c17be9cd6b converged 547 -29 ce4f79d894b07094ccfe03af7f1a12af67f0118c 4a188975a065b4fc56ce5e8ca0a324708a8b1db8 converged 558 -29 d486919734c6126af9e5384e8b97195a1be735bd 63515c03d06f6af2f0d5936b97b758bee90747f0 converged 566 -29 d6bb53c9858fddf173898cdcfa1cbf42f5d52c5c 26be8c03f00cff0952b0f8da9bb422344ea542d8 converged 569 -29 d878f7c6794d0a6d9a8d04be68ebb93eafdf01cc 3dee05bcf4782a1d4eacdbdfaabdd7cd0700f025 converged 572 -29 eff1b12635c7a318872a0f6b90f607945e244862 22ee9aa2467d132002db875e8eabcf634459a6a0 converged 591 -29 f041ef7e5426039e222d3d583c87473c23145c28 4a61a36f890ba4c3efb53abc2d61c2a9e1feca21 converged 592 -29 f28af1197504d2ee7f1e057da67aad80fe1df0b0 faf489eee1f3cc3ebaefc6cef4b2509387a7d955 converged 596 -29 fa605cbbc698ba0f9acc3c71010dcb352ebdd981 90251bbbb9f49f8a96186c112c483386144356cb converged 604 -30 079b1ab9c77f0cd073fe6f85aaf32219c6b91920 97905b734f8d433d3bc8e818d0ef453121fa96c6 converged 314 -30 0b5acdabcbd51350a3bf41d5ce9a3b851ea2256e 15d327aae1cc84f15c98535acbdff9b74645217e converged 321 -30 0b9335c3cf2a43c4482e33fab75f23a3b0ec60bb b6a7125d7304e9dcd2d6dc29a2fc8d486552bb48 converged 322 -30 0f7427572b0f9331bd80f1fc480dd43dc96f2a71 329f94d458cb5ed8e36a5b1c2c6bd2c4d0159915 converged 329 -30 1660ac09ed9c437fc6a1450716fb24d95c44e5ce d4a14abeb1b48ffe99e29ab7113bad68bccb1fa6 converged 343 -30 25f24296b448cbe86114a1e880fb532cdce17e20 eead2e5f5d84b3e949c67be0cd2aa6bf34419bed converged 360 -30 2668af3e681b88104e214dd4a7d4aafdebafe127 131c7f37fd3d447d1518d3501bb14836943f7d68 converged 361 -30 278626eac43f672c7f61cb8f5b0f7240bf907776 1e3390e245a9874f3c23449e0eae7b49ec7e67b0 converged 364 -30 288d395431cf2eb781933d91a1217136466377b7 546b5fc41b4bef92b7e876cb3ac00d805d5157cf converged 366 -30 2ed39a26e20fe6a264a8681eea608d92ab254f24 f6b60fd72ac0aafd04127a3736688bea6a512757 converged 372 -30 377dc91bc1bea41c6d508e25de24383c0316770d fbff33725b919bf84872363ff8543dbba0520147 converged 380 -30 395abbf54cbaf4dc8d2cafb1cf8a02a22f3cc92d d7658d62fd79f9d3ac683fbbd1c04391f46d187a converged 384 -30 3d5cecbfb07bcba179396e4cde4c105f267d4c34 f1d9a30fb7eedc4157d251af0b7f998d7a661cca converged 388 -30 41a5b060fafd8265ebaa8b9590f7306c924ff8e3 43cb27bb42b83617d5cf9101f4e6b19a017c85c5 converged 392 -30 430dd638aaec2fd2706f245ec0e5a60be8502048 eae64a737081c20900d69526f924fce8156d7224 converged 395 -30 4462512c3bc36872ae675261a1c0aaaddf3bf1e1 77db66f99a1d5a69fae8c42f43b5ef1cd2a7528c converged 397 -30 47d9e81a07bc42a0b046bd22369f1e207c184edd 7b0f01b58a2b60cd8543e75db67fd52376902d98 converged 400 -30 4bf033e3d19c834f9fc08193cdea01e1aec3b8a2 4652b4064d96710c682407609ad3a616225229c1 converged 409 -30 51609d9282f7a9e4195bcb0a3bb575b6daffcb6c f1211117fc34bd04905eaad2e2aa7b2336f3f2eb converged 414 -30 531cee23bb2a9187017195819734cce068d1f1f2 bf39ce38a736c082589a152f6924b25cf4c35e84 converged 416 -30 53b074a0fb4bc855c35426f82a047aaea3451fbb 076a8b045774309fb6c43935bd59f41e44e99fdd converged 417 -30 54fc3567dfd4ca40b67077dc1ebb539b01265180 58e0ca10403d3e58bb484a7b69ff00e9a91a9229 converged 418 -30 5603e1cd3c513d3cb058af4d5e3759590163da2d 8150d4f5e9059eb93d24e70ed880d81b111ab17e converged 419 -30 62df99497d514aaa0a020e319fb9f4a73af6053f 620f2415e3002114ab7b3cd7ada1d467314f3cbf converged 432 -30 6a1f6523748e32bcdac79eae7b5b26e12d344d02 4726685eb0556e087b16064d24dbc25d74bc7308 converged 442 -30 6b8afec69da36905e42d7cdaa982b12790dc01cd 193d2a1f6de2a3544ea19c7c790da2188575be03 converged 444 -30 748b26c95512fff36ce7849f35aa3ff840de49d9 8f591feede4d51f375f8412e4b665f74d6136fca converged 451 -30 7586012c89aa7aa29e6f719db06b1ed92b748003 3363ca677f9324c712a20f6f7ce2e6f4aad78567 converged 452 -30 761ad3e12d27ea12363980d677b398a917ff4a38 033e6ea8346a58e500c4c7d37ef075068c6f98ae converged 454 -30 77bc3594f18275b6882c60a981f6a5c935eee2f5 d13b5414234ec8335d31c7611bb98f7c8cffc2ff converged 457 -30 7d2c893dd838a7fa3f6d4b8afe098fb0ce38caf8 8b71d26926c9b7bff28c3090e3c340d7fb3704b9 converged 460 -30 7f486a2ca57410de948da8623b3ac7f51c9d2065 0d31335ca2fbff357a588e184c0ffd3f8992054c converged 465 -30 8e5e92329d682285d15f0851777d8a1c0180a803 10c3f804272fc9392dc8f49251e2ec704ceb86a0 converged 475 -30 8efc52286d7323942d723011bdf922e2312b218c 526e9c5a0ba26f8f0e9ad5ef9e6c0ef0733d4581 converged 478 -30 991fdf3e59e81f9c9d634e7dabcdabb78a7d7f6d 2a7652b5cb8f1eb29e568bf279f21221618695e7 converged 492 -30 994efbfd7077092941ab698950b510bcccdc578e 4e750bf595c12f1423f4b58489c049b8ea55f2af converged 493 -30 9a5c780f3d4f5634cbd4d7ae30d124a897eef006 f102163686b5242057ce8a59514df16a4fae43cf converged 496 -30 9f218a969f62a03ef252334b4dba7a421118c2d1 ee683acefc567686fb5ddc4257ce226517da92e2 converged 502 -30 a33b1cf5ea0866025ff96436aa001117f5426416 0081c6fe629f542bdf1551cb7f7dde2782544db1 converged 510 -30 b2610f7388d950faa49cbcd9b55bc155d4e6a7e7 cdba4f3c4dfcd964491373706d32bc62e84a6c1e converged 523 -30 b3e277071737f4fea97bc7a7e2efdf71a313134d e63287e1d2108533ee25068b6d00f786c46580b5 converged 524 -30 b4d2995f2f1b984c516be0a3e87389dfd4150ec1 d328123476cb2302908a2cad87f5879402460896 converged 526 -30 b7cb883975cd7ed029df1c79627116598fa634d4 ce3f8f0cb21e1f098dfee366bf3335ac550b6089 converged 531 -30 b9278fe2beee35f78fd88bccd5b6fdb8cf9a49fd 22802987794e647047c72a678d66ea18eef58c7a converged 534 -30 be36cd4a5a0e442c3c3340847af50b928eef68b9 3b16e69b91bcd2c3ab6235ae10a1cb2ad0957bfb converged 539 -30 c10d1db00e7b5129507e7db910069367072bcab9 72717581a8500561efa897d9aab0107c4f23697c converged 541 -30 c7cc0b43d4185bd3ffde5a89e431b5946b4ca85a 8772a23704729fe8d41a2ad70d7337e1e7129d04 converged 546 -30 ca5ee2bb458148b172115c42c57618e31af78a85 248d0af412ebee863f5d1e02681d1cacd02ced69 converged 549 -30 cb456245e9b933ca4250745946ebfcc2d653bc22 6f728c23b2ca3207c0f4a0f5aabc331b3243dd1c converged 550 -30 cb8dbd055d69b6eb6a1e9784fd05dd8e14a37b9d 9c5a548c3a204b766201887d72c35ece52833d34 converged 551 -30 d7dda403d507084ed05cc1b9bf6c65829da6a22c afc0baea449e33ba08a33610c68093b43e07f4d7 converged 571 -30 dab16115cee8ffa0bce8f85a2ccd1b053fad2d50 0eaeb68324c260cbb85656c9ecae2edf7d30c1dc converged 575 -30 de14295bbf982e71cb016c9837ad969232b6addc b9e9eaeb9a9b1957dc58068807ddfb7ac5c08407 converged 580 -30 e04cd576ce0e25075d86ad8d701a970966d70126 52e263ac53a8eea69c95496c56010c6f63ed0dd3 converged 581 -30 e0b6e36d3ff25cf77fbc653610885243e3d3434f abe6683bb7fc5334c5547c2a7a98a9a700c73e1b converged 582 -30 e8da11d87c97fb1d0a2a13bfd37fe8f032b7b35b 888f9aab98d1fea5f37798d5e534a43dc3f25dcd converged 585 -30 e8e8ab63aa33aac013912a7bba79e70bf0104670 55a3d6d36683780f84cb7b55dacc397f7af8c336 converged 586 -30 eaac7db71e9c295cf4770b3bac90f14898d2f3fe 81527b30bcce2963d313c8731a1862713ecffdc9 converged 587 -30 f88e30f21dfcdaee543a181bf4c039a7598dd249 3c3d6f49665fddd11df5bc9e1fd0365f069e43df converged 602 -30 fb97d1da0003d9c3f3ec298634a0797784bc49f6 77e7df698be9ce5cea4e9800dd63b29592e7f1ff converged 606 -31 288d395431cf2eb781933d91a1217136466377b7 546b5fc41b4bef92b7e876cb3ac00d805d5157cf converged 366 -31 395abbf54cbaf4dc8d2cafb1cf8a02a22f3cc92d d7658d62fd79f9d3ac683fbbd1c04391f46d187a converged 384 -31 4462512c3bc36872ae675261a1c0aaaddf3bf1e1 77db66f99a1d5a69fae8c42f43b5ef1cd2a7528c converged 397 -31 4bf033e3d19c834f9fc08193cdea01e1aec3b8a2 4652b4064d96710c682407609ad3a616225229c1 converged 409 -31 51609d9282f7a9e4195bcb0a3bb575b6daffcb6c f1211117fc34bd04905eaad2e2aa7b2336f3f2eb converged 414 -31 531cee23bb2a9187017195819734cce068d1f1f2 bf39ce38a736c082589a152f6924b25cf4c35e84 converged 416 -31 54fc3567dfd4ca40b67077dc1ebb539b01265180 58e0ca10403d3e58bb484a7b69ff00e9a91a9229 converged 418 -31 6a1f6523748e32bcdac79eae7b5b26e12d344d02 4726685eb0556e087b16064d24dbc25d74bc7308 converged 442 -31 7d2c893dd838a7fa3f6d4b8afe098fb0ce38caf8 8b71d26926c9b7bff28c3090e3c340d7fb3704b9 converged 460 -31 9f218a969f62a03ef252334b4dba7a421118c2d1 ee683acefc567686fb5ddc4257ce226517da92e2 converged 502 -31 b3e277071737f4fea97bc7a7e2efdf71a313134d e63287e1d2108533ee25068b6d00f786c46580b5 converged 524 -31 b9278fe2beee35f78fd88bccd5b6fdb8cf9a49fd 22802987794e647047c72a678d66ea18eef58c7a converged 534 -31 cb456245e9b933ca4250745946ebfcc2d653bc22 6f728c23b2ca3207c0f4a0f5aabc331b3243dd1c converged 550 -31 dab16115cee8ffa0bce8f85a2ccd1b053fad2d50 0eaeb68324c260cbb85656c9ecae2edf7d30c1dc converged 575 -31 e8da11d87c97fb1d0a2a13bfd37fe8f032b7b35b 888f9aab98d1fea5f37798d5e534a43dc3f25dcd converged 585 -32 057e825aff074e168523706c03897e0777df9b94 5a4dd5ca0b87789189baf6b3323a71c23fa08044 converged 309 -32 0e512f5267893868c482de2eaad7e086720f9da8 d03303db3c6219c54451905681e93ee0ecee501a converged 328 -32 101ae292210d6919e7202b9789585f3003181bb6 976dfd8fe9c8178462e0556b70e34e6aafa4f4e4 converged 330 -32 14913922c0fbab173e657b2a819e2c1dec9f01f0 529b2b51674c7726ece779c27f68297d477c55be converged 337 -32 1bd9dc3c69c6706234cd1004c04c73938474f9f8 810fa7cc6a922561b070aad131149e8a6ca93b47 converged 350 -32 494f8e62354ed6fceadf7b4266909da1cb6cfc0f 384f4dcfa8e4c5e0fe8dc520b32592bb1494b46e converged 403 -32 4d8dd369d5876d899d0442cd2676b4aa77c108ed c662dba7a84e43f7436af50d9e26b42ea3195169 converged 410 -32 5baa9b7c1cd1a2259cc473814a4389647bb6f312 9c107016dcb3d4a7fbc494c3a594c4ecb00e4d92 converged 425 -32 609efe21f6eb9bae6f533e933e8d1bd32c868990 a8f43312993371bd04feac9864b9384d2bd9fc0e converged 429 -32 610e0202389348f6d8ed29b1ff13364c67ccb41e 9f4f490a037ac351a8175426ff67afb6a0948574 converged 430 -32 7c0eb388a0fbeff738c5767a89ed28c9254dd147 39d77d5e4f0999e8f8cfa5f9d44e7f822a44c549 converged 459 -32 9188e5c25e8616a28af08c97dd8d5633755a6321 2d45ea3a4464a803f76ecbd2ce77d4fcc78b6fd3 converged 482 -32 92fa4376954618fbf5ca8d4bb3000d4effbdbe96 e47d04681382ff755006878a726695cdccd4d5ff converged 486 -32 9ba0ca84ebe3f443d0a525ead480c8edf26f4b85 17f753388b239e09fc1a6ab2cee500b436a61db8 converged 498 -32 ad48594408b255e610e12ed95d678c0feb4094e4 8adf3752709af5ed2e2ef469acc78991b5daa9ca converged 518 -32 b5e4c0c917c548e86d915f7869b194e66224dad5 8cc7102ee1db508fad197a1ec32958d72ec5e18d converged 529 -32 cbe6ec589b3f32701962efcd767f2721a50b3be1 4f9b0ee671cfb1c9bb2be2732c45e82fe3696be3 converged 552 -32 d30c6d7c0f0d1582bb1260f70d0b5f9e9df249b2 5a2c2dd5d0cf690605494991e06fd13408c86434 converged 563 -32 d31c0e73a1d5612fcf092af2e809da4d3c9885b7 b0b83be573afad2be555f13a5f6d6fd0230e18d0 converged 564 -33 09867787f649174cccd24a0d89c0b3ad0ad0df0f 3086c33c78ff09b2580e8914b6fbb9432f53195e converged 318 -33 0bf2c8c4276ad1bdac0113aa57d0db48210c2ddf e0fabb18f63cb3a3efb721e6b39da895384bdf06 converged 323 -33 16015aab58128aeb0ef17c995f4ca08362e9a25a 7087b596461a5cc548e0b34c0ecefc58d3a8c2bd converged 342 -33 45972608d05083d7331c6ebedb352456b662b3e2 7f5b0ccfbbaf7d48badbf62acd684110c5ea20a7 converged 398 -33 6f9fc830e7868189584898431fa75f58214bf35d 1ca12d6cf936466ad5ffa235bf57e440c654a692 converged 448 -33 7eab71189ddcabfc28ce2ce022dd7831518cb8a5 da5c2594e588ab04cedf6d8fd7ae5880acaf378b converged 464 -33 b94348e90dd164735a371b6ba5d7f8bfb80d6038 88098c753d901058714c4023ea1d407ce1fbdacd converged 536 -33 be233a085a3b84c8745617f7052e62ba7357596f 5a667313fa7328d70580a99084264340e611cd70 converged 538 -33 f291975c3ee7b0b948d11264ce52acec6f8977a1 0e81cee30e0630db49ea484a19f2edc5864909a9 converged 597 -34 08674a0e0f85623d4da72d469faa72d36287032f c0321cebfb67b63a1fda1e0c3f0664a666fef3d0 converged 316 -34 0a56eb8b37e7679cba2221f874905bb997678b38 fc0c9be4fccfc590996f339329ea21c294917d8a converged 320 -34 2c765dbb487a0c5f372132fccf5bf477ee6ee8c0 92716c6bd02d34433b6c6f82ed1c80c6eb440434 converged 370 -34 31c42076cad601a24eeeb60ba52e3947334cd0f2 66552cf682483e0dd8e2dd5c282cec91905e35f7 converged 375 -34 34a10292a7aeede12aaf0ada14f6e0dfb6c87b61 c7bc7a01a30f0c41dd3488ccf331856528002fdb converged 378 -34 3a5c47e9d4ea1daceab214c179435ac2edf3e6b7 746945ed4f403585a5f1d971c08c9f123849bd7a converged 385 -34 43ec6cf9f9e284417842d43e46ad3c96f71a51f4 eacb87b0b465d61f0a28a078f8034f0deed7708a converged 396 -34 4ac1db11145bc5622a889fd7155a3b3a943ab7ad a3d5806aef70cf565b6804832eecf3f070b2a3cf converged 407 -34 52f2b937962ff1f0d9a7277ef70f0e85bce96995 773370abdf23484dce6df0405f0657e839178be5 converged 415 -34 5c8f6d1fb307a42de2f1f5475ef68aa95c61fd5f 9fe4d7eed066827e75926a46531a4dc63ea84201 converged 427 -34 630a0debf01296e254447b1781d530268935bef4 adf6f5179f16bd2bdda444870dd6738f6b84a3c6 converged 433 -34 770e20de1e7c78ad852e0c0b4bb9860505be1d9a a1ac62eb9b68f494992f03b7c8a8809118bee9d7 converged 456 -34 9bffc6c309b836298fa0db1bc7eb4504c5302290 65935fdf2ab14f7daae2e248a295d8da703bb4ce converged 499 -34 a6383108066814d4f3641a96f4dca690c595a3d3 4946c87290876d79f96844df0d3f25843e505a52 converged 512 -34 ce4f79d894b07094ccfe03af7f1a12af67f0118c 4a188975a065b4fc56ce5e8ca0a324708a8b1db8 converged 558 -34 d486919734c6126af9e5384e8b97195a1be735bd 63515c03d06f6af2f0d5936b97b758bee90747f0 converged 566 -34 d6bb53c9858fddf173898cdcfa1cbf42f5d52c5c 26be8c03f00cff0952b0f8da9bb422344ea542d8 converged 569 -34 d878f7c6794d0a6d9a8d04be68ebb93eafdf01cc 3dee05bcf4782a1d4eacdbdfaabdd7cd0700f025 converged 572 -34 f28af1197504d2ee7f1e057da67aad80fe1df0b0 faf489eee1f3cc3ebaefc6cef4b2509387a7d955 converged 596 -35 042bbc7494ef5cb0ca7e6465e9f6d6c0141647a7 5d2f6033c62fdc7bfee627a3bad9e496c737791d converged 306 -35 0967f5345f27b53d7a380b08472c2478d7822c15 2cc82dd042650545eabcffff2321ee4b14af464e converged 317 -35 0d7b96c65bf8434dad8ff4ae7b2dac11ba59468f d94f409c497f0f3540c66b026e49e802a30c127d converged 324 -35 18b1df16080e691b63718cd87db6000a38ccce10 4fe094f2bc0eb156116070348dbb29511cd9b018 converged 346 -35 268fa5ef37532a55a0689b5caeac7f13ec0d8b21 333db6803024d7e4fdbf7d9317d6ec44160a9ecb converged 362 -35 2eb1ed5f4bfb876fdfe5bd571db34d924c3bb272 1714e98434d839e593204a3aa1e437670061eabb converged 371 -35 58edf590b6aab47a5d7285558406629508e605d0 d904aa0e51830eaddbc745dedf6cd5133c40735c converged 423 -35 6aa1ef507f8055df7cb4ac988f9c99f4aa034242 db41c6ec577af9f874b7dd628f67cf0800b8f28b converged 443 -35 6d4cd1885333b5178e725d711c86606516c7892f d7c2515d6e5c9b76626660dd87c674d5eeada349 converged 446 -35 76d281f71882b669130a32fcf9469f555f8a655b 4433d07c8e217166f012e154d1575e129a99a868 converged 455 -35 7e9a4360787c319ef67dfc9f3866fabf1d62bc43 63143236c878c52ca544deeec523a561fffd87fe converged 463 -35 8f3e2c3c211c102f47e4a2f61d88a45e273bb4b9 8e2064e08f35a21456c860d865248f6876608e46 converged 479 -35 92dc32d1aae8dc0349641b124188432db051ebd2 c4606a03d0f78649d7e6f03e6f89f054a96ea437 converged 485 -35 9f71d5d8541539f416457185ee8f70a01a5b189d 1945672b66548532f946b21ed6302e06b217b079 converged 504 -35 b0678c8ed27558036a4a3cae163928976d19a479 c5524b6a5f71b4edc48223c56d34aa56d30f7289 converged 520 -35 b6521474f6627b1828eb31a1052cce4722547abe 9420a94af4d2359545a024d26acfc36b27545297 converged 530 -35 b7da8652aa4646ca8a183975092b76d92966c337 7da63c8acaa37e1183e91a1523c80e2d10a4d637 converged 532 -35 b88b20623c5793f9f1d0e8860ab65f4701f9a245 62a2fd85075057930c0e2f2578fc2056146308c7 converged 533 -35 c2aafeee359f61a8007d42dc71d8e125b362a8c6 0826247200a541c85132b63883d8ab17aaa9aad4 converged 543 -35 dc7082da321e75ef33eafd6e45cfc31f49092646 2287368f5a2f29ffe32efa099dfbffaaaaa1c3e8 converged 578 -35 fa3aa51785950d4dac83bb50473536372af1e01f eebc289a60081c199949feea664faa7dcdadbf04 converged 603 -36 079b1ab9c77f0cd073fe6f85aaf32219c6b91920 97905b734f8d433d3bc8e818d0ef453121fa96c6 converged 314 -36 0b5acdabcbd51350a3bf41d5ce9a3b851ea2256e 15d327aae1cc84f15c98535acbdff9b74645217e converged 321 -36 25f24296b448cbe86114a1e880fb532cdce17e20 eead2e5f5d84b3e949c67be0cd2aa6bf34419bed converged 360 -36 2668af3e681b88104e214dd4a7d4aafdebafe127 131c7f37fd3d447d1518d3501bb14836943f7d68 converged 361 -36 2ed39a26e20fe6a264a8681eea608d92ab254f24 f6b60fd72ac0aafd04127a3736688bea6a512757 converged 372 -36 377dc91bc1bea41c6d508e25de24383c0316770d fbff33725b919bf84872363ff8543dbba0520147 converged 380 -36 3d5cecbfb07bcba179396e4cde4c105f267d4c34 f1d9a30fb7eedc4157d251af0b7f998d7a661cca converged 388 -36 430dd638aaec2fd2706f245ec0e5a60be8502048 eae64a737081c20900d69526f924fce8156d7224 converged 395 -36 53b074a0fb4bc855c35426f82a047aaea3451fbb 076a8b045774309fb6c43935bd59f41e44e99fdd converged 417 -36 62df99497d514aaa0a020e319fb9f4a73af6053f 620f2415e3002114ab7b3cd7ada1d467314f3cbf converged 432 -36 7586012c89aa7aa29e6f719db06b1ed92b748003 3363ca677f9324c712a20f6f7ce2e6f4aad78567 converged 452 -36 8e5e92329d682285d15f0851777d8a1c0180a803 10c3f804272fc9392dc8f49251e2ec704ceb86a0 converged 475 -36 9a5c780f3d4f5634cbd4d7ae30d124a897eef006 f102163686b5242057ce8a59514df16a4fae43cf converged 496 -36 a33b1cf5ea0866025ff96436aa001117f5426416 0081c6fe629f542bdf1551cb7f7dde2782544db1 converged 510 -36 b4d2995f2f1b984c516be0a3e87389dfd4150ec1 d328123476cb2302908a2cad87f5879402460896 converged 526 -36 be36cd4a5a0e442c3c3340847af50b928eef68b9 3b16e69b91bcd2c3ab6235ae10a1cb2ad0957bfb converged 539 -36 ca5ee2bb458148b172115c42c57618e31af78a85 248d0af412ebee863f5d1e02681d1cacd02ced69 converged 549 -36 f88e30f21dfcdaee543a181bf4c039a7598dd249 3c3d6f49665fddd11df5bc9e1fd0365f069e43df converged 602 -36 fb97d1da0003d9c3f3ec298634a0797784bc49f6 77e7df698be9ce5cea4e9800dd63b29592e7f1ff converged 606 -37 079b1ab9c77f0cd073fe6f85aaf32219c6b91920 97905b734f8d433d3bc8e818d0ef453121fa96c6 converged 314 -37 0b5acdabcbd51350a3bf41d5ce9a3b851ea2256e 15d327aae1cc84f15c98535acbdff9b74645217e converged 321 -37 0b9335c3cf2a43c4482e33fab75f23a3b0ec60bb b6a7125d7304e9dcd2d6dc29a2fc8d486552bb48 converged 322 -37 1660ac09ed9c437fc6a1450716fb24d95c44e5ce d4a14abeb1b48ffe99e29ab7113bad68bccb1fa6 converged 343 -37 25f24296b448cbe86114a1e880fb532cdce17e20 eead2e5f5d84b3e949c67be0cd2aa6bf34419bed converged 360 -37 2668af3e681b88104e214dd4a7d4aafdebafe127 131c7f37fd3d447d1518d3501bb14836943f7d68 converged 361 -37 2ed39a26e20fe6a264a8681eea608d92ab254f24 f6b60fd72ac0aafd04127a3736688bea6a512757 converged 372 -37 377dc91bc1bea41c6d508e25de24383c0316770d fbff33725b919bf84872363ff8543dbba0520147 converged 380 -37 3d5cecbfb07bcba179396e4cde4c105f267d4c34 f1d9a30fb7eedc4157d251af0b7f998d7a661cca converged 388 -37 430dd638aaec2fd2706f245ec0e5a60be8502048 eae64a737081c20900d69526f924fce8156d7224 converged 395 -37 4462512c3bc36872ae675261a1c0aaaddf3bf1e1 77db66f99a1d5a69fae8c42f43b5ef1cd2a7528c converged 397 -37 47d9e81a07bc42a0b046bd22369f1e207c184edd 7b0f01b58a2b60cd8543e75db67fd52376902d98 converged 400 -37 53b074a0fb4bc855c35426f82a047aaea3451fbb 076a8b045774309fb6c43935bd59f41e44e99fdd converged 417 -37 62df99497d514aaa0a020e319fb9f4a73af6053f 620f2415e3002114ab7b3cd7ada1d467314f3cbf converged 432 -37 761ad3e12d27ea12363980d677b398a917ff4a38 033e6ea8346a58e500c4c7d37ef075068c6f98ae converged 454 -37 8e5e92329d682285d15f0851777d8a1c0180a803 10c3f804272fc9392dc8f49251e2ec704ceb86a0 converged 475 -37 8efc52286d7323942d723011bdf922e2312b218c 526e9c5a0ba26f8f0e9ad5ef9e6c0ef0733d4581 converged 478 -37 991fdf3e59e81f9c9d634e7dabcdabb78a7d7f6d 2a7652b5cb8f1eb29e568bf279f21221618695e7 converged 492 -37 9a5c780f3d4f5634cbd4d7ae30d124a897eef006 f102163686b5242057ce8a59514df16a4fae43cf converged 496 -37 9f218a969f62a03ef252334b4dba7a421118c2d1 ee683acefc567686fb5ddc4257ce226517da92e2 converged 502 -37 a33b1cf5ea0866025ff96436aa001117f5426416 0081c6fe629f542bdf1551cb7f7dde2782544db1 converged 510 -37 b9278fe2beee35f78fd88bccd5b6fdb8cf9a49fd 22802987794e647047c72a678d66ea18eef58c7a converged 534 -37 be36cd4a5a0e442c3c3340847af50b928eef68b9 3b16e69b91bcd2c3ab6235ae10a1cb2ad0957bfb converged 539 -37 c10d1db00e7b5129507e7db910069367072bcab9 72717581a8500561efa897d9aab0107c4f23697c converged 541 -37 ca5ee2bb458148b172115c42c57618e31af78a85 248d0af412ebee863f5d1e02681d1cacd02ced69 converged 549 -37 cb8dbd055d69b6eb6a1e9784fd05dd8e14a37b9d 9c5a548c3a204b766201887d72c35ece52833d34 converged 551 -37 de14295bbf982e71cb016c9837ad969232b6addc b9e9eaeb9a9b1957dc58068807ddfb7ac5c08407 converged 580 -37 e04cd576ce0e25075d86ad8d701a970966d70126 52e263ac53a8eea69c95496c56010c6f63ed0dd3 converged 581 -37 e0b6e36d3ff25cf77fbc653610885243e3d3434f abe6683bb7fc5334c5547c2a7a98a9a700c73e1b converged 582 -37 eaac7db71e9c295cf4770b3bac90f14898d2f3fe 81527b30bcce2963d313c8731a1862713ecffdc9 converged 587 -37 f88e30f21dfcdaee543a181bf4c039a7598dd249 3c3d6f49665fddd11df5bc9e1fd0365f069e43df converged 602 -38 1dbadd0711258dfe348cc22826288fd9c4370ec8 d9ed5a748eff1698009be7f461dbb5e5cd6df203 converged 352 -38 30583a84e72b0ab055087ce243b07faf2b89c089 727b4e5664b566e22ca4c94b02a36689f77e4337 converged 373 -38 3e7d0466cbe932d04e4f80761133c31fffcef92b 882519e095b3244eaae75158c59414703b1bce12 converged 389 -38 5db905501553fa62b3aeaebb318884b8c7fa4440 8909863fd9a0afcacb9911217684b61ccfd0f268 converged 428 -38 7df296267f7fe00bd6fa890413cfd766f3c564a4 bc3dcbb08ecc6e65ef0bf2791ab10ed5c2c670bc converged 462 -38 7fe1c3da8dc47035e61eb9ba914d94e7e7ee0dd1 12b1e386935bf8bc6c5bb045011a2d5caba8467f converged 466 -38 ccb9b60433bdd7ec56b0d9581e6318a02beac01c 95c8aede64a8207003c29128e8602552688fe63e converged 553 -38 d9324f2995ad0639613df9f51db6213fee19a45d f5fc1bfbb3223cde54df839974b04ab6e309fb74 converged 573 -38 f4937b4cc930a5d074111143e6e9ebb6fd3ac4ba d49266aa75af6e76b2b4ca4575d49cd1a6fb2913 converged 598 -39 072d449dc164e98c19805f67ee6a5519b780fd2d 10818f391a2cf0d3b851948d1017548a4dcdcedb converged 312 -39 0db939ade562d517d56b747cb60af3e32d859b09 b859bb9fe8800b8787bd1d7ee6af1bb2eaf2aeb9 converged 325 -39 158124d17fb643b7104455e0e65a799b56035f94 a5bf1b48ae92cead636765987b792d3715b74b25 converged 340 -39 1affa858be371732e5a6c8dadf5bb1384529b3a0 622445f8016e1c2ea810a1bd7bfb1aef5312999a converged 348 -39 1cff723dd6ac1d7020c74fd21a675ae48520627c fa5706667a060097addc31389139b8f4cd5ba023 converged 351 -39 21bf947183e389b5548abc06f1e738c0cbed3d24 e65f02bc0ee5b88993e0ecfeff0cb49a7a9b34c1 converged 357 -39 37d61eb6ac28e8143278bcff9ef74777b1324375 95460b25815075a3fbeb3fab2839a257a72e97ed converged 381 -39 3f960da3ec9176a5d454b984afa5a46056a51d2a 4b2c89d51b50c8afe550daacc73ce954df0464d6 converged 391 -39 58c02fbe1b4622c5cbfca110cb5f6f967188e0d9 d057ee065dcb431cbd94bcf71003e28e450a1ccf converged 422 -39 84710c8b233b12501fe802b02a84bcba77d988a4 046ef7d6d082fc3df8ae33f7050ce83eb1c0c013 converged 468 -39 926f28df75a0d353d70d4fd43bb2bae79c548cc9 faac47de85f78f9e912f5ea1cfac8551b994f4e5 converged 484 -39 a173f24b64fbb4b49472f71d1e99210ff743c99f 35ed233c5f8a38514ef1257bccfc842c13adbf9f converged 508 -39 aa42ad4c5b0e2558eb0dbe8d1a45ea8dc187f9bb 9af7c3ac221f978244fd78e1d141d27a81c9b884 converged 515 -39 ace4d6ad2f4b6cfc780885cb498c3af872fab5dc 91ae1376aacf11e1a1ac205fa633b8c83297c64f converged 517 -39 cefafcf222b91bc05c2325b9c540c1ec8462686b c5e03d4f7126e6c90d635848f24aac145d69c6c9 converged 559 -39 d6114df91f4560a386aed240f1a12a3c2afdbeff 3d04020fcd41f9f363aa2f08629f95f079e6b3ae converged 567 -39 e59c51e4820db7be851b57738511abadddad37d7 a0bc132536a6b0e6c878d2a95a1563ce45cf2902 converged 584 -39 f1a5501befe93a37ec3f8040fab9f356d9d902c5 dd1833ad4e5fd17b096cab6e3d74badd564d1e09 converged 594 -39 fe465c9d34b06277eebbfb6fdf492252fc12620d 8234f4af23a58948af434e5d533b606dd46cc27f converged 608 -40 1549427c59a4a13c26485a770e8230d66a8d8220 5d9aaf23cda71ac314ae4b6762b3805c33fbddc8 converged 338 -40 2a10b7314ca86904515bce84dd50ea69c54eab89 eccaf4eb519dcea4936e867e5e55b159674af078 converged 367 -40 3115a55165cf2efd12ab12e78e9514be8115a322 f7ceb78e820b60eb9acd5c8bb217b71472d5ab48 converged 374 -40 4dd7bf489369c0eb45f868fd47674e0adcee6a5e b1e93504c868d632894e41b13b3e57efca08ee9b converged 411 -40 4fb3e4787803e83a779cbd2543280a71c2b255a6 3f514907570b94af7c0aef6b62492bb2387689c9 converged 412 -40 70ac24ae994cd77ab713cc81090b02f4ba9cf519 d1bf6ad7d371aba36faa6e642f1a206ff411348c converged 450 -40 81eb19077f2d257eaaa054a2b781a83449bc9044 0eb03d4774d5dde608ad80f66e9c5f8f55bfd91b converged 467 -40 961e4f1df3d5bc215f267e2fe62cd0aea7049303 8db0a1fb6e2cde17365ba15935d595fba3531be1 converged 490 -40 9ac768a05a1a5183538fbc360a6621819feb75c3 7d3136320e9ac9a10c015ee4af0cecd0a1370be0 converged 497 -40 9eb0d6d53eb8c753562a2bbf0c5a90870ecfa324 567c09a72943c77e5ac19a0036a8cc8e186e3fe6 converged 501 -40 a0ddc04c0125fc887cb9cb1fbfb35f1049ff207e 36c01d3c86b8979b9145ab4829c316fa64ae3e7a converged 506 -40 a21216a4c136b5f7152347da94d54d8b3bdcf918 0fc56f8b9779e8bb02f2777145ba835c3f2d6467 converged 509 -40 a9b50f608cb2d4bcc19d23b266cb57b431f5f38f baa0d532d60a8ada3cb4556ed2a245e1a665e107 converged 514 -40 ae916af9c42834c88d02b364774af99f78c2e8a9 d0ed6c9c8c4c54fa7efeb59f334dfa9fa1359570 converged 519 -40 b55a6f5e2ba73fb406da11ae3390a3a3fcaeb13e 6938c53ab0a345024c788ea20ac03c2ddde83f49 converged 527 -40 b5a8ca74a9ef56a34ad23829ba7cbaeb8006223a 06d84a0fa740748affad82b60000a78b5f75a6fd converged 528 -40 c7759c891a2cf19e315832a2275d444ab0471877 3bf0a29570fb31d9d5858a2cda5331b3f3b09d7b converged 544 -40 d63883a391696db5c6ff98f76404cf4b3a97d70f 22ae006114a43968dd2e79738a33a6d56fcb0c5c converged 568 -40 d71dd9ef32818411e03aabcc7f952cbb8e073364 0ffbab2612ea0330ee5c4a021752e87740ac8cdb converged 570 -40 d9f521188e10884be7feb4e36e64c54ba92a2eb0 cde6a902ff7a65a904396b4a03543719ca63f620 converged 574 -40 dc504e7e6d04d45ccc6e6eb088e10e6ae370db7f 877a07d273ffa7a31d0898c763e6de31a611cf67 converged 577 -41 057e825aff074e168523706c03897e0777df9b94 5a4dd5ca0b87789189baf6b3323a71c23fa08044 converged 309 -41 0e21dea54f9050ff6ddec278c6156f6f67fa4620 09a89d9a5187b4349092cc849cf0659c323d8efe converged 327 -41 0e512f5267893868c482de2eaad7e086720f9da8 d03303db3c6219c54451905681e93ee0ecee501a converged 328 -41 1bd9dc3c69c6706234cd1004c04c73938474f9f8 810fa7cc6a922561b070aad131149e8a6ca93b47 converged 350 -41 1eba32b1bf4f9a46630e975ca3136089e0db1ae0 5f6e491ea488340bc5a1ae9950192c443235736c converged 353 -41 1ec158b6dcff1a27b37423604529175845d69dc1 f8b034f1a9db699ea3db37a5bee0ccd83682c041 converged 354 -41 494f8e62354ed6fceadf7b4266909da1cb6cfc0f 384f4dcfa8e4c5e0fe8dc520b32592bb1494b46e converged 403 -41 4d8dd369d5876d899d0442cd2676b4aa77c108ed c662dba7a84e43f7436af50d9e26b42ea3195169 converged 410 -41 5b6d6e295e2e83098930002d7d9b1597e8104f84 f7db28ec5e6d0837ddb8c51e4854507a7028ee9c converged 424 -41 5baa9b7c1cd1a2259cc473814a4389647bb6f312 9c107016dcb3d4a7fbc494c3a594c4ecb00e4d92 converged 425 -41 609efe21f6eb9bae6f533e933e8d1bd32c868990 a8f43312993371bd04feac9864b9384d2bd9fc0e converged 429 -41 610e0202389348f6d8ed29b1ff13364c67ccb41e 9f4f490a037ac351a8175426ff67afb6a0948574 converged 430 -41 633ced11405a6b40d20ab6daa7f4931c49878700 fa91c20929461bc675fee706de9f83a59313a501 converged 434 -41 6c788fc78680e305f5bb2a1610c62142f950ccf3 9e223cd1a0f23b0ab63b1d39e7bed58fccc1f21f converged 445 -41 75c8f1e53570db45131d425d2dd16c4469969077 49d4808593297a1093c46be318d38f5410810371 converged 453 -41 7b6ea3ca13c428ab14ace9e8e92987f4c652550b 1cef97ee856f2addf2779443364657b9335a2d8f converged 458 -41 7c0eb388a0fbeff738c5767a89ed28c9254dd147 39d77d5e4f0999e8f8cfa5f9d44e7f822a44c549 converged 459 -41 8ea6ebfd7a7d1c9c756c49d2e6523b01de9b83e6 f7194dbd76ffc5c1b2383f31bff31d10577fd981 converged 477 -41 9188e5c25e8616a28af08c97dd8d5633755a6321 2d45ea3a4464a803f76ecbd2ce77d4fcc78b6fd3 converged 482 -41 9210d9c448226ab6432d66d5452df74a65c0c44b 951da31474f5a9a5239e9928ea748d334eab324b converged 483 -41 92fa4376954618fbf5ca8d4bb3000d4effbdbe96 e47d04681382ff755006878a726695cdccd4d5ff converged 486 -41 94052f5b5f78700311bba2a7edec5694e7570206 7ddd99c903ecc5bce5f4a1b774835ba1820e5313 converged 487 -41 9780e4c83411b9a4d781de9bdf7a865017012e36 973189cb238ffe3af9ca882d82eb98de4c729833 converged 491 -41 9982829dfdbd9a0dd00ea036ecb90759dcb07301 d7aa3f1f578571271174caa4f820d8ee6c05168f converged 494 -41 9ba0ca84ebe3f443d0a525ead480c8edf26f4b85 17f753388b239e09fc1a6ab2cee500b436a61db8 converged 498 -41 ad48594408b255e610e12ed95d678c0feb4094e4 8adf3752709af5ed2e2ef469acc78991b5daa9ca converged 518 -41 b5e4c0c917c548e86d915f7869b194e66224dad5 8cc7102ee1db508fad197a1ec32958d72ec5e18d converged 529 -41 cbe6ec589b3f32701962efcd767f2721a50b3be1 4f9b0ee671cfb1c9bb2be2732c45e82fe3696be3 converged 552 -41 ccbd9202891dbe48ab8cafa97baf56cd5bb5e979 5b08a3d2495fd61251fb10f656eac5a9bdb1bf03 converged 554 -41 d31c0e73a1d5612fcf092af2e809da4d3c9885b7 b0b83be573afad2be555f13a5f6d6fd0230e18d0 converged 564 -41 f6627139aaf47a9dff99ec9ba2dfbbcf5d566c2f 062b04858d37e6fcb37d4de8ac6b2e9e43b1d6db converged 600 -42 02650104e02b12a9a70a250a6b9d125f09fb1e16 0d0069de7424d621834a03c91c5d1078997cdc7a converged 305 -42 0d7b96c65bf8434dad8ff4ae7b2dac11ba59468f d94f409c497f0f3540c66b026e49e802a30c127d converged 324 -42 12957259e6f931c9e3dfa056e212ad43964188f1 01fcbc243dd480112fe137fbfa35f5b284660f67 converged 336 -42 1b00eaa10092ce6c27959ace686163bfbd1a5820 e89318206ed86e3dc6e5693a3c3c6997d768681d converged 349 -42 349cf0269b4a6f2ef93de480ebd03076c6cd074d 0fe58f2f14b805d7ad4ecb913d2a9dfacc9310a9 converged 377 -42 38c1d4a389933e0a95cf59a3ff97e399254e51c8 9c9091d511b41be403d384a2ab42249ff94347ff converged 383 -42 49a9123859f5e9a39df6993ca2b826b1c086fb6d 9ae96d718539e58018bced9b316831c54df92a87 converged 404 -42 588e4a744d60b67cf174f02cc6fbc47233b27bf2 61036fab2dc29c95608cf58633e8a4c8a182dee3 converged 421 -42 6aa1ef507f8055df7cb4ac988f9c99f4aa034242 db41c6ec577af9f874b7dd628f67cf0800b8f28b converged 443 -42 6e37ab0aab00d51d6ddc10c195fec2972a20e4ef 4cb66ac5144c867a4d4b900ee60341673a06660b converged 447 -42 76d281f71882b669130a32fcf9469f555f8a655b 4433d07c8e217166f012e154d1575e129a99a868 converged 455 -42 92dc32d1aae8dc0349641b124188432db051ebd2 c4606a03d0f78649d7e6f03e6f89f054a96ea437 converged 485 -42 9e464c8fe08c39ce5fa4952d6aca568bde73751c 578fca0a07838b4938e017db07dcb09c2d2ddaf9 converged 500 -42 bf3192d4367f1043d6a55cd2b97b2b856b5014b0 60bd0c629b65b6129493d4773a85bde5f2a74c33 converged 540 -42 dad4ffea3b799193558c2083b14fb4d0d4607b37 4d2383c8a99a7d3319b9a80ccba446d90e6821d9 converged 576 -43 062854d7ba0b4411e1d472c2fd4d00ea44d9d6cc 7581b2b9c2793fc40f86bab592e756c143059058 converged 310 -43 155d28c93d141f92e3cef3dace7746b4b42268c1 8bb91d2fab8b115e99d79e27ccb229a82932bdb1 converged 339 -43 3aa96165f327f320711286ec507f2c658987ad6a 4e43060d63f138c5a3175161d836531a35e05198 converged 386 -43 482e66ada430a624fb2ee6f2c743ef0cad295495 7c8bcd173a0a939af49ff473cdbd669f3f861645 converged 401 -43 5b6d6e295e2e83098930002d7d9b1597e8104f84 f7db28ec5e6d0837ddb8c51e4854507a7028ee9c converged 424 -43 633ced11405a6b40d20ab6daa7f4931c49878700 fa91c20929461bc675fee706de9f83a59313a501 converged 434 -43 704434095b342886b42dfea859c9c9f36a71bc38 c60a844891d20eaaed7296ee643bb3de39f96f82 converged 449 -43 9a51a1031e068fbbcaebd3e9e4079c8b103aa1cd 64d39622be61c40fe3fa534ea36240f99f0fae0a converged 495 -43 a778f23a229c2faa756ea3347694bf1382fc084f fc5d4df58ad85b8d75413dfc7c190fe3fe00efa2 converged 513 -43 ab48257c9f53c661fd4b069a55d55b89bfc9e721 64cfe85f7bc17550b9c4507f9fc0bad91cffad8b converged 516 -43 b4bf2fa96ffeddbd625786b9e3364bd1b4a6a283 cd8a386f031bf2bf9933d930ab2dadb155d2ad80 converged 525 -43 ccbd9202891dbe48ab8cafa97baf56cd5bb5e979 5b08a3d2495fd61251fb10f656eac5a9bdb1bf03 converged 554 -43 d0a58ef27f487fbbcdd30d7869a4a28f82cac2f5 314afd6b9615e8186ebf5fb68b575f202ca47ccd converged 561 -43 dde1b03bdb515fa5265fe148b747eea81a61ab79 2aa331b6560869e322698a489ad458304938ac72 converged 579 -43 fb012ac2a2a7ade37930fd553da9421cba16f552 691df76ae0043992aa6e418b8f4c31cd3a11ae6c converged 605 -44 02650104e02b12a9a70a250a6b9d125f09fb1e16 0d0069de7424d621834a03c91c5d1078997cdc7a converged 305 -44 072d449dc164e98c19805f67ee6a5519b780fd2d 10818f391a2cf0d3b851948d1017548a4dcdcedb converged 312 -44 0db939ade562d517d56b747cb60af3e32d859b09 b859bb9fe8800b8787bd1d7ee6af1bb2eaf2aeb9 converged 325 -44 12332689b0137519d1032e7b3c1bbdd99a145f05 7361f19a277131a8c4ff397320705acb004749b9 converged 335 -44 158124d17fb643b7104455e0e65a799b56035f94 a5bf1b48ae92cead636765987b792d3715b74b25 converged 340 -44 179bee3114d70926e6728f3e4970718f60a2db62 c141fa55e49f5c83566d1a00a8d69d3eb235b7f3 converged 345 -44 1affa858be371732e5a6c8dadf5bb1384529b3a0 622445f8016e1c2ea810a1bd7bfb1aef5312999a converged 348 -44 1cff723dd6ac1d7020c74fd21a675ae48520627c fa5706667a060097addc31389139b8f4cd5ba023 converged 351 -44 1edb836840d374c226c45cb34972bc0ec9b577a8 c77d4ecd252f0eb4888517bf9e10d2efe7810bb3 converged 355 -44 21bf947183e389b5548abc06f1e738c0cbed3d24 e65f02bc0ee5b88993e0ecfeff0cb49a7a9b34c1 converged 357 -44 2a2c28f48887aa266a915d1128edb1364f5fa1c8 634ae690616e603a8e55e65a259e3415812ad35a converged 368 -44 2b5cf83b16cd86be0ba5bc30d59546cdc32959da be71e3aca4425ae23e322a6f60eb2012eaf7e060 converged 369 -44 33bf9cebf5327ac9303e7fed3d7973237afe8ab3 62825b5a4fae7409f75fcc7ba1e6e7e8e45109af converged 376 -44 37d61eb6ac28e8143278bcff9ef74777b1324375 95460b25815075a3fbeb3fab2839a257a72e97ed converged 381 -44 3849d5a1fbb0386873b33b08109ffa41b62061c7 136d1c92b3c08ea87c51fa1cf5130b50fddb2571 converged 382 -44 58c02fbe1b4622c5cbfca110cb5f6f967188e0d9 d057ee065dcb431cbd94bcf71003e28e450a1ccf converged 422 -44 661ecef37ddb1bfdf490ef65585e50ce6a11f502 0a79450dc9d3087e007797b1d5c46c1c3bce5ff4 converged 437 -44 69a9339dc105b38760dce76df845acbc33fffb4d 03333dcfce170e5eb5cf4a7d9546561fa4ff11df converged 439 -44 6e37ab0aab00d51d6ddc10c195fec2972a20e4ef 4cb66ac5144c867a4d4b900ee60341673a06660b converged 447 -44 7d6f78b66e8515936a87583c487cf738c99e03a2 c5137429264481f986bb7d60fbfa6b4809a19c33 converged 461 -44 84710c8b233b12501fe802b02a84bcba77d988a4 046ef7d6d082fc3df8ae33f7050ce83eb1c0c013 converged 468 -44 926f28df75a0d353d70d4fd43bb2bae79c548cc9 faac47de85f78f9e912f5ea1cfac8551b994f4e5 converged 484 -44 a173f24b64fbb4b49472f71d1e99210ff743c99f 35ed233c5f8a38514ef1257bccfc842c13adbf9f converged 508 -44 ace4d6ad2f4b6cfc780885cb498c3af872fab5dc 91ae1376aacf11e1a1ac205fa633b8c83297c64f converged 517 -44 cefafcf222b91bc05c2325b9c540c1ec8462686b c5e03d4f7126e6c90d635848f24aac145d69c6c9 converged 559 -44 d6114df91f4560a386aed240f1a12a3c2afdbeff 3d04020fcd41f9f363aa2f08629f95f079e6b3ae converged 567 -44 dad4ffea3b799193558c2083b14fb4d0d4607b37 4d2383c8a99a7d3319b9a80ccba446d90e6821d9 converged 576 -44 e58194327ff02119c20ef726e2fe24563a009eb3 6dfc44cb1646cc4332c5280e685120d2a8ddbe99 converged 583 -44 ef8f080301d39f60ce5f3303a6f79fed411423fa 53aaf65012696b0b84ee2abab0493c004f9a7a21 converged 590 -44 f1a5501befe93a37ec3f8040fab9f356d9d902c5 dd1833ad4e5fd17b096cab6e3d74badd564d1e09 converged 594 -44 fe465c9d34b06277eebbfb6fdf492252fc12620d 8234f4af23a58948af434e5d533b606dd46cc27f converged 608 -45 0b9335c3cf2a43c4482e33fab75f23a3b0ec60bb b6a7125d7304e9dcd2d6dc29a2fc8d486552bb48 converged 322 -45 1660ac09ed9c437fc6a1450716fb24d95c44e5ce d4a14abeb1b48ffe99e29ab7113bad68bccb1fa6 converged 343 -45 278626eac43f672c7f61cb8f5b0f7240bf907776 1e3390e245a9874f3c23449e0eae7b49ec7e67b0 converged 364 -45 47d9e81a07bc42a0b046bd22369f1e207c184edd 7b0f01b58a2b60cd8543e75db67fd52376902d98 converged 400 -45 6b8afec69da36905e42d7cdaa982b12790dc01cd 193d2a1f6de2a3544ea19c7c790da2188575be03 converged 444 -45 748b26c95512fff36ce7849f35aa3ff840de49d9 8f591feede4d51f375f8412e4b665f74d6136fca converged 451 -45 7586012c89aa7aa29e6f719db06b1ed92b748003 3363ca677f9324c712a20f6f7ce2e6f4aad78567 converged 452 -45 761ad3e12d27ea12363980d677b398a917ff4a38 033e6ea8346a58e500c4c7d37ef075068c6f98ae converged 454 -45 77bc3594f18275b6882c60a981f6a5c935eee2f5 d13b5414234ec8335d31c7611bb98f7c8cffc2ff converged 457 -45 7f486a2ca57410de948da8623b3ac7f51c9d2065 0d31335ca2fbff357a588e184c0ffd3f8992054c converged 465 -45 8efc52286d7323942d723011bdf922e2312b218c 526e9c5a0ba26f8f0e9ad5ef9e6c0ef0733d4581 converged 478 -45 991fdf3e59e81f9c9d634e7dabcdabb78a7d7f6d 2a7652b5cb8f1eb29e568bf279f21221618695e7 converged 492 -45 994efbfd7077092941ab698950b510bcccdc578e 4e750bf595c12f1423f4b58489c049b8ea55f2af converged 493 -45 b2610f7388d950faa49cbcd9b55bc155d4e6a7e7 cdba4f3c4dfcd964491373706d32bc62e84a6c1e converged 523 -45 b7cb883975cd7ed029df1c79627116598fa634d4 ce3f8f0cb21e1f098dfee366bf3335ac550b6089 converged 531 -45 be36cd4a5a0e442c3c3340847af50b928eef68b9 3b16e69b91bcd2c3ab6235ae10a1cb2ad0957bfb converged 539 -45 c10d1db00e7b5129507e7db910069367072bcab9 72717581a8500561efa897d9aab0107c4f23697c converged 541 -45 c7cc0b43d4185bd3ffde5a89e431b5946b4ca85a 8772a23704729fe8d41a2ad70d7337e1e7129d04 converged 546 -45 cb8dbd055d69b6eb6a1e9784fd05dd8e14a37b9d 9c5a548c3a204b766201887d72c35ece52833d34 converged 551 -45 d7dda403d507084ed05cc1b9bf6c65829da6a22c afc0baea449e33ba08a33610c68093b43e07f4d7 converged 571 -45 de14295bbf982e71cb016c9837ad969232b6addc b9e9eaeb9a9b1957dc58068807ddfb7ac5c08407 converged 580 -45 e04cd576ce0e25075d86ad8d701a970966d70126 52e263ac53a8eea69c95496c56010c6f63ed0dd3 converged 581 -45 e0b6e36d3ff25cf77fbc653610885243e3d3434f abe6683bb7fc5334c5547c2a7a98a9a700c73e1b converged 582 -45 e8e8ab63aa33aac013912a7bba79e70bf0104670 55a3d6d36683780f84cb7b55dacc397f7af8c336 converged 586 -45 eaac7db71e9c295cf4770b3bac90f14898d2f3fe 81527b30bcce2963d313c8731a1862713ecffdc9 converged 587 -46 02650104e02b12a9a70a250a6b9d125f09fb1e16 0d0069de7424d621834a03c91c5d1078997cdc7a converged 305 -46 042bbc7494ef5cb0ca7e6465e9f6d6c0141647a7 5d2f6033c62fdc7bfee627a3bad9e496c737791d converged 306 -46 072d449dc164e98c19805f67ee6a5519b780fd2d 10818f391a2cf0d3b851948d1017548a4dcdcedb converged 312 -46 0d7b96c65bf8434dad8ff4ae7b2dac11ba59468f d94f409c497f0f3540c66b026e49e802a30c127d converged 324 -46 0db939ade562d517d56b747cb60af3e32d859b09 b859bb9fe8800b8787bd1d7ee6af1bb2eaf2aeb9 converged 325 -46 12332689b0137519d1032e7b3c1bbdd99a145f05 7361f19a277131a8c4ff397320705acb004749b9 converged 335 -46 12957259e6f931c9e3dfa056e212ad43964188f1 01fcbc243dd480112fe137fbfa35f5b284660f67 converged 336 -46 158124d17fb643b7104455e0e65a799b56035f94 a5bf1b48ae92cead636765987b792d3715b74b25 converged 340 -46 179bee3114d70926e6728f3e4970718f60a2db62 c141fa55e49f5c83566d1a00a8d69d3eb235b7f3 converged 345 -46 1affa858be371732e5a6c8dadf5bb1384529b3a0 622445f8016e1c2ea810a1bd7bfb1aef5312999a converged 348 -46 1b00eaa10092ce6c27959ace686163bfbd1a5820 e89318206ed86e3dc6e5693a3c3c6997d768681d converged 349 -46 1cff723dd6ac1d7020c74fd21a675ae48520627c fa5706667a060097addc31389139b8f4cd5ba023 converged 351 -46 1edb836840d374c226c45cb34972bc0ec9b577a8 c77d4ecd252f0eb4888517bf9e10d2efe7810bb3 converged 355 -46 214123bc075e6e379ec7078360d9b73f071a3045 6d9b78ce06d780d6b2146d67d32bfdee62c766c8 converged 356 -46 21bf947183e389b5548abc06f1e738c0cbed3d24 e65f02bc0ee5b88993e0ecfeff0cb49a7a9b34c1 converged 357 -46 268fa5ef37532a55a0689b5caeac7f13ec0d8b21 333db6803024d7e4fdbf7d9317d6ec44160a9ecb converged 362 -46 2a2c28f48887aa266a915d1128edb1364f5fa1c8 634ae690616e603a8e55e65a259e3415812ad35a converged 368 -46 2b5cf83b16cd86be0ba5bc30d59546cdc32959da be71e3aca4425ae23e322a6f60eb2012eaf7e060 converged 369 -46 2eb1ed5f4bfb876fdfe5bd571db34d924c3bb272 1714e98434d839e593204a3aa1e437670061eabb converged 371 -46 33bf9cebf5327ac9303e7fed3d7973237afe8ab3 62825b5a4fae7409f75fcc7ba1e6e7e8e45109af converged 376 -46 349cf0269b4a6f2ef93de480ebd03076c6cd074d 0fe58f2f14b805d7ad4ecb913d2a9dfacc9310a9 converged 377 -46 37d61eb6ac28e8143278bcff9ef74777b1324375 95460b25815075a3fbeb3fab2839a257a72e97ed converged 381 -46 3849d5a1fbb0386873b33b08109ffa41b62061c7 136d1c92b3c08ea87c51fa1cf5130b50fddb2571 converged 382 -46 38c1d4a389933e0a95cf59a3ff97e399254e51c8 9c9091d511b41be403d384a2ab42249ff94347ff converged 383 -46 3f960da3ec9176a5d454b984afa5a46056a51d2a 4b2c89d51b50c8afe550daacc73ce954df0464d6 converged 391 -46 49a9123859f5e9a39df6993ca2b826b1c086fb6d 9ae96d718539e58018bced9b316831c54df92a87 converged 404 -46 588e4a744d60b67cf174f02cc6fbc47233b27bf2 61036fab2dc29c95608cf58633e8a4c8a182dee3 converged 421 -46 58c02fbe1b4622c5cbfca110cb5f6f967188e0d9 d057ee065dcb431cbd94bcf71003e28e450a1ccf converged 422 -46 58edf590b6aab47a5d7285558406629508e605d0 d904aa0e51830eaddbc745dedf6cd5133c40735c converged 423 -46 661ecef37ddb1bfdf490ef65585e50ce6a11f502 0a79450dc9d3087e007797b1d5c46c1c3bce5ff4 converged 437 -46 69a9339dc105b38760dce76df845acbc33fffb4d 03333dcfce170e5eb5cf4a7d9546561fa4ff11df converged 439 -46 69eceee5397aebf212dc597716d6c17ede97646f 8a7e31803847b68f4a4b6a623aab17081bd0e683 converged 440 -46 6aa1ef507f8055df7cb4ac988f9c99f4aa034242 db41c6ec577af9f874b7dd628f67cf0800b8f28b converged 443 -46 6d4cd1885333b5178e725d711c86606516c7892f d7c2515d6e5c9b76626660dd87c674d5eeada349 converged 446 -46 6e37ab0aab00d51d6ddc10c195fec2972a20e4ef 4cb66ac5144c867a4d4b900ee60341673a06660b converged 447 -46 76d281f71882b669130a32fcf9469f555f8a655b 4433d07c8e217166f012e154d1575e129a99a868 converged 455 -46 7d6f78b66e8515936a87583c487cf738c99e03a2 c5137429264481f986bb7d60fbfa6b4809a19c33 converged 461 -46 84710c8b233b12501fe802b02a84bcba77d988a4 046ef7d6d082fc3df8ae33f7050ce83eb1c0c013 converged 468 -46 926f28df75a0d353d70d4fd43bb2bae79c548cc9 faac47de85f78f9e912f5ea1cfac8551b994f4e5 converged 484 -46 92dc32d1aae8dc0349641b124188432db051ebd2 c4606a03d0f78649d7e6f03e6f89f054a96ea437 converged 485 -46 9e464c8fe08c39ce5fa4952d6aca568bde73751c 578fca0a07838b4938e017db07dcb09c2d2ddaf9 converged 500 -46 a173f24b64fbb4b49472f71d1e99210ff743c99f 35ed233c5f8a38514ef1257bccfc842c13adbf9f converged 508 -46 aa42ad4c5b0e2558eb0dbe8d1a45ea8dc187f9bb 9af7c3ac221f978244fd78e1d141d27a81c9b884 converged 515 -46 ace4d6ad2f4b6cfc780885cb498c3af872fab5dc 91ae1376aacf11e1a1ac205fa633b8c83297c64f converged 517 -46 b0678c8ed27558036a4a3cae163928976d19a479 c5524b6a5f71b4edc48223c56d34aa56d30f7289 converged 520 -46 b7da8652aa4646ca8a183975092b76d92966c337 7da63c8acaa37e1183e91a1523c80e2d10a4d637 converged 532 -46 b928431c950a59bbcb99a651174576f524bf650d 3dca5eb836539b5a1c7ef2ab9a824e9d6e3bdd47 converged 535 -46 bf3192d4367f1043d6a55cd2b97b2b856b5014b0 60bd0c629b65b6129493d4773a85bde5f2a74c33 converged 540 -46 c2aafeee359f61a8007d42dc71d8e125b362a8c6 0826247200a541c85132b63883d8ab17aaa9aad4 converged 543 -46 c7c3f228039f1bf91c8f8a74441fe2246ac61b63 a4a68fd2506a318b0c2a416cb5085637507b8639 converged 545 -46 cefafcf222b91bc05c2325b9c540c1ec8462686b c5e03d4f7126e6c90d635848f24aac145d69c6c9 converged 559 -46 d6114df91f4560a386aed240f1a12a3c2afdbeff 3d04020fcd41f9f363aa2f08629f95f079e6b3ae converged 567 -46 dad4ffea3b799193558c2083b14fb4d0d4607b37 4d2383c8a99a7d3319b9a80ccba446d90e6821d9 converged 576 -46 dc7082da321e75ef33eafd6e45cfc31f49092646 2287368f5a2f29ffe32efa099dfbffaaaaa1c3e8 converged 578 -46 e58194327ff02119c20ef726e2fe24563a009eb3 6dfc44cb1646cc4332c5280e685120d2a8ddbe99 converged 583 -46 e59c51e4820db7be851b57738511abadddad37d7 a0bc132536a6b0e6c878d2a95a1563ce45cf2902 converged 584 -46 ef8f080301d39f60ce5f3303a6f79fed411423fa 53aaf65012696b0b84ee2abab0493c004f9a7a21 converged 590 -46 f1a5501befe93a37ec3f8040fab9f356d9d902c5 dd1833ad4e5fd17b096cab6e3d74badd564d1e09 converged 594 -46 fa3aa51785950d4dac83bb50473536372af1e01f eebc289a60081c199949feea664faa7dcdadbf04 converged 603 -46 fe465c9d34b06277eebbfb6fdf492252fc12620d 8234f4af23a58948af434e5d533b606dd46cc27f converged 608 -47 2353e2555f3b2cc6c617c6a5a7f6887fe28414ca af2e51c4f8fdb8f0fe5d03ca6cf1e913789b491f converged 359 -47 288d395431cf2eb781933d91a1217136466377b7 546b5fc41b4bef92b7e876cb3ac00d805d5157cf converged 366 -47 51609d9282f7a9e4195bcb0a3bb575b6daffcb6c f1211117fc34bd04905eaad2e2aa7b2336f3f2eb converged 414 -47 5603e1cd3c513d3cb058af4d5e3759590163da2d 8150d4f5e9059eb93d24e70ed880d81b111ab17e converged 419 -47 639921afb00a469b1f92c73a06a48454bf180b42 4cda8331fb46203e39f22d86a1150c07e3de0a7f converged 435 -47 748b26c95512fff36ce7849f35aa3ff840de49d9 8f591feede4d51f375f8412e4b665f74d6136fca converged 451 -47 77bc3594f18275b6882c60a981f6a5c935eee2f5 d13b5414234ec8335d31c7611bb98f7c8cffc2ff converged 457 -47 7f486a2ca57410de948da8623b3ac7f51c9d2065 0d31335ca2fbff357a588e184c0ffd3f8992054c converged 465 -47 85173b06048db8f63cb8dfe0b3c19112bae29816 e707f558fb86fa34e51830093d107fcd47364db9 converged 469 -47 994efbfd7077092941ab698950b510bcccdc578e 4e750bf595c12f1423f4b58489c049b8ea55f2af converged 493 -47 9f4009d7c6dab72a3092216b71d036b2f4c799a5 12703258a40d60ec1d6af8389d2b5776041f9414 converged 503 -47 b2610f7388d950faa49cbcd9b55bc155d4e6a7e7 cdba4f3c4dfcd964491373706d32bc62e84a6c1e converged 523 -47 b3e277071737f4fea97bc7a7e2efdf71a313134d e63287e1d2108533ee25068b6d00f786c46580b5 converged 524 -47 b7cb883975cd7ed029df1c79627116598fa634d4 ce3f8f0cb21e1f098dfee366bf3335ac550b6089 converged 531 -47 c7cc0b43d4185bd3ffde5a89e431b5946b4ca85a 8772a23704729fe8d41a2ad70d7337e1e7129d04 converged 546 -47 d230824d99f5bf7d59ac204bbc1b35f5293317a1 f444dfd86ca98c0fe86dddc91662ab47ceb70dc5 converged 562 -47 d7dda403d507084ed05cc1b9bf6c65829da6a22c afc0baea449e33ba08a33610c68093b43e07f4d7 converged 571 -47 e8da11d87c97fb1d0a2a13bfd37fe8f032b7b35b 888f9aab98d1fea5f37798d5e534a43dc3f25dcd converged 585 -47 e8e8ab63aa33aac013912a7bba79e70bf0104670 55a3d6d36683780f84cb7b55dacc397f7af8c336 converged 586 -47 ee44bd083e06c19498a8fea3d26855ca62bae4ba dd58bb655409c10ad5d82d9f196c3cb38a6f12af converged 589 -47 f1cf70ad90c37e205346d4c7b6dd7fe9b6f92730 5ccb2c952cb37915a26f6509063fbb3d2e8b2362 converged 595 -48 075e1ed2739aeffc3e5e0831d56826358336802e 512a7931e3abfb5ec1979319b37ef32774e5813d converged 313 -48 106b29bd87dd66dfc497b1ace292164de1cf3bf8 eb7e5d2b483492c71e82998e99e860f4a354f9d1 converged 331 -48 15f0fb655471f3272e07fd2e4c078ce671d84ef1 840c7d900bad653ed5acbe4441934a6b8a6ee579 converged 341 -48 18e1729f3b166edb687dc93cb09a978e641f70b2 94b7c729d7f2396c4dd9061056722f79f1337089 converged 347 -48 2a10b7314ca86904515bce84dd50ea69c54eab89 eccaf4eb519dcea4936e867e5e55b159674af078 converged 367 -48 2c765dbb487a0c5f372132fccf5bf477ee6ee8c0 92716c6bd02d34433b6c6f82ed1c80c6eb440434 converged 370 -48 3115a55165cf2efd12ab12e78e9514be8115a322 f7ceb78e820b60eb9acd5c8bb217b71472d5ab48 converged 374 -48 34a10292a7aeede12aaf0ada14f6e0dfb6c87b61 c7bc7a01a30f0c41dd3488ccf331856528002fdb converged 378 -48 5851d88b1ec0b1412a280c2605e919ab85eae26d f48260f71b2fc2ebdc86fc6df67bc5f58583b749 converged 420 -48 5be7d38257523a516109ee17da70be6b849bb4d2 02c38c0280bf7b429b226fc0e488fe606a2f26b4 converged 426 -48 612e355d6e0b948e7552858e256a3483fd343e6f 4024e2747ae829640390fe2586beafc1cd29d715 converged 431 -48 70ac24ae994cd77ab713cc81090b02f4ba9cf519 d1bf6ad7d371aba36faa6e642f1a206ff411348c converged 450 -48 8c299a02d9e8d20d2c28a5bc1acb67becf678d2c d5d93404e06a3af99679610ed172cc961ea775b7 converged 473 -48 8c5946c97abdaadd483711e34b11d509b1265fad 5a4a48cc331e510dd8356e338539ac5caa974ad4 converged 474 -48 961e4f1df3d5bc215f267e2fe62cd0aea7049303 8db0a1fb6e2cde17365ba15935d595fba3531be1 converged 490 -48 9eb0d6d53eb8c753562a2bbf0c5a90870ecfa324 567c09a72943c77e5ac19a0036a8cc8e186e3fe6 converged 501 -48 a21216a4c136b5f7152347da94d54d8b3bdcf918 0fc56f8b9779e8bb02f2777145ba835c3f2d6467 converged 509 -48 ae916af9c42834c88d02b364774af99f78c2e8a9 d0ed6c9c8c4c54fa7efeb59f334dfa9fa1359570 converged 519 -48 b5a8ca74a9ef56a34ad23829ba7cbaeb8006223a 06d84a0fa740748affad82b60000a78b5f75a6fd converged 528 -48 c95739f0f351d9979093a0d9686985b4898b4a44 16ac10f09dd230de1b31fe48e038d4c17be9cd6b converged 547 -48 d32b48c84c281f55910d246375dd2455a054c970 18ab0172266339f50060b0f560086cbaef445e08 converged 565 -48 d71dd9ef32818411e03aabcc7f952cbb8e073364 0ffbab2612ea0330ee5c4a021752e87740ac8cdb converged 570 -48 eff1b12635c7a318872a0f6b90f607945e244862 22ee9aa2467d132002db875e8eabcf634459a6a0 converged 591 -48 f041ef7e5426039e222d3d583c87473c23145c28 4a61a36f890ba4c3efb53abc2d61c2a9e1feca21 converged 592 -48 fa605cbbc698ba0f9acc3c71010dcb352ebdd981 90251bbbb9f49f8a96186c112c483386144356cb converged 604 -49 057c4998ce3043b33c0dc899cf3adcb6f8aadb62 61bb7682cb55431ff3a7c38c38928bd107a01736 converged 308 -49 075e1ed2739aeffc3e5e0831d56826358336802e 512a7931e3abfb5ec1979319b37ef32774e5813d converged 313 -49 08674a0e0f85623d4da72d469faa72d36287032f c0321cebfb67b63a1fda1e0c3f0664a666fef3d0 converged 316 -49 09e6375bd0ace29b879b739c21ff7640cc2d9048 87aad7cb691c2080682ad7e4f3110a5bb64983cb converged 319 -49 0a56eb8b37e7679cba2221f874905bb997678b38 fc0c9be4fccfc590996f339329ea21c294917d8a converged 320 -49 106b29bd87dd66dfc497b1ace292164de1cf3bf8 eb7e5d2b483492c71e82998e99e860f4a354f9d1 converged 331 -49 15f0fb655471f3272e07fd2e4c078ce671d84ef1 840c7d900bad653ed5acbe4441934a6b8a6ee579 converged 341 -49 18e1729f3b166edb687dc93cb09a978e641f70b2 94b7c729d7f2396c4dd9061056722f79f1337089 converged 347 -49 2a10b7314ca86904515bce84dd50ea69c54eab89 eccaf4eb519dcea4936e867e5e55b159674af078 converged 367 -49 2c765dbb487a0c5f372132fccf5bf477ee6ee8c0 92716c6bd02d34433b6c6f82ed1c80c6eb440434 converged 370 -49 3115a55165cf2efd12ab12e78e9514be8115a322 f7ceb78e820b60eb9acd5c8bb217b71472d5ab48 converged 374 -49 31c42076cad601a24eeeb60ba52e3947334cd0f2 66552cf682483e0dd8e2dd5c282cec91905e35f7 converged 375 -49 34a10292a7aeede12aaf0ada14f6e0dfb6c87b61 c7bc7a01a30f0c41dd3488ccf331856528002fdb converged 378 -49 35c8b84854c95e19ca73d3e5d0a8420aa1bb80db 0aa96dc6bf07436ea8ae2fad8f73ed77362af01d converged 379 -49 3a5c47e9d4ea1daceab214c179435ac2edf3e6b7 746945ed4f403585a5f1d971c08c9f123849bd7a converged 385 -49 426128635de4bcb7fac4c29bb21fdeb5eef38e99 e4edc15ffed638000c814f0ee60466075f4999ea converged 394 -49 43ec6cf9f9e284417842d43e46ad3c96f71a51f4 eacb87b0b465d61f0a28a078f8034f0deed7708a converged 396 -49 4a4ddb5d925e3c0c3f916977e0f867bb520ae90a 26266cec6d9a7e978b8c39ee633a8a1dd7bc042b converged 405 -49 4ac1db11145bc5622a889fd7155a3b3a943ab7ad a3d5806aef70cf565b6804832eecf3f070b2a3cf converged 407 -49 4dd7bf489369c0eb45f868fd47674e0adcee6a5e b1e93504c868d632894e41b13b3e57efca08ee9b converged 411 -49 52f2b937962ff1f0d9a7277ef70f0e85bce96995 773370abdf23484dce6df0405f0657e839178be5 converged 415 -49 5851d88b1ec0b1412a280c2605e919ab85eae26d f48260f71b2fc2ebdc86fc6df67bc5f58583b749 converged 420 -49 5be7d38257523a516109ee17da70be6b849bb4d2 02c38c0280bf7b429b226fc0e488fe606a2f26b4 converged 426 -49 5c8f6d1fb307a42de2f1f5475ef68aa95c61fd5f 9fe4d7eed066827e75926a46531a4dc63ea84201 converged 427 -49 612e355d6e0b948e7552858e256a3483fd343e6f 4024e2747ae829640390fe2586beafc1cd29d715 converged 431 -49 630a0debf01296e254447b1781d530268935bef4 adf6f5179f16bd2bdda444870dd6738f6b84a3c6 converged 433 -49 70ac24ae994cd77ab713cc81090b02f4ba9cf519 d1bf6ad7d371aba36faa6e642f1a206ff411348c converged 450 -49 770e20de1e7c78ad852e0c0b4bb9860505be1d9a a1ac62eb9b68f494992f03b7c8a8809118bee9d7 converged 456 -49 891703d3d06b0dd715625a0ca4b1750b19f7044f 55d9ee98c6f24ec215f3313649be915d501ef4e7 converged 471 -49 8c299a02d9e8d20d2c28a5bc1acb67becf678d2c d5d93404e06a3af99679610ed172cc961ea775b7 converged 473 -49 8c5946c97abdaadd483711e34b11d509b1265fad 5a4a48cc331e510dd8356e338539ac5caa974ad4 converged 474 -49 8e73e3916cd33249b7ccbbdd5de4ef309930d80c e9719cf3bd1cf004319367b93de95779bc28cd45 converged 476 -49 943c7def5fab3b9fc9afe34c1cf20e0a67cf3bc4 1d3cc34676dc1b466283e4e730d5da2bc50be51a converged 488 -49 961e4f1df3d5bc215f267e2fe62cd0aea7049303 8db0a1fb6e2cde17365ba15935d595fba3531be1 converged 490 -49 9ac768a05a1a5183538fbc360a6621819feb75c3 7d3136320e9ac9a10c015ee4af0cecd0a1370be0 converged 497 -49 9bffc6c309b836298fa0db1bc7eb4504c5302290 65935fdf2ab14f7daae2e248a295d8da703bb4ce converged 499 -49 9eb0d6d53eb8c753562a2bbf0c5a90870ecfa324 567c09a72943c77e5ac19a0036a8cc8e186e3fe6 converged 501 -49 9fbbc7c4a4b58c5f46b522e676aa6d25c1351f26 caeaca52899d1c25d1feed523d1491165180b5ee converged 505 -49 a21216a4c136b5f7152347da94d54d8b3bdcf918 0fc56f8b9779e8bb02f2777145ba835c3f2d6467 converged 509 -49 a6383108066814d4f3641a96f4dca690c595a3d3 4946c87290876d79f96844df0d3f25843e505a52 converged 512 -49 ae916af9c42834c88d02b364774af99f78c2e8a9 d0ed6c9c8c4c54fa7efeb59f334dfa9fa1359570 converged 519 -49 b1d8dde6f5b24daaa1612db2678889d5b763e3d9 aa27acd44048481f2facfeb94c982f96ceaa2edd converged 522 -49 b5a8ca74a9ef56a34ad23829ba7cbaeb8006223a 06d84a0fa740748affad82b60000a78b5f75a6fd converged 528 -49 c7759c891a2cf19e315832a2275d444ab0471877 3bf0a29570fb31d9d5858a2cda5331b3f3b09d7b converged 544 -49 c95739f0f351d9979093a0d9686985b4898b4a44 16ac10f09dd230de1b31fe48e038d4c17be9cd6b converged 547 -49 c9627b175ee0056d3439dbe670c377da7e93b290 19594b4873f3960302804802f235da4775d9eab9 converged 548 -49 ce4d63c752eabb99d46966a3ac431f6ea489d2b9 1075b9cd405f0740e344a4e7c1d92247c967118f converged 557 -49 ce4f79d894b07094ccfe03af7f1a12af67f0118c 4a188975a065b4fc56ce5e8ca0a324708a8b1db8 converged 558 -49 d32b48c84c281f55910d246375dd2455a054c970 18ab0172266339f50060b0f560086cbaef445e08 converged 565 -49 d486919734c6126af9e5384e8b97195a1be735bd 63515c03d06f6af2f0d5936b97b758bee90747f0 converged 566 -49 d6bb53c9858fddf173898cdcfa1cbf42f5d52c5c 26be8c03f00cff0952b0f8da9bb422344ea542d8 converged 569 -49 d71dd9ef32818411e03aabcc7f952cbb8e073364 0ffbab2612ea0330ee5c4a021752e87740ac8cdb converged 570 -49 d878f7c6794d0a6d9a8d04be68ebb93eafdf01cc 3dee05bcf4782a1d4eacdbdfaabdd7cd0700f025 converged 572 -49 d9f521188e10884be7feb4e36e64c54ba92a2eb0 cde6a902ff7a65a904396b4a03543719ca63f620 converged 574 -49 dc504e7e6d04d45ccc6e6eb088e10e6ae370db7f 877a07d273ffa7a31d0898c763e6de31a611cf67 converged 577 -49 eff1b12635c7a318872a0f6b90f607945e244862 22ee9aa2467d132002db875e8eabcf634459a6a0 converged 591 -49 f041ef7e5426039e222d3d583c87473c23145c28 4a61a36f890ba4c3efb53abc2d61c2a9e1feca21 converged 592 -49 f28af1197504d2ee7f1e057da67aad80fe1df0b0 faf489eee1f3cc3ebaefc6cef4b2509387a7d955 converged 596 -49 f5b405df003a35ecfe879bb5eb7dae6b4eec2dad 6055795e2af65cfdf6d2ba326df808a1ed533d37 converged 599 -49 fa605cbbc698ba0f9acc3c71010dcb352ebdd981 90251bbbb9f49f8a96186c112c483386144356cb converged 604 -50 07abe67565ef00ca65939290f57b7f8fed0ef3e9 db909c4d5e17b692dbe18375ccba7e4912dc8b30 converged 315 -50 109f8d0f5bbe62523061149347330b571c675604 a7276147140056217123a5873763e01758457e1f converged 332 -50 286e32b908ef23fe49d3da4bf383190839677948 8d235200eeefc8b41cf2e2045167247cdf906b6d converged 365 -50 45ad6a215f64db417d49b8bdbe368e4950899b53 ec4f083e53c49ad337cd4d813af31a9b84826f1c converged 399 -50 4b8edc749fc6ea1831e6b5ac7f9c5ae8aded5628 9789e717ba33952ef85c09d4029fd358635d9f5b converged 408 -50 69efb804ab1932a948600494efb7073504826206 23dfd6e2883ca16f8d9827b9ea3f6818dcc0432f converged 441 -50 860f565f39c4491817ca18b59a2a542828b35ab8 df76b02b6b6e27779c5ba87e36769d08eefc0493 converged 470 -50 90246b4f81a9a10e1f2fbd0c89bed45ceed1dd19 f2dbd8cada7b0322cda13c02aa0ecaefaf869f60 converged 481 -50 ee11d8f8f5d4701867dfb1ede321c37ed45792f6 6aaac27e1084e027b18fae8ef7f9106676259b95 converged 588 -51 0531c19f799288421c7093bc1a7e8739d91a6780 0b4234b9fdd7b7fcdef8de4a1d96ddd8af92ec43 converged 307 -51 06f8fbd96827ebcc2af6cd68eb9d01db513af0cc 54e136b150ae049afc4e083f47bf88b27faf60c6 converged 311 -51 16a422954ecee1958247a1cbccca2cc072eb48a6 7c4f93cd26c40370592e81e7a3ca97fc43738303 converged 344 -51 3aa96165f327f320711286ec507f2c658987ad6a 4e43060d63f138c5a3175161d836531a35e05198 converged 386 -51 41da3955199dd0ca8f3277a04c8d5275a74de03d 550a1cea1689e4820ef2783ad54fb87c4514681d converged 393 -51 490525dae0da5b33f52c8cceac2abaf5695c1dae e6b10634f7ae229651896e25a48c7ffd89ab399f converged 402 -51 4a9a780375969a9f7ecefcd7f94a7084917b02c0 9395b4f7e6095bfadeb88d38d63bfca21bddf1b0 converged 406 -51 4fcbfab58e277bb9df4f7b20795b1a394a086639 b6d5dfd20c8460a6d4f87fbaff9eb82c846f93cf converged 413 -51 6869e075cac2ea8a726b825f52feb9cb19282c9d cdbef69d4c340f80692f185052db48029f2dc26f converged 438 -51 8a005e4d231a4353d929481925baf648189614b7 2460c1e9c3be656cc9fc7221fc0ebcd4a675765b converged 472 -51 8f59452aa3db35358ce6ceca6e899cadb572dad3 dcd93b5428328bfc4ac97a71b2d31b46d55f0307 converged 480 -51 94d66ec8e8f53ff1f84f8db383082eb901747fa1 720108279293b8fd0be4da66cb73d3c279037f86 converged 489 -51 a41a2816c7ddadedd3b0c78171fb8c1b9fda38ca 5e1190bc17503c61ff2a4c8e3e1813d174128968 converged 511 -51 ab48257c9f53c661fd4b069a55d55b89bfc9e721 64cfe85f7bc17550b9c4507f9fc0bad91cffad8b converged 516 -51 b15d26612f7a81a0115867e264977b4310422ba1 4e1ed0b237ebadd7530ab974b4f6d063950baffc converged 521 -51 b4bf2fa96ffeddbd625786b9e3364bd1b4a6a283 cd8a386f031bf2bf9933d930ab2dadb155d2ad80 converged 525 -51 c196bb26dca6f755184e623394b6ee73b530cdab 54e2a84b40997302db2b5a67931012d62fc11804 converged 542 -51 cceb2e64f460497f3abce3cdf940796305922ad4 7160a8725f01d13112ca34bb64237f7d0b7d508e converged 555 -51 cd197cce362a92d13965ca88db7dded4fa2f115d 76dd4e631e1ebc0401c67ea88fef7bf116fd720b converged 556 -51 dde1b03bdb515fa5265fe148b747eea81a61ab79 2aa331b6560869e322698a489ad458304938ac72 converged 579 -51 fd41c8aaac996261aae6dfd0cf486f8cdb008a6b d9467c8ca1b2d943b4b997cadedc960f937cdf83 converged 607 -52 042bbc7494ef5cb0ca7e6465e9f6d6c0141647a7 5d2f6033c62fdc7bfee627a3bad9e496c737791d converged 306 -52 12332689b0137519d1032e7b3c1bbdd99a145f05 7361f19a277131a8c4ff397320705acb004749b9 converged 335 -52 179bee3114d70926e6728f3e4970718f60a2db62 c141fa55e49f5c83566d1a00a8d69d3eb235b7f3 converged 345 -52 1edb836840d374c226c45cb34972bc0ec9b577a8 c77d4ecd252f0eb4888517bf9e10d2efe7810bb3 converged 355 -52 268fa5ef37532a55a0689b5caeac7f13ec0d8b21 333db6803024d7e4fdbf7d9317d6ec44160a9ecb converged 362 -52 2a2c28f48887aa266a915d1128edb1364f5fa1c8 634ae690616e603a8e55e65a259e3415812ad35a converged 368 -52 2b5cf83b16cd86be0ba5bc30d59546cdc32959da be71e3aca4425ae23e322a6f60eb2012eaf7e060 converged 369 -52 2eb1ed5f4bfb876fdfe5bd571db34d924c3bb272 1714e98434d839e593204a3aa1e437670061eabb converged 371 -52 33bf9cebf5327ac9303e7fed3d7973237afe8ab3 62825b5a4fae7409f75fcc7ba1e6e7e8e45109af converged 376 -52 3849d5a1fbb0386873b33b08109ffa41b62061c7 136d1c92b3c08ea87c51fa1cf5130b50fddb2571 converged 382 -52 58edf590b6aab47a5d7285558406629508e605d0 d904aa0e51830eaddbc745dedf6cd5133c40735c converged 423 -52 661ecef37ddb1bfdf490ef65585e50ce6a11f502 0a79450dc9d3087e007797b1d5c46c1c3bce5ff4 converged 437 -52 69a9339dc105b38760dce76df845acbc33fffb4d 03333dcfce170e5eb5cf4a7d9546561fa4ff11df converged 439 -52 69eceee5397aebf212dc597716d6c17ede97646f 8a7e31803847b68f4a4b6a623aab17081bd0e683 converged 440 -52 6d4cd1885333b5178e725d711c86606516c7892f d7c2515d6e5c9b76626660dd87c674d5eeada349 converged 446 -52 7d6f78b66e8515936a87583c487cf738c99e03a2 c5137429264481f986bb7d60fbfa6b4809a19c33 converged 461 -52 b0678c8ed27558036a4a3cae163928976d19a479 c5524b6a5f71b4edc48223c56d34aa56d30f7289 converged 520 -52 b928431c950a59bbcb99a651174576f524bf650d 3dca5eb836539b5a1c7ef2ab9a824e9d6e3bdd47 converged 535 -52 c2aafeee359f61a8007d42dc71d8e125b362a8c6 0826247200a541c85132b63883d8ab17aaa9aad4 converged 543 -52 d6114df91f4560a386aed240f1a12a3c2afdbeff 3d04020fcd41f9f363aa2f08629f95f079e6b3ae converged 567 -52 dc7082da321e75ef33eafd6e45cfc31f49092646 2287368f5a2f29ffe32efa099dfbffaaaaa1c3e8 converged 578 -52 e58194327ff02119c20ef726e2fe24563a009eb3 6dfc44cb1646cc4332c5280e685120d2a8ddbe99 converged 583 -52 e59c51e4820db7be851b57738511abadddad37d7 a0bc132536a6b0e6c878d2a95a1563ce45cf2902 converged 584 -52 ef8f080301d39f60ce5f3303a6f79fed411423fa 53aaf65012696b0b84ee2abab0493c004f9a7a21 converged 590 -52 fa3aa51785950d4dac83bb50473536372af1e01f eebc289a60081c199949feea664faa7dcdadbf04 converged 603 -53 0531c19f799288421c7093bc1a7e8739d91a6780 0b4234b9fdd7b7fcdef8de4a1d96ddd8af92ec43 converged 307 -53 057e825aff074e168523706c03897e0777df9b94 5a4dd5ca0b87789189baf6b3323a71c23fa08044 converged 309 -53 062854d7ba0b4411e1d472c2fd4d00ea44d9d6cc 7581b2b9c2793fc40f86bab592e756c143059058 converged 310 -53 0e21dea54f9050ff6ddec278c6156f6f67fa4620 09a89d9a5187b4349092cc849cf0659c323d8efe converged 327 -53 0e512f5267893868c482de2eaad7e086720f9da8 d03303db3c6219c54451905681e93ee0ecee501a converged 328 -53 101ae292210d6919e7202b9789585f3003181bb6 976dfd8fe9c8178462e0556b70e34e6aafa4f4e4 converged 330 -53 1143e70ace47ac9c9bade2025ba25a37cf0fa798 1bb7be909daa040e546a53aadf0872795bbb267f converged 334 -53 14913922c0fbab173e657b2a819e2c1dec9f01f0 529b2b51674c7726ece779c27f68297d477c55be converged 337 -53 155d28c93d141f92e3cef3dace7746b4b42268c1 8bb91d2fab8b115e99d79e27ccb229a82932bdb1 converged 339 -53 16a422954ecee1958247a1cbccca2cc072eb48a6 7c4f93cd26c40370592e81e7a3ca97fc43738303 converged 344 -53 1bd9dc3c69c6706234cd1004c04c73938474f9f8 810fa7cc6a922561b070aad131149e8a6ca93b47 converged 350 -53 1eba32b1bf4f9a46630e975ca3136089e0db1ae0 5f6e491ea488340bc5a1ae9950192c443235736c converged 353 -53 1ec158b6dcff1a27b37423604529175845d69dc1 f8b034f1a9db699ea3db37a5bee0ccd83682c041 converged 354 -53 3aa96165f327f320711286ec507f2c658987ad6a 4e43060d63f138c5a3175161d836531a35e05198 converged 386 -53 482e66ada430a624fb2ee6f2c743ef0cad295495 7c8bcd173a0a939af49ff473cdbd669f3f861645 converged 401 -53 494f8e62354ed6fceadf7b4266909da1cb6cfc0f 384f4dcfa8e4c5e0fe8dc520b32592bb1494b46e converged 403 -53 4d8dd369d5876d899d0442cd2676b4aa77c108ed c662dba7a84e43f7436af50d9e26b42ea3195169 converged 410 -53 4fcbfab58e277bb9df4f7b20795b1a394a086639 b6d5dfd20c8460a6d4f87fbaff9eb82c846f93cf converged 413 -53 5b6d6e295e2e83098930002d7d9b1597e8104f84 f7db28ec5e6d0837ddb8c51e4854507a7028ee9c converged 424 -53 5baa9b7c1cd1a2259cc473814a4389647bb6f312 9c107016dcb3d4a7fbc494c3a594c4ecb00e4d92 converged 425 -53 609efe21f6eb9bae6f533e933e8d1bd32c868990 a8f43312993371bd04feac9864b9384d2bd9fc0e converged 429 -53 610e0202389348f6d8ed29b1ff13364c67ccb41e 9f4f490a037ac351a8175426ff67afb6a0948574 converged 430 -53 633ced11405a6b40d20ab6daa7f4931c49878700 fa91c20929461bc675fee706de9f83a59313a501 converged 434 -53 656bd38a12cd3be747e05454892f63ef7d363838 8d63cd33ed7159cc70717599623c634604de049a converged 436 -53 6869e075cac2ea8a726b825f52feb9cb19282c9d cdbef69d4c340f80692f185052db48029f2dc26f converged 438 -53 6c788fc78680e305f5bb2a1610c62142f950ccf3 9e223cd1a0f23b0ab63b1d39e7bed58fccc1f21f converged 445 -53 704434095b342886b42dfea859c9c9f36a71bc38 c60a844891d20eaaed7296ee643bb3de39f96f82 converged 449 -53 75c8f1e53570db45131d425d2dd16c4469969077 49d4808593297a1093c46be318d38f5410810371 converged 453 -53 7b6ea3ca13c428ab14ace9e8e92987f4c652550b 1cef97ee856f2addf2779443364657b9335a2d8f converged 458 -53 7c0eb388a0fbeff738c5767a89ed28c9254dd147 39d77d5e4f0999e8f8cfa5f9d44e7f822a44c549 converged 459 -53 8a005e4d231a4353d929481925baf648189614b7 2460c1e9c3be656cc9fc7221fc0ebcd4a675765b converged 472 -53 8ea6ebfd7a7d1c9c756c49d2e6523b01de9b83e6 f7194dbd76ffc5c1b2383f31bff31d10577fd981 converged 477 -53 9188e5c25e8616a28af08c97dd8d5633755a6321 2d45ea3a4464a803f76ecbd2ce77d4fcc78b6fd3 converged 482 -53 9210d9c448226ab6432d66d5452df74a65c0c44b 951da31474f5a9a5239e9928ea748d334eab324b converged 483 -53 92fa4376954618fbf5ca8d4bb3000d4effbdbe96 e47d04681382ff755006878a726695cdccd4d5ff converged 486 -53 94052f5b5f78700311bba2a7edec5694e7570206 7ddd99c903ecc5bce5f4a1b774835ba1820e5313 converged 487 -53 9780e4c83411b9a4d781de9bdf7a865017012e36 973189cb238ffe3af9ca882d82eb98de4c729833 converged 491 -53 9982829dfdbd9a0dd00ea036ecb90759dcb07301 d7aa3f1f578571271174caa4f820d8ee6c05168f converged 494 -53 9a51a1031e068fbbcaebd3e9e4079c8b103aa1cd 64d39622be61c40fe3fa534ea36240f99f0fae0a converged 495 -53 9ba0ca84ebe3f443d0a525ead480c8edf26f4b85 17f753388b239e09fc1a6ab2cee500b436a61db8 converged 498 -53 a41a2816c7ddadedd3b0c78171fb8c1b9fda38ca 5e1190bc17503c61ff2a4c8e3e1813d174128968 converged 511 -53 a778f23a229c2faa756ea3347694bf1382fc084f fc5d4df58ad85b8d75413dfc7c190fe3fe00efa2 converged 513 -53 ab48257c9f53c661fd4b069a55d55b89bfc9e721 64cfe85f7bc17550b9c4507f9fc0bad91cffad8b converged 516 -53 ad48594408b255e610e12ed95d678c0feb4094e4 8adf3752709af5ed2e2ef469acc78991b5daa9ca converged 518 -53 b4bf2fa96ffeddbd625786b9e3364bd1b4a6a283 cd8a386f031bf2bf9933d930ab2dadb155d2ad80 converged 525 -53 b5e4c0c917c548e86d915f7869b194e66224dad5 8cc7102ee1db508fad197a1ec32958d72ec5e18d converged 529 -53 bd873ae7f2367d19e33e16101939fe7f628f5877 4de395fab21bae1a1bc8ac72fb0eaad0a9d657b8 converged 537 -53 c196bb26dca6f755184e623394b6ee73b530cdab 54e2a84b40997302db2b5a67931012d62fc11804 converged 542 -53 cbe6ec589b3f32701962efcd767f2721a50b3be1 4f9b0ee671cfb1c9bb2be2732c45e82fe3696be3 converged 552 -53 ccbd9202891dbe48ab8cafa97baf56cd5bb5e979 5b08a3d2495fd61251fb10f656eac5a9bdb1bf03 converged 554 -53 cceb2e64f460497f3abce3cdf940796305922ad4 7160a8725f01d13112ca34bb64237f7d0b7d508e converged 555 -53 cd197cce362a92d13965ca88db7dded4fa2f115d 76dd4e631e1ebc0401c67ea88fef7bf116fd720b converged 556 -53 d0a58ef27f487fbbcdd30d7869a4a28f82cac2f5 314afd6b9615e8186ebf5fb68b575f202ca47ccd converged 561 -53 d30c6d7c0f0d1582bb1260f70d0b5f9e9df249b2 5a2c2dd5d0cf690605494991e06fd13408c86434 converged 563 -53 d31c0e73a1d5612fcf092af2e809da4d3c9885b7 b0b83be573afad2be555f13a5f6d6fd0230e18d0 converged 564 -53 dde1b03bdb515fa5265fe148b747eea81a61ab79 2aa331b6560869e322698a489ad458304938ac72 converged 579 -53 f136a8b9acd0baf5efa5ed79a72f4688fe4c41da a74d9244ff20f94d09a066cf9885078a776d6989 converged 593 -53 f6627139aaf47a9dff99ec9ba2dfbbcf5d566c2f 062b04858d37e6fcb37d4de8ac6b2e9e43b1d6db converged 600 -53 fb012ac2a2a7ade37930fd553da9421cba16f552 691df76ae0043992aa6e418b8f4c31cd3a11ae6c converged 605 -53 fd41c8aaac996261aae6dfd0cf486f8cdb008a6b d9467c8ca1b2d943b4b997cadedc960f937cdf83 converged 607 -54 057c4998ce3043b33c0dc899cf3adcb6f8aadb62 61bb7682cb55431ff3a7c38c38928bd107a01736 converged 308 -54 09e6375bd0ace29b879b739c21ff7640cc2d9048 87aad7cb691c2080682ad7e4f3110a5bb64983cb converged 319 -54 35c8b84854c95e19ca73d3e5d0a8420aa1bb80db 0aa96dc6bf07436ea8ae2fad8f73ed77362af01d converged 379 -54 426128635de4bcb7fac4c29bb21fdeb5eef38e99 e4edc15ffed638000c814f0ee60466075f4999ea converged 394 -54 4a4ddb5d925e3c0c3f916977e0f867bb520ae90a 26266cec6d9a7e978b8c39ee633a8a1dd7bc042b converged 405 -54 4dd7bf489369c0eb45f868fd47674e0adcee6a5e b1e93504c868d632894e41b13b3e57efca08ee9b converged 411 -54 891703d3d06b0dd715625a0ca4b1750b19f7044f 55d9ee98c6f24ec215f3313649be915d501ef4e7 converged 471 -54 8e73e3916cd33249b7ccbbdd5de4ef309930d80c e9719cf3bd1cf004319367b93de95779bc28cd45 converged 476 -54 9fbbc7c4a4b58c5f46b522e676aa6d25c1351f26 caeaca52899d1c25d1feed523d1491165180b5ee converged 505 -54 b1d8dde6f5b24daaa1612db2678889d5b763e3d9 aa27acd44048481f2facfeb94c982f96ceaa2edd converged 522 -54 c7759c891a2cf19e315832a2275d444ab0471877 3bf0a29570fb31d9d5858a2cda5331b3f3b09d7b converged 544 -54 ce4d63c752eabb99d46966a3ac431f6ea489d2b9 1075b9cd405f0740e344a4e7c1d92247c967118f converged 557 -54 d9f521188e10884be7feb4e36e64c54ba92a2eb0 cde6a902ff7a65a904396b4a03543719ca63f620 converged 574 -54 dc504e7e6d04d45ccc6e6eb088e10e6ae370db7f 877a07d273ffa7a31d0898c763e6de31a611cf67 converged 577 -54 f5b405df003a35ecfe879bb5eb7dae6b4eec2dad 6055795e2af65cfdf6d2ba326df808a1ed533d37 converged 599 -55 0531c19f799288421c7093bc1a7e8739d91a6780 0b4234b9fdd7b7fcdef8de4a1d96ddd8af92ec43 converged 307 -55 0e21dea54f9050ff6ddec278c6156f6f67fa4620 09a89d9a5187b4349092cc849cf0659c323d8efe converged 327 -55 101ae292210d6919e7202b9789585f3003181bb6 976dfd8fe9c8178462e0556b70e34e6aafa4f4e4 converged 330 -55 1143e70ace47ac9c9bade2025ba25a37cf0fa798 1bb7be909daa040e546a53aadf0872795bbb267f converged 334 -55 16a422954ecee1958247a1cbccca2cc072eb48a6 7c4f93cd26c40370592e81e7a3ca97fc43738303 converged 344 -55 1eba32b1bf4f9a46630e975ca3136089e0db1ae0 5f6e491ea488340bc5a1ae9950192c443235736c converged 353 -55 1ec158b6dcff1a27b37423604529175845d69dc1 f8b034f1a9db699ea3db37a5bee0ccd83682c041 converged 354 -55 656bd38a12cd3be747e05454892f63ef7d363838 8d63cd33ed7159cc70717599623c634604de049a converged 436 -55 6869e075cac2ea8a726b825f52feb9cb19282c9d cdbef69d4c340f80692f185052db48029f2dc26f converged 438 -55 6c788fc78680e305f5bb2a1610c62142f950ccf3 9e223cd1a0f23b0ab63b1d39e7bed58fccc1f21f converged 445 -55 75c8f1e53570db45131d425d2dd16c4469969077 49d4808593297a1093c46be318d38f5410810371 converged 453 -55 7b6ea3ca13c428ab14ace9e8e92987f4c652550b 1cef97ee856f2addf2779443364657b9335a2d8f converged 458 -55 8a005e4d231a4353d929481925baf648189614b7 2460c1e9c3be656cc9fc7221fc0ebcd4a675765b converged 472 -55 8ea6ebfd7a7d1c9c756c49d2e6523b01de9b83e6 f7194dbd76ffc5c1b2383f31bff31d10577fd981 converged 477 -55 9188e5c25e8616a28af08c97dd8d5633755a6321 2d45ea3a4464a803f76ecbd2ce77d4fcc78b6fd3 converged 482 -55 9210d9c448226ab6432d66d5452df74a65c0c44b 951da31474f5a9a5239e9928ea748d334eab324b converged 483 -55 94052f5b5f78700311bba2a7edec5694e7570206 7ddd99c903ecc5bce5f4a1b774835ba1820e5313 converged 487 -55 9780e4c83411b9a4d781de9bdf7a865017012e36 973189cb238ffe3af9ca882d82eb98de4c729833 converged 491 -55 9982829dfdbd9a0dd00ea036ecb90759dcb07301 d7aa3f1f578571271174caa4f820d8ee6c05168f converged 494 -55 a41a2816c7ddadedd3b0c78171fb8c1b9fda38ca 5e1190bc17503c61ff2a4c8e3e1813d174128968 converged 511 -55 c196bb26dca6f755184e623394b6ee73b530cdab 54e2a84b40997302db2b5a67931012d62fc11804 converged 542 -55 cceb2e64f460497f3abce3cdf940796305922ad4 7160a8725f01d13112ca34bb64237f7d0b7d508e converged 555 -55 cd197cce362a92d13965ca88db7dded4fa2f115d 76dd4e631e1ebc0401c67ea88fef7bf116fd720b converged 556 -55 f6627139aaf47a9dff99ec9ba2dfbbcf5d566c2f 062b04858d37e6fcb37d4de8ac6b2e9e43b1d6db converged 600 -55 fd41c8aaac996261aae6dfd0cf486f8cdb008a6b d9467c8ca1b2d943b4b997cadedc960f937cdf83 converged 607 -56 0e1b37392255c8e1029048961c20b6f6ac9111ab afc6fee3f93f2ff762f1297a7da7d750481e8e38 converged 326 -56 112bc01f010f8df228a9524987461f9f34ea0437 3f177c5fc12341b89ef8bef5d452a6df3b4f761e converged 333 -56 21f4f441cdec6810f42efa617cb6491521b7fa3d 57962b7cef26f050c3378e186a634395d75a60ed converged 358 -56 26cc6a41b8cd906d1d4e5432584029b512ed58e4 3c7d1bf72bcf66b65cbfe71125fbb703c181749c converged 363 -56 3cd819d63095c0c33ae71323c04e43c015a7fa88 f78d50088bd450c004336de2ca048aad8655b5fe converged 387 -56 3f48452e8a434e8931cd5b5e074f0f2cf55e729b becfac3d367264b6f9c2d5cc42931bc8ed849f29 converged 390 -56 a13c8811843bc15c45d10059ed4fa97a0c97e622 c27f7f9fc5c3502db4b38df816ddb304b3b3503e converged 507 -56 cfb28c686040b3dcc22a94554c8161b1974d23a8 afe25b551072b5e0dcfad710899d1b8430474e9a converged 560 -56 f8596ee0738fd16a5157d960bb58795b73bbc92e bf0bfe30b8f5655f7f2bae16b2caa8a3e871159f converged 601 +analysis parent +1 01820143b8dad579042e97586dfcaa8d836632df +1 02281934448385eab0ba82aab9439774075bc749 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git2rdata: 0.5.2 optimize: yes NA string: NA + name: stratum + title: Strata using in the Common Breeding Bird Cencus in Flanders sorting: description - hash: 8939235374ae6387b0fb36f196c32d876cb1d4d9 + hash: cf2b97fe93643b437e0b1b4f17ae01a5c55b3b78 data_hash: 1e324ca9fc741d5308441a4e2cd7a6caf75f41c4 description: class: character + description: Name of the stratum 'n': class: integer + description: Total number of squares in the stratum id: class: integer + description: Unique identifier of the stratum diff --git a/inst/results/stratum_weight.tsv b/inst/results/stratum_weight.tsv index 5e3f13d..d87b3e3 100644 --- a/inst/results/stratum_weight.tsv +++ b/inst/results/stratum_weight.tsv @@ -1,458 +1,494 @@ points_1 points_2 points_3 points_4 points_5 points_6 relevant visits analysis stratum weight -12 40 49 19 20 11 151 6499 97 Landbouw 1 -3 16 9 5 13 14 60 3196 98 Bos 0.25575 -2 4 4 6 12 28 56 3467 98 Heide en duin 0.30257 -4 3 0 3 1 1 12 503 98 Landbouw 0.41108 -0 2 3 1 0 1 7 345 98 Moeras en water 0.03059 -0 3 4 15 21 98 141 9237 99 Bos 0.03966 -0 3 4 7 13 53 80 5119 99 Heide en duin 0.02852 -0 3 4 14 20 297 338 23418 99 Landbouw 0.83343 -0 0 0 4 10 48 62 4166 99 Moeras en water 0.01788 -0 1 2 4 7 41 55 3476 99 Suburbaan 0.02738 -0 1 3 6 10 46 66 4425 99 Urbaan 0.05314 -3 14 11 4 0 1 33 1195 100 Bos 0.43547 -0 4 5 2 1 1 13 646 100 Heide en duin 0.21745 -1 2 1 0 0 0 4 124 100 Landbouw 0.34708 -1 2 0 1 0 0 4 119 101 Bos 0.00515 -3 3 0 1 1 0 8 137 101 Heide en duin 0.01523 -7 11 12 11 8 10 59 1972 101 Landbouw 0.88795 -3 8 7 3 10 9 40 1400 101 Moeras en water 0.07041 -1 2 3 1 0 1 8 144 101 Suburbaan 0.02127 -3 5 6 3 2 0 19 784 102 Bos 0.0178 -1 9 4 6 4 5 29 1363 102 Heide en duin 0.03443 -5 25 21 24 17 14 106 5273 102 Landbouw 0.87043 -2 6 12 7 6 9 42 2164 102 Moeras en water 0.04034 -0 4 3 3 0 1 11 509 102 Suburbaan 0.01823 -3 4 1 0 0 0 8 150 102 Urbaan 0.01877 -1 1 1 1 0 0 4 149 103 Bos 0.005 -1 2 2 3 1 1 10 513 103 Heide en duin 0.01902 -5 17 13 4 13 10 62 2998 103 Landbouw 0.90622 -4 6 7 3 6 1 27 1018 103 Moeras en water 0.04615 -1 3 1 2 2 0 9 306 103 Suburbaan 0.0236 -2 1 0 1 0 0 4 140 104 Bos 0.00908 -3 3 5 3 3 2 19 810 104 Heide en duin 0.07292 -4 4 11 4 4 4 31 1553 104 Landbouw 0.8229 -1 4 4 8 3 2 22 1089 104 Moeras en water 0.0683 -1 0 1 1 1 2 6 207 104 Suburbaan 0.02679 -3 6 5 5 5 5 29 1502 105 Landbouw 0.98805 -2 1 1 0 0 0 4 82 105 Moeras en water 0.01195 -7 19 15 11 2 1 55 2178 106 Bos 0.07929 -4 8 10 2 0 1 25 989 106 Heide en duin 0.04568 -10 35 8 3 0 1 57 1823 106 Landbouw 0.7204 -3 9 5 8 3 3 31 1375 106 Moeras en water 0.04582 -1 6 7 4 1 1 20 868 106 Suburbaan 0.05103 -4 5 3 1 1 0 14 509 106 Urbaan 0.05778 -3 12 14 22 14 21 86 4870 107 Bos 0.03327 -1 7 12 8 5 12 45 2470 107 Heide en duin 0.02206 -8 20 26 50 61 91 256 14988 107 Landbouw 0.86819 -2 6 6 11 8 9 42 2150 107 Moeras en water 0.01666 -1 7 8 6 4 8 34 1539 107 Suburbaan 0.02328 -4 7 7 7 5 3 33 1457 107 Urbaan 0.03655 -2 2 1 3 0 1 9 246 108 Bos 0.02167 -1 7 3 6 3 5 25 1215 108 Heide en duin 0.08583 -1 9 5 12 3 7 37 1841 108 Landbouw 0.87862 -1 1 2 1 1 0 6 213 108 Moeras en water 0.01389 -1 7 5 7 4 5 29 1657 109 Bos 0.24905 -1 9 5 11 11 8 45 2429 109 Heide en duin 0.48985 -2 1 1 0 0 0 4 63 109 Landbouw 0.22588 -1 3 0 1 0 0 5 162 109 Moeras en water 0.03522 -0 18 10 12 7 12 59 3050 110 Bos 0.01822 -1 4 11 7 11 11 45 2449 110 Heide en duin 0.01762 -0 14 18 27 52 208 319 20786 110 Landbouw 0.86376 -2 4 13 6 7 13 45 2308 110 Moeras en water 0.01425 -0 1 4 5 6 30 46 2912 110 Suburbaan 0.02514 -0 0 2 3 2 62 69 4758 110 Urbaan 0.06101 -1 3 2 0 4 1 11 407 111 Bos 0.02649 -0 5 1 6 1 0 13 585 111 Heide en duin 0.04365 -5 12 5 5 6 4 37 1749 111 Landbouw 0.85925 -5 2 3 6 5 5 26 1246 111 Moeras en water 0.07062 -0 7 16 27 37 52 139 8408 112 Bos 0.06284 -1 10 10 10 17 14 62 3347 112 Heide en duin 0.03553 -7 43 51 41 35 25 202 9672 112 Landbouw 0.80062 -0 4 8 11 9 13 45 2475 112 Moeras en water 0.02086 -2 7 3 5 5 7 29 1333 112 Suburbaan 0.0232 -6 14 7 6 4 7 44 1947 112 Urbaan 0.05695 -1 1 2 0 0 0 4 69 113 Bos 0.00614 -2 0 1 0 1 0 4 102 113 Heide en duin 0.00778 -6 21 11 7 3 4 52 1314 113 Landbouw 0.9333 -1 0 3 2 1 0 7 180 113 Moeras en water 0.01259 -1 1 2 0 0 0 4 72 113 Suburbaan 0.01087 -2 1 0 0 2 1 6 167 113 Urbaan 0.02931 -0 1 1 3 15 129 149 9980 114 Bos 0.04264 -0 1 6 9 16 39 71 4552 114 Heide en duin 0.02575 -2 5 16 26 41 241 331 21907 114 Landbouw 0.83042 -1 1 1 10 8 38 59 3787 114 Moeras en water 0.01731 -0 4 4 2 4 40 54 3285 114 Suburbaan 0.02735 -1 2 4 7 10 45 69 4310 114 Urbaan 0.05653 -2 2 2 3 0 0 9 292 115 Bos 0.01654 -4 5 8 14 9 8 48 2384 115 Heide en duin 0.12581 -6 14 14 3 6 3 46 2064 115 Landbouw 0.83394 -0 3 1 2 0 0 6 245 115 Moeras en water 0.01272 -1 1 1 1 0 0 4 102 115 Suburbaan 0.01098 -5 2 0 0 0 0 7 119 116 Bos 0.02588 -3 5 2 1 0 1 12 331 116 Heide en duin 0.0656 -4 9 2 4 0 0 19 604 116 Landbouw 0.71838 -4 9 19 4 4 3 43 1749 116 Moeras en water 0.19014 -0 1 4 5 16 123 149 9887 117 Bos 0.04007 -0 2 1 9 14 56 82 5239 117 Heide en duin 0.02795 -0 0 1 6 21 325 353 24484 117 Landbouw 0.83231 -0 1 1 3 4 53 62 4192 117 Moeras en water 0.0171 -0 1 2 1 1 52 57 3648 117 Suburbaan 0.02713 -0 0 1 0 1 70 72 5028 117 Urbaan 0.05544 -1 18 29 24 26 16 114 5810 118 Bos 0.05565 -3 7 14 12 7 1 44 2072 118 Heide en duin 0.02722 -6 41 50 55 42 14 208 10277 118 Landbouw 0.89015 -4 4 7 5 8 2 30 1339 118 Moeras en water 0.01502 -1 5 2 1 1 0 10 241 118 Suburbaan 0.00778 -1 0 3 0 0 0 4 123 118 Urbaan 0.00419 -7 12 8 2 1 0 30 956 119 Bos 0.01109 -1 10 3 6 6 3 29 1407 119 Heide en duin 0.01359 -4 31 61 71 56 63 286 15273 119 Landbouw 0.92721 -2 6 11 9 4 4 36 1631 119 Moeras en water 0.01365 -1 5 11 7 2 4 30 1227 119 Suburbaan 0.01963 -1 4 3 4 1 1 14 679 119 Urbaan 0.01482 -3 9 4 7 3 3 29 1205 120 Bos 0.00919 -3 9 6 4 4 8 34 1439 120 Heide en duin 0.01366 -10 16 37 63 57 131 314 17929 120 Landbouw 0.87232 -3 8 9 6 4 6 36 1548 120 Moeras en water 0.0117 -0 4 2 5 10 27 48 2958 120 Suburbaan 0.02692 -0 1 0 3 4 65 73 4984 120 Urbaan 0.06622 -3 14 6 10 2 7 42 1195 121 Bos 0.04744 -5 11 8 6 5 1 36 874 121 Heide en duin 0.05154 -11 28 30 12 2 2 85 1972 121 Landbouw 0.84166 -3 9 5 6 5 6 34 1101 121 Moeras en water 0.03937 -2 6 1 1 0 0 10 165 121 Suburbaan 0.01999 -4 6 16 20 18 17 81 4819 122 Bos 0.03185 -1 5 7 8 5 3 29 1664 122 Heide en duin 0.01445 -2 15 36 56 65 85 259 15754 122 Landbouw 0.89275 -1 8 11 8 8 2 38 1725 122 Moeras en water 0.01532 -3 5 10 10 1 1 30 1128 122 Suburbaan 0.02087 -2 7 9 3 1 0 22 752 122 Urbaan 0.02476 -4 0 0 0 0 0 4 31 123 Bos 0.19602 -3 2 4 1 2 0 12 467 123 Moeras en water 0.80398 -2 4 12 14 29 68 129 8138 124 Bos 0.10903 -3 7 7 12 10 11 50 2717 124 Heide en duin 0.05357 -9 28 18 19 15 8 97 4181 124 Landbouw 0.7188 -0 2 4 8 8 6 28 1660 124 Moeras en water 0.02427 -2 4 4 4 6 1 21 909 124 Suburbaan 0.03141 -4 12 4 4 2 0 26 860 124 Urbaan 0.06292 -1 7 8 6 5 4 31 1136 125 Bos 0.1787 -3 11 7 6 6 7 40 1350 125 Heide en duin 0.29227 -3 0 3 3 1 1 11 348 125 Landbouw 0.50539 -1 1 2 0 1 0 5 151 125 Moeras en water 0.02364 -0 8 4 3 0 0 15 629 126 Bos 0.03195 -1 6 0 1 0 0 8 209 126 Heide en duin 0.0189 -2 31 12 2 0 0 47 1614 126 Landbouw 0.87768 -1 4 1 2 1 0 9 323 126 Moeras en water 0.01747 -1 2 1 0 0 0 4 98 126 Suburbaan 0.01131 -2 4 1 0 0 0 7 174 126 Urbaan 0.04269 -5 7 4 0 1 1 18 482 127 Bos 0.00644 -1 8 0 3 3 1 16 820 127 Heide en duin 0.00726 -7 25 31 59 74 112 308 17427 127 Landbouw 0.96619 -3 1 5 7 6 4 26 1303 127 Moeras en water 0.00954 -1 2 2 0 1 2 8 279 127 Suburbaan 0.00443 -4 2 1 0 0 0 7 129 127 Urbaan 0.00615 -2 7 4 9 8 11 41 2440 128 Bos 0.01477 -2 6 3 14 5 8 38 2060 128 Heide en duin 0.01736 -4 19 25 42 56 149 295 18275 128 Landbouw 0.93193 -0 6 4 4 8 18 40 2368 128 Moeras en water 0.01478 -2 2 3 4 5 1 17 720 128 Suburbaan 0.01084 -3 4 3 0 1 0 11 336 128 Urbaan 0.01032 -4 8 10 14 19 17 72 4125 129 Bos 0.44007 -1 9 4 4 1 2 21 1020 129 Heide en duin 0.1627 -0 4 1 1 0 0 6 279 129 Landbouw 0.32152 -3 3 1 0 1 0 8 219 129 Suburbaan 0.07572 -3 0 3 2 0 0 8 278 130 Bos 0.00455 -2 6 5 5 5 7 30 1298 130 Heide en duin 0.02474 -7 19 28 29 35 50 168 9044 130 Landbouw 0.9581 -1 7 2 1 1 1 13 443 130 Moeras en water 0.008 -1 0 1 0 3 0 5 106 130 Suburbaan 0.0046 -3 10 8 8 3 5 37 1621 131 Bos 0.01125 -0 9 8 10 6 6 39 1979 131 Heide en duin 0.01503 -3 18 23 44 64 179 331 20502 131 Landbouw 0.88253 -1 5 10 5 14 9 44 2365 131 Moeras en water 0.01372 -0 2 9 4 10 17 42 2373 131 Suburbaan 0.02261 -0 1 3 9 15 35 63 3948 131 Urbaan 0.05485 -1 1 1 1 1 1 6 200 132 Heide en duin 0.01617 -1 8 7 6 5 6 33 1706 132 Landbouw 0.73799 -1 0 1 1 0 5 8 431 132 Moeras en water 0.02092 -2 0 3 1 3 3 12 653 132 Suburbaan 0.04966 -1 3 3 6 3 8 24 1235 132 Urbaan 0.17527 -4 6 4 2 1 0 17 616 133 Bos 0.0229 -6 6 5 2 0 2 21 659 133 Heide en duin 0.03585 -11 17 21 7 7 6 69 2817 133 Landbouw 0.81476 -2 6 14 11 8 11 52 2576 133 Moeras en water 0.07181 -1 3 5 1 1 0 11 316 133 Suburbaan 0.02384 -5 4 0 0 0 0 9 152 133 Urbaan 0.03085 -2 0 1 1 0 1 5 272 134 Bos 0.00723 -1 2 1 1 1 2 8 410 134 Heide en duin 0.01834 -3 10 11 13 8 16 61 2984 134 Landbouw 0.96702 -1 1 1 1 0 0 4 127 134 Moeras en water 0.00742 -1 5 1 2 0 0 9 421 135 Bos 0.007 -1 11 4 13 4 8 41 1719 135 Heide en duin 0.04041 -4 38 28 24 27 15 136 6498 135 Landbouw 0.92709 -2 3 4 1 3 0 13 494 135 Moeras en water 0.01036 -1 3 5 2 1 0 12 365 135 Suburbaan 0.01514 -0 1 6 13 33 83 136 8920 136 Bos 0.04439 -2 5 8 12 18 25 70 4062 136 Heide en duin 0.02896 -5 28 52 47 67 85 284 15909 136 Landbouw 0.8127 -0 6 7 6 15 23 57 3337 136 Moeras en water 0.01908 -0 7 2 8 14 20 51 2810 136 Suburbaan 0.02946 -0 3 2 12 17 36 70 4318 136 Urbaan 0.06541 -1 11 5 5 0 0 22 507 137 Bos 0.63445 -1 3 3 2 0 1 10 423 137 Heide en duin 0.36555 -0 5 6 4 2 1 18 716 138 Bos 1 -1 7 6 0 1 2 17 902 139 Bos 0.18297 -2 2 2 0 0 0 6 172 139 Heide en duin 0.06821 -1 6 1 0 0 0 8 201 139 Landbouw 0.66053 -0 3 3 1 1 0 8 335 139 Moeras en water 0.08829 -0 4 15 17 21 72 129 7679 140 Bos 0.19773 -3 7 6 5 10 4 35 1874 140 Heide en duin 0.068 -7 14 7 8 4 3 43 1683 140 Landbouw 0.57784 -0 8 2 3 4 4 21 952 140 Moeras en water 0.033 -2 4 3 2 2 5 18 768 140 Suburbaan 0.04883 -6 5 3 2 0 1 17 499 140 Urbaan 0.0746 -0 1 2 3 20 122 148 9776 141 Bos 0.04693 -1 2 3 13 15 39 73 4542 141 Heide en duin 0.02934 -11 23 32 36 60 134 296 17135 141 Landbouw 0.8229 -0 1 5 6 8 38 58 3765 141 Moeras en water 0.01886 -1 2 5 6 9 26 49 2839 141 Suburbaan 0.0275 -1 6 7 9 14 23 60 3362 141 Urbaan 0.05447 -2 3 7 2 0 1 15 347 142 Bos 0.00555 -2 8 6 5 3 2 26 765 142 Heide en duin 0.01219 -3 32 50 50 67 91 293 10909 142 Landbouw 0.94977 -1 4 12 4 2 4 27 767 142 Moeras en water 0.01024 -2 6 4 2 0 0 14 219 142 Suburbaan 0.00851 -2 4 1 4 2 0 13 372 142 Urbaan 0.01376 -1 1 3 2 3 2 12 332 143 Heide en duin 0.6296 -1 3 2 2 0 1 9 277 143 Moeras en water 0.3704 -7 25 26 21 4 9 92 3867 144 Landbouw 0.9962 -1 1 2 0 0 0 4 143 144 Moeras en water 0.0038 -4 2 1 0 0 0 7 157 145 Bos 0.04915 -4 4 2 0 0 0 10 217 145 Heide en duin 0.10383 -1 4 1 0 1 0 7 217 145 Landbouw 0.50269 -3 6 16 7 4 5 41 1790 145 Moeras en water 0.34434 -0 5 5 5 2 1 18 518 146 Bos 0.0083 -1 3 8 11 4 9 36 1097 146 Heide en duin 0.02104 -7 25 52 59 47 40 230 7982 146 Landbouw 0.92991 -0 3 10 7 5 14 39 1453 146 Moeras en water 0.01844 -3 3 1 3 4 2 16 453 146 Suburbaan 0.01306 -2 5 1 0 0 0 8 121 146 Urbaan 0.00924 -2 13 21 16 6 10 68 3635 147 Bos 0.45861 -2 7 9 7 3 2 30 1516 147 Heide en duin 0.25646 -3 1 0 1 0 0 5 158 147 Landbouw 0.23652 -1 1 0 1 2 2 7 441 147 Moeras en water 0.04841 -2 2 2 0 1 0 7 151 148 Bos 0.01 -3 4 2 2 1 3 15 388 148 Heide en duin 0.0317 -4 21 10 1 2 2 40 920 148 Landbouw 0.58477 -3 7 2 2 3 5 22 644 148 Moeras en water 0.03761 -1 4 3 4 3 6 21 759 148 Suburbaan 0.05902 -2 4 3 8 14 27 58 2189 148 Urbaan 0.27689 -2 5 4 2 3 0 16 703 149 Bos 0.01481 -2 6 4 2 2 1 17 633 149 Heide en duin 0.01994 -9 34 24 23 9 14 113 5176 149 Landbouw 0.91685 -4 3 7 9 5 6 34 1715 149 Moeras en water 0.03226 -2 1 1 2 0 0 6 205 149 Suburbaan 0.00819 -2 2 0 0 0 0 4 64 149 Urbaan 0.00795 -0 1 1 1 18 130 151 10055 150 Bos 0.04296 -1 3 4 7 14 53 82 5096 150 Heide en duin 0.02957 -7 17 18 34 53 201 330 20692 150 Landbouw 0.82311 -0 2 1 8 6 42 59 3787 150 Moeras en water 0.01721 -0 1 1 6 8 39 55 3439 150 Suburbaan 0.02769 -0 0 0 4 4 65 73 4981 150 Urbaan 0.05946 -1 7 1 4 2 0 15 467 151 Heide en duin 0.6239 -1 3 3 0 0 0 7 215 151 Moeras en water 0.20187 -1 1 1 1 0 0 4 114 151 Suburbaan 0.17422 -1 2 0 0 1 0 4 232 152 Bos 0.00971 -0 2 0 5 1 1 9 493 152 Heide en duin 0.03694 -1 12 6 0 3 7 29 1153 152 Landbouw 0.82325 -2 1 4 4 5 2 18 865 152 Moeras en water 0.05644 -2 2 1 1 1 2 9 454 152 Suburbaan 0.04584 -1 0 1 2 0 0 4 142 152 Urbaan 0.02781 -0 0 0 4 8 139 151 10145 153 Bos 0.05883 -3 7 6 7 11 41 75 4480 153 Heide en duin 0.03704 -9 33 33 45 47 61 228 12304 153 Landbouw 0.77875 -1 5 3 2 10 32 53 3229 153 Moeras en water 0.02117 -1 5 8 6 6 20 46 2377 153 Suburbaan 0.03172 -3 9 13 10 10 20 65 3338 153 Urbaan 0.0725 -7 39 48 22 33 38 187 9251 154 Landbouw 0.99502 -1 2 2 2 2 0 9 350 154 Moeras en water 0.00498 -1 3 2 0 0 1 7 239 155 Bos 0.0099 -4 0 0 2 0 0 6 145 155 Heide en duin 0.01046 -6 33 13 7 1 2 62 2303 155 Landbouw 0.89695 -1 3 3 3 0 1 11 464 155 Moeras en water 0.01861 -1 1 0 4 3 0 9 428 155 Suburbaan 0.02628 -1 5 0 2 0 1 9 405 155 Urbaan 0.03779 -1 4 7 8 3 10 33 1709 156 Landbouw 0.94395 -1 1 2 1 0 2 7 281 156 Urbaan 0.05605 -1 3 0 1 1 0 6 260 157 Bos 0.00361 -1 4 2 2 2 0 11 546 157 Heide en duin 0.00915 -6 24 25 30 30 34 149 7982 157 Landbouw 0.9433 -0 1 11 6 2 4 24 1196 157 Moeras en water 0.01777 -3 4 1 3 2 2 15 556 157 Suburbaan 0.01789 -1 2 1 1 0 0 5 179 157 Urbaan 0.00827 -1 2 2 0 0 0 5 102 158 Heide en duin 0.01411 -1 21 10 3 1 0 36 864 158 Landbouw 0.87805 -2 7 3 7 3 2 24 718 158 Moeras en water 0.06846 -4 3 0 0 0 1 8 165 158 Suburbaan 0.03939 -0 0 3 4 14 130 151 10004 159 Bos 0.04088 -0 1 4 5 15 54 79 5135 159 Heide en duin 0.02711 -0 6 7 15 25 297 350 23532 159 Landbouw 0.83065 -0 1 3 4 11 44 63 4065 159 Moeras en water 0.01749 -0 1 1 3 4 48 57 3610 159 Suburbaan 0.02731 -0 0 0 1 2 70 73 5062 159 Urbaan 0.05658 -0 7 10 14 26 84 141 8912 160 Bos 0.04691 -0 4 11 7 15 26 63 3907 160 Heide en duin 0.02657 -4 20 43 53 58 107 285 16523 160 Landbouw 0.83132 -3 2 8 4 14 22 53 3153 160 Moeras en water 0.01808 -1 5 1 9 11 15 42 2425 160 Suburbaan 0.02473 -2 4 9 11 11 18 55 3040 160 Urbaan 0.05239 -2 9 10 8 6 6 41 2135 161 Bos 0.01287 -3 7 5 4 5 13 37 1704 161 Heide en duin 0.01472 -2 23 26 39 72 150 312 18998 161 Landbouw 0.85838 -0 6 6 8 12 23 55 3036 161 Moeras en water 0.0177 -0 1 3 8 12 33 57 3448 161 Suburbaan 0.03166 -0 1 2 2 8 59 72 4902 161 Urbaan 0.06469 -0 8 4 3 9 8 32 1259 162 Bos 0.02959 -1 15 7 8 9 4 44 1347 162 Heide en duin 0.05157 -3 19 29 28 18 11 108 3672 162 Landbouw 0.87557 -0 7 9 4 9 8 37 1321 162 Moeras en water 0.03508 -1 3 1 0 1 0 6 119 162 Suburbaan 0.00818 -1 3 2 2 0 0 8 291 163 Heide en duin 0.04066 -2 10 2 2 3 1 20 706 163 Landbouw 0.80346 -2 5 6 5 6 4 28 1399 163 Moeras en water 0.13155 -2 0 1 0 1 0 4 141 163 Suburbaan 0.02433 -6 6 3 8 5 10 38 2121 164 Landbouw 0.96725 -1 2 5 2 0 1 11 419 164 Moeras en water 0.03275 -4 3 1 0 1 0 9 159 165 Heide en duin 0.03272 -11 14 9 4 0 0 38 739 165 Landbouw 0.95552 -3 1 1 0 0 0 5 74 165 Moeras en water 0.01176 -3 3 2 0 1 0 9 279 166 Heide en duin 0.02 -10 14 10 8 8 4 54 2318 166 Landbouw 0.93369 -0 7 0 5 3 1 16 757 166 Moeras en water 0.03235 -1 1 0 2 1 0 5 135 166 Suburbaan 0.01396 -1 15 11 16 8 4 55 2796 167 Bos 0.06763 -1 8 13 6 4 3 35 1685 167 Heide en duin 0.05455 -2 39 17 9 7 6 80 3316 167 Landbouw 0.86246 -0 3 2 1 1 0 7 303 167 Moeras en water 0.00883 -2 1 1 0 0 0 4 55 167 Suburbaan 0.00653 -0 4 3 4 1 2 14 681 168 Landbouw 0.86989 -3 1 1 2 1 3 11 518 168 Moeras en water 0.07994 -3 0 1 0 0 1 5 174 168 Suburbaan 0.05017 -7 2 2 2 2 0 15 554 169 Bos 0.01063 -1 6 6 0 1 0 14 465 169 Heide en duin 0.01168 -20 44 30 21 15 15 145 5969 169 Landbouw 0.90107 -3 8 7 4 5 4 31 1434 169 Moeras en water 0.02253 -1 6 3 4 0 0 14 483 169 Suburbaan 0.01756 -9 6 3 0 0 0 18 406 169 Urbaan 0.03653 -2 3 0 2 0 0 7 219 170 Heide en duin 0.13469 -1 2 1 0 0 0 4 84 170 Landbouw 0.46581 -5 7 2 2 5 1 22 905 170 Moeras en water 0.3995 -1 2 2 1 0 1 7 148 171 Heide en duin 0.0754 -2 3 2 1 0 2 10 420 171 Landbouw 0.78228 -2 4 2 2 2 2 14 596 171 Moeras en water 0.14232 -8 8 7 8 8 2 41 1898 172 Bos 0.01512 -4 9 16 2 11 6 48 2243 172 Heide en duin 0.02244 -8 29 54 43 56 87 277 15542 172 Landbouw 0.89579 -1 3 9 9 10 25 57 3264 172 Moeras en water 0.02156 -1 5 8 8 2 3 27 1152 172 Suburbaan 0.01763 -4 11 4 4 3 0 26 928 172 Urbaan 0.02746 -3 10 9 6 8 5 41 1672 173 Heide en duin 0.04636 -5 31 30 21 18 15 120 5692 173 Landbouw 0.93843 -2 1 3 1 0 1 8 303 173 Moeras en water 0.00732 -1 0 4 0 0 1 6 108 173 Suburbaan 0.00789 -3 6 1 4 6 7 27 1430 174 Bos 0.38116 -1 0 1 3 1 0 6 315 174 Landbouw 0.61884 -0 3 3 11 20 111 148 9723 175 Bos 0.04429 -0 3 4 8 12 53 80 5097 175 Heide en duin 0.03035 -6 21 32 28 50 176 313 19153 175 Landbouw 0.82118 -1 0 2 6 5 47 61 4004 175 Moeras en water 0.01872 -0 0 3 5 15 30 53 3263 175 Suburbaan 0.02807 -1 7 5 17 17 20 67 3677 175 Urbaan 0.0574 -1 2 0 1 1 0 5 177 176 Heide en duin 0.03485 -4 3 4 1 0 0 12 322 176 Landbouw 0.66281 -3 10 14 4 4 1 36 1388 176 Moeras en water 0.25369 -1 1 3 0 0 0 5 152 176 Suburbaan 0.04865 -2 2 3 1 0 0 8 252 177 Bos 0.00421 -1 3 3 1 0 1 9 365 177 Heide en duin 0.00609 -18 46 33 35 28 11 171 7415 177 Landbouw 0.90083 -1 7 3 1 0 4 16 667 177 Moeras en water 0.00986 -0 5 5 4 8 7 29 1603 177 Suburbaan 0.03084 -3 7 7 6 4 1 28 1182 177 Urbaan 0.04817 -3 17 10 18 8 4 60 2819 178 Bos 0.01892 -0 6 11 7 9 9 42 2101 178 Heide en duin 0.01678 -3 24 29 51 79 124 310 18128 178 Landbouw 0.85683 -1 7 12 5 13 11 49 2534 178 Moeras en water 0.01584 -1 2 2 3 8 35 51 3271 178 Suburbaan 0.02846 -0 1 3 7 15 44 70 4508 178 Urbaan 0.06318 -0 1 5 10 25 105 146 9580 179 Bos 0.06345 -2 7 7 8 12 32 68 4184 179 Heide en duin 0.03746 -12 43 34 41 40 43 213 10649 179 Landbouw 0.81162 -1 6 11 4 7 18 47 2647 179 Moeras en water 0.02095 -0 5 4 4 3 9 25 1331 179 Suburbaan 0.01923 -3 13 6 4 4 8 38 1793 179 Urbaan 0.04728 -1 0 0 2 0 1 4 221 180 Heide en duin 0.02106 -0 2 2 3 4 6 17 983 180 Landbouw 0.82524 -1 0 2 1 1 4 9 484 180 Moeras en water 0.0511 -1 0 2 0 0 2 5 291 180 Suburbaan 0.0392 -1 0 2 0 1 1 5 219 180 Urbaan 0.06341 -1 2 2 1 1 0 7 271 181 Bos 0.00429 -0 9 5 3 0 0 17 629 181 Heide en duin 0.01539 -2 48 51 28 15 10 154 6729 181 Landbouw 0.96435 -1 4 2 1 0 1 9 341 181 Moeras en water 0.00586 -2 2 4 1 0 0 9 289 181 Suburbaan 0.01011 -1 2 0 2 1 1 7 213 182 Bos 0.04386 -3 0 2 1 1 0 7 152 182 Heide en duin 0.05559 -1 2 2 3 3 2 13 428 182 Landbouw 0.8331 -0 4 2 2 0 1 9 276 182 Moeras en water 0.06745 -4 6 15 15 23 25 88 5145 183 Bos 0.3937 -2 10 5 12 7 6 42 2255 183 Heide en duin 0.23818 -2 0 3 1 0 0 6 298 183 Landbouw 0.19612 -0 2 4 2 0 0 8 305 183 Moeras en water 0.0367 -2 4 2 1 0 1 10 318 183 Suburbaan 0.07126 -1 3 1 1 0 0 6 210 183 Urbaan 0.06404 -6 10 15 5 1 1 38 1395 184 Bos 0.01676 -1 8 5 4 0 1 19 747 184 Heide en duin 0.01062 -5 48 47 39 47 36 222 10811 184 Landbouw 0.85828 -1 4 11 5 3 2 26 1165 184 Moeras en water 0.01176 -1 1 5 4 9 16 36 2313 184 Suburbaan 0.0281 -1 4 7 15 10 22 59 3304 184 Urbaan 0.07449 -2 8 11 2 2 2 27 1268 185 Landbouw 0.97467 -1 0 2 3 1 0 7 270 185 Moeras en water 0.02533 -1 1 2 1 1 1 7 220 186 Bos 0.05426 -2 2 0 2 2 0 8 278 186 Landbouw 0.55496 -2 2 1 0 1 1 7 214 186 Moeras en water 0.05563 -1 0 1 3 0 4 9 571 186 Suburbaan 0.12803 -1 1 1 3 1 1 8 395 186 Urbaan 0.20711 -4 11 13 19 24 26 97 5397 187 Bos 0.17577 -5 6 9 5 4 1 30 1345 187 Heide en duin 0.06891 -14 13 7 0 3 0 37 1137 187 Landbouw 0.58782 -5 2 4 0 0 3 14 469 187 Moeras en water 0.02601 -0 3 2 4 5 1 15 749 187 Suburbaan 0.04811 -4 4 3 3 3 1 18 869 187 Urbaan 0.09338 -4 5 1 0 2 0 12 317 188 Bos 0.00399 -0 4 6 4 3 2 19 772 188 Heide en duin 0.00801 -5 33 47 68 62 90 305 15936 188 Landbouw 0.88948 -1 8 8 1 4 3 25 951 188 Moeras en water 0.00853 -1 1 6 9 7 14 38 2134 188 Suburbaan 0.02237 -0 1 3 13 12 42 71 4418 188 Urbaan 0.06762 -4 4 0 1 0 0 9 273 189 Bos 0.0933 -3 3 4 0 2 1 13 453 189 Heide en duin 0.19218 -3 3 0 0 0 0 6 110 189 Landbouw 0.51124 -5 4 2 3 2 1 17 623 189 Moeras en water 0.20329 -1 3 5 0 2 2 13 650 190 Bos 0.01687 -3 11 1 1 1 0 17 517 190 Heide en duin 0.02796 -6 23 17 23 7 4 80 3476 190 Landbouw 0.91006 -0 5 3 6 7 6 27 1439 190 Moeras en water 0.03592 -2 2 1 0 0 0 5 167 190 Suburbaan 0.00919 -1 13 10 10 17 16 67 3670 191 Bos 0.15673 -0 5 7 11 21 33 77 4431 191 Heide en duin 0.22832 -1 8 7 4 2 2 24 1054 191 Landbouw 0.49222 -1 2 3 6 5 10 27 1737 191 Moeras en water 0.06476 -0 4 4 2 0 4 14 410 191 Suburbaan 0.05796 -0 0 0 0 5 145 150 10166 192 Bos 0.04401 -2 1 2 3 2 63 73 4906 192 Heide en duin 0.02715 -1 7 21 18 35 241 323 21435 192 Landbouw 0.8308 -0 6 1 5 5 39 56 3538 192 Moeras en water 0.01685 -0 1 5 6 7 29 48 2768 192 Suburbaan 0.02492 -1 5 7 8 7 39 67 3966 192 Urbaan 0.05628 +0 1 1 2 18 138 160 11832 1 Bos 0.04616 +0 1 4 12 14 49 80 5571 1 Heide en duin 0.02925 +8 21 36 37 65 165 332 21605 1 Landbouw 0.82739 +0 0 3 8 7 43 61 4469 1 Moeras en water 0.01823 +1 1 6 7 10 28 53 3279 1 Suburbaan 0.02513 +2 6 7 9 16 26 66 4078 1 Urbaan 0.05384 +1 1 1 1 0 0 4 177 2 Bos 0.0043 +2 1 2 4 1 1 11 623 2 Heide en duin 0.01815 +8 17 14 7 15 13 74 3885 2 Landbouw 0.91568 +4 7 7 3 6 2 29 1262 2 Moeras en water 0.04304 +1 3 1 2 2 0 9 327 2 Suburbaan 0.01883 +12 11 8 8 8 2 49 2216 3 Bos 0.01655 +4 11 18 3 10 7 53 2672 3 Heide en duin 0.02269 +9 31 47 61 58 100 306 18780 3 Landbouw 0.89281 +1 4 10 12 9 26 62 3894 3 Moeras en water 0.0217 +1 6 9 9 2 3 30 1324 3 Suburbaan 0.01665 +6 8 8 5 4 0 31 1231 3 Urbaan 0.02961 +0 1 3 5 14 137 160 11867 4 Bos 0.04009 +0 1 2 7 16 62 88 6223 4 Heide en duin 0.02795 +0 0 2 5 15 364 386 29231 4 Landbouw 0.83555 +0 2 1 4 4 55 66 4871 4 Moeras en water 0.01714 +0 1 2 1 1 55 60 4065 4 Suburbaan 0.02471 +0 0 1 0 1 75 77 5856 4 Urbaan 0.05456 +2 2 1 0 0 0 5 166 5 Bos 0.04753 +3 4 1 0 0 0 8 137 5 Landbouw 0.71855 +3 10 4 1 0 1 19 701 5 Moeras en water 0.23392 +1 2 0 2 0 0 5 194 6 Bos 0.02358 +2 2 0 1 1 0 6 198 6 Heide en duin 0.03736 +4 5 2 3 0 0 14 438 6 Landbouw 0.66208 +4 10 12 6 5 2 39 1787 6 Moeras en water 0.23822 +1 1 3 0 0 0 5 168 6 Suburbaan 0.03875 +6 7 5 0 1 1 20 579 7 Bos 0.00674 +1 7 0 4 4 2 18 1061 7 Heide en duin 0.00768 +6 20 38 59 80 129 332 20846 7 Landbouw 0.96587 +3 2 4 9 6 5 29 1622 7 Moeras en water 0.01012 +1 2 2 0 1 2 8 320 7 Suburbaan 0.00387 +4 2 1 0 0 0 7 136 7 Urbaan 0.00571 +3 2 4 0 0 1 10 375 8 Bos 0.0045 +1 4 5 2 0 1 13 509 8 Heide en duin 0.0076 +16 47 42 52 34 19 210 10368 8 Landbouw 0.90697 +1 7 4 1 0 4 17 785 8 Moeras en water 0.00881 +0 5 5 6 7 8 31 1811 8 Suburbaan 0.02547 +4 8 9 6 4 2 33 1526 8 Urbaan 0.04666 +5 11 15 22 26 33 112 6751 9 Bos 0.18566 +3 11 8 7 6 2 37 1911 9 Heide en duin 0.07773 +15 15 7 0 2 1 40 1384 9 Landbouw 0.5728 +5 5 4 0 0 3 17 606 9 Moeras en water 0.0292 +0 3 2 4 5 1 15 791 9 Suburbaan 0.04086 +4 4 5 3 3 1 20 994 9 Urbaan 0.09376 +2 4 12 11 24 95 148 10396 10 Bos 0.10451 +3 5 7 10 14 15 54 3377 10 Heide en duin 0.04833 +10 35 22 23 19 12 121 5816 10 Landbouw 0.73811 +1 4 5 8 9 8 35 2100 10 Moeras en water 0.02561 +2 5 3 4 7 1 22 1018 10 Suburbaan 0.02553 +6 11 5 3 4 0 29 1072 10 Urbaan 0.05791 +2 3 6 0 2 2 15 742 11 Bos 0.01565 +4 10 3 2 1 0 20 723 11 Heide en duin 0.02833 +6 25 23 24 11 4 93 4602 11 Landbouw 0.89798 +0 7 6 5 10 6 34 1887 11 Moeras en water 0.03937 +2 2 2 0 0 0 6 214 11 Suburbaan 0.00918 +1 2 1 0 0 0 4 96 11 Urbaan 0.00948 +2 8 6 9 7 12 44 2861 12 Bos 0.01441 +1 6 4 14 5 12 42 2566 12 Heide en duin 0.01743 +3 15 23 51 59 179 330 22693 12 Landbouw 0.93364 +0 7 5 4 7 19 42 2802 12 Moeras en water 0.01425 +3 2 3 4 5 1 18 750 12 Suburbaan 0.00915 +2 4 5 0 1 0 12 438 12 Urbaan 0.01111 +2 4 6 2 1 1 16 416 13 Bos 0.00524 +1 10 7 4 5 2 29 930 13 Heide en duin 0.01203 +2 30 56 54 77 117 336 13706 13 Landbouw 0.9504 +1 5 11 7 2 5 31 1022 13 Moeras en water 0.01052 +2 6 3 3 0 0 14 246 13 Suburbaan 0.007 +3 4 2 4 2 1 16 449 13 Urbaan 0.01482 +0 0 2 1 14 144 161 11995 14 Bos 0.04229 +0 1 9 9 15 47 81 5549 14 Heide en duin 0.02696 +4 6 16 20 41 281 368 26605 14 Landbouw 0.83501 +1 1 1 8 8 42 61 4435 14 Moeras en water 0.0166 +0 2 5 2 5 42 56 3679 14 Suburbaan 0.02417 +1 2 4 6 10 51 74 5116 14 Urbaan 0.05497 +3 2 1 2 0 0 8 269 15 Heide en duin 0.12796 +1 3 1 0 0 0 5 110 15 Landbouw 0.49842 +6 6 4 1 5 3 25 1157 15 Moeras en water 0.37362 +5 8 4 12 4 13 46 2642 16 Landbouw 0.96966 +2 2 4 2 1 1 12 536 16 Moeras en water 0.03034 +0 4 1 2 3 2 12 621 17 Bos 0.02914 +1 3 4 2 4 1 15 816 17 Heide en duin 0.04617 +5 11 7 4 4 9 40 2267 17 Landbouw 0.83914 +4 4 6 5 8 7 34 1852 17 Moeras en water 0.08555 +1 0 0 2 0 1 4 252 18 Heide en duin 0.01851 +0 2 3 2 7 6 20 1329 18 Landbouw 0.84104 +1 0 2 1 1 4 9 565 18 Moeras en water 0.04539 +1 0 2 0 0 3 6 432 18 Suburbaan 0.04 +1 0 2 0 1 1 5 261 18 Urbaan 0.05506 +4 7 6 1 1 1 20 873 19 Bos 0.02175 +6 6 5 5 1 2 25 896 19 Heide en duin 0.03446 +12 22 26 11 12 6 89 3844 19 Landbouw 0.83625 +2 7 14 10 7 15 55 3107 19 Moeras en water 0.06198 +1 3 5 1 1 0 11 354 19 Suburbaan 0.01787 +5 4 1 0 0 0 10 185 19 Urbaan 0.02768 +3 6 2 3 6 11 31 1976 20 Bos 0.36072 +0 1 1 2 2 0 6 395 20 Landbouw 0.60311 +1 2 1 0 0 0 4 99 20 Moeras en water 0.03617 +1 6 8 6 1 5 27 1428 21 Bos 1 +9 26 28 22 5 10 100 4639 22 Landbouw 0.99641 +1 1 2 0 0 0 4 176 22 Moeras en water 0.00359 +2 21 16 18 14 4 75 3729 23 Bos 0.02186 +1 7 13 7 11 12 51 2849 23 Heide en duin 0.01884 +3 18 32 52 86 149 340 22368 23 Landbouw 0.8562 +1 5 11 6 15 13 51 3092 23 Moeras en water 0.0154 +1 1 2 4 9 37 54 3685 23 Suburbaan 0.02587 +0 1 4 5 16 49 75 5308 23 Urbaan 0.06183 +2 11 11 7 6 7 44 2495 24 Bos 0.01265 +2 10 6 5 5 17 45 2361 24 Heide en duin 0.0164 +1 18 31 43 81 174 348 23329 24 Landbouw 0.86456 +0 5 7 8 13 23 56 3531 24 Moeras en water 0.01669 +0 0 3 5 13 38 59 3866 24 Suburbaan 0.02788 +0 1 3 2 5 65 76 5680 24 Urbaan 0.06181 +1 3 0 0 1 0 5 266 25 Bos 0.0116 +0 2 0 5 1 2 10 624 25 Heide en duin 0.03677 +1 13 8 0 4 7 33 1546 25 Landbouw 0.827 +1 1 4 4 5 4 19 1142 25 Moeras en water 0.05711 +2 2 2 1 1 2 10 524 25 Suburbaan 0.04291 +1 0 1 1 1 0 4 200 25 Urbaan 0.02461 +1 1 2 0 0 2 6 385 26 Bos 0.01045 +0 1 1 1 3 2 8 533 26 Heide en duin 0.01766 +1 12 12 11 11 17 64 3542 26 Landbouw 0.96287 +2 1 1 1 0 0 5 165 26 Moeras en water 0.00902 +0 0 0 2 8 153 163 12151 27 Bos 0.05884 +2 5 8 6 13 48 82 5394 27 Heide en duin 0.03752 +5 24 35 45 54 88 251 16042 27 Landbouw 0.78275 +1 3 3 4 9 34 54 3756 27 Moeras en water 0.0202 +1 5 5 7 7 26 51 2969 27 Suburbaan 0.03026 +2 6 10 8 16 27 69 4337 27 Urbaan 0.07044 +2 3 2 3 1 1 12 501 28 Bos 0.0172 +4 11 10 12 11 9 57 2959 28 Heide en duin 0.11293 +8 22 17 5 7 4 63 2917 28 Landbouw 0.85082 +0 3 1 3 0 0 7 310 28 Moeras en water 0.01134 +1 1 1 1 0 0 4 115 28 Suburbaan 0.00771 +1 2 0 0 1 0 4 185 29 Heide en duin 0.03275 +0 5 4 1 2 1 13 684 29 Landbouw 0.96725 +1 4 0 1 1 0 7 311 30 Bos 0.00397 +1 4 2 2 2 1 12 676 30 Heide en duin 0.00923 +6 24 28 36 31 40 165 9728 30 Landbouw 0.94378 +0 2 12 5 3 6 28 1532 30 Moeras en water 0.01921 +3 4 1 4 2 2 16 619 30 Suburbaan 0.01632 +1 2 1 1 0 0 5 186 30 Urbaan 0.00749 +4 1 0 0 0 0 5 67 31 Bos 0.21615 +4 3 4 1 1 1 14 614 31 Moeras en water 0.78385 +2 8 4 3 3 0 20 937 32 Bos 0.01461 +2 7 6 2 3 2 22 1051 32 Heide en duin 0.02037 +9 44 29 34 13 17 146 7218 32 Landbouw 0.92125 +3 4 8 10 7 7 39 2197 32 Moeras en water 0.02951 +2 1 1 2 0 0 6 226 32 Suburbaan 0.006 +3 2 0 0 0 0 5 87 32 Urbaan 0.00826 +3 3 1 0 0 0 7 198 33 Bos 0.02478 +4 6 2 1 0 1 14 428 33 Heide en duin 0.07329 +4 10 2 2 2 0 20 774 33 Landbouw 0.71363 +4 9 17 5 6 3 44 2068 33 Moeras en water 0.1883 +4 9 11 14 21 19 78 4833 34 Bos 0.44933 +1 9 4 5 2 2 23 1327 34 Heide en duin 0.16792 +0 4 1 1 0 0 6 291 34 Landbouw 0.29858 +1 2 0 1 0 0 4 117 34 Moeras en water 0.01791 +3 3 1 0 1 0 8 226 34 Suburbaan 0.06627 +1 18 15 20 8 5 67 3644 35 Bos 0.07937 +1 7 10 11 4 4 37 2060 35 Heide en duin 0.05555 +2 40 18 9 6 8 83 3794 35 Landbouw 0.84942 +0 3 3 1 1 0 8 348 35 Moeras en water 0.00982 +2 1 1 0 0 0 4 57 35 Suburbaan 0.00584 +3 4 1 0 0 0 8 253 36 Bos 0.05241 +4 3 3 0 0 0 10 263 36 Heide en duin 0.08302 +1 5 3 0 1 0 10 301 36 Landbouw 0.56592 +5 5 14 10 5 5 44 2111 36 Moeras en water 0.29865 +2 1 0 1 0 0 4 154 37 Bos 0.00734 +3 4 6 4 2 3 22 1008 37 Heide en duin 0.06826 +5 7 13 6 4 5 40 1975 37 Landbouw 0.84594 +1 4 3 9 3 3 23 1317 37 Moeras en water 0.05834 +1 0 1 1 1 2 6 225 37 Suburbaan 0.02012 +3 0 1 0 0 0 4 87 38 Heide en duin 0.09486 +2 2 0 0 0 0 4 93 38 Landbouw 0.64662 +1 3 3 1 2 0 10 469 38 Moeras en water 0.25851 +3 14 10 7 5 6 45 2072 39 Bos 0.01253 +1 9 11 11 4 8 44 2365 39 Heide en duin 0.01553 +4 13 24 44 69 214 368 25086 39 Landbouw 0.8852 +1 6 10 5 15 11 48 2899 39 Moeras en water 0.01385 +0 2 9 4 10 19 44 2673 39 Suburbaan 0.02013 +0 1 3 8 17 38 67 4617 39 Urbaan 0.05276 +0 7 6 6 10 9 38 1644 40 Bos 0.03009 +1 12 13 9 9 7 51 1843 40 Heide en duin 0.05118 +2 19 31 34 28 14 128 4779 40 Landbouw 0.87551 +1 4 7 3 8 17 40 1747 40 Moeras en water 0.03281 +0 3 3 1 1 0 8 197 40 Suburbaan 0.01041 +2 8 7 9 4 3 33 1800 41 Landbouw 0.97519 +1 1 1 4 0 1 8 370 41 Moeras en water 0.02481 +2 5 12 4 3 1 27 1092 42 Bos 0.727 +1 4 2 1 0 0 8 309 42 Heide en duin 0.273 +5 8 15 17 22 31 98 6206 43 Bos 0.4118 +1 11 8 12 7 8 47 2757 43 Heide en duin 0.2503 +2 0 3 1 0 0 6 314 43 Landbouw 0.1815 +0 2 4 1 1 0 8 362 43 Moeras en water 0.03483 +2 4 2 1 0 1 10 322 43 Suburbaan 0.06215 +1 3 1 1 0 0 6 223 43 Urbaan 0.05942 +3 5 2 1 2 0 13 466 44 Bos 0.00393 +0 5 6 5 5 3 24 1062 44 Heide en duin 0.0092 +6 32 52 68 73 111 342 19704 44 Landbouw 0.89381 +2 8 8 1 3 4 26 1100 44 Moeras en water 0.00815 +1 1 6 9 6 17 40 2453 44 Suburbaan 0.01989 +0 2 2 13 13 46 76 5162 44 Urbaan 0.06502 +4 18 10 7 11 17 67 3749 45 Bos 0.26275 +3 3 6 7 11 30 60 4070 45 Heide en duin 0.29821 +4 2 2 3 1 1 13 566 45 Landbouw 0.40653 +0 2 2 2 0 2 8 437 45 Moeras en water 0.03251 +3 4 3 1 0 0 11 362 46 Bos 0.11137 +2 5 1 2 0 0 10 373 46 Heide en duin 0.11548 +2 6 1 0 0 0 9 239 46 Landbouw 0.69971 +2 3 1 2 0 0 8 316 46 Moeras en water 0.07343 +2 18 11 12 14 20 77 4451 47 Bos 0.16893 +0 4 8 12 19 36 79 5146 47 Heide en duin 0.21965 +1 8 9 4 2 2 26 1285 47 Landbouw 0.49275 +2 2 4 6 5 11 30 2007 47 Moeras en water 0.06819 +0 4 4 2 0 4 14 425 47 Suburbaan 0.05047 +1 5 3 7 5 1 22 760 48 Bos 0.00906 +2 3 10 10 5 10 40 1374 48 Heide en duin 0.02087 +6 24 53 70 58 51 262 10160 48 Landbouw 0.9319 +1 5 8 8 6 17 45 1847 48 Moeras en water 0.0192 +3 3 1 3 4 2 16 496 48 Suburbaan 0.01083 +2 5 1 0 0 0 8 137 48 Urbaan 0.00815 +0 1 5 8 21 124 159 11650 49 Bos 0.06196 +3 6 7 7 16 37 76 5059 49 Heide en duin 0.03753 +9 37 41 55 48 52 242 13831 49 Landbouw 0.81465 +2 6 10 5 7 22 52 3234 49 Moeras en water 0.021 +0 6 4 6 4 9 29 1536 49 Suburbaan 0.01857 +1 16 6 6 4 9 42 2199 49 Urbaan 0.04628 +1 6 8 8 4 5 32 1979 50 Bos 0.24097 +1 10 3 11 10 14 49 3092 50 Heide en duin 0.46763 +2 1 2 0 0 0 5 98 50 Landbouw 0.2602 +1 2 0 2 0 0 5 226 50 Moeras en water 0.03121 +3 7 11 7 8 5 41 1979 51 Heide en duin 0.04299 +7 32 29 28 21 15 132 6903 51 Landbouw 0.94336 +2 1 3 1 0 1 8 348 51 Moeras en water 0.00686 +1 0 4 0 0 1 6 110 51 Suburbaan 0.0068 +3 6 5 6 5 6 31 1731 52 Landbouw 0.98853 +2 1 0 0 0 1 4 128 52 Moeras en water 0.01147 +7 6 17 20 17 25 92 6072 53 Bos 0.03198 +1 6 10 5 9 3 34 2070 53 Heide en duin 0.01498 +2 16 40 66 69 106 299 19630 53 Landbouw 0.89795 +0 9 12 5 12 3 41 2184 53 Moeras en water 0.01477 +3 4 11 11 1 1 31 1249 53 Suburbaan 0.01771 +3 6 8 5 1 0 23 926 53 Urbaan 0.02261 +7 11 15 7 1 1 42 1799 54 Bos 0.01705 +3 11 8 3 2 2 29 1210 54 Heide en duin 0.01492 +5 48 48 50 52 42 245 13338 54 Landbouw 0.85908 +1 5 11 6 3 2 28 1408 54 Moeras en water 0.01178 +0 3 7 3 8 18 39 2596 54 Suburbaan 0.02601 +1 3 10 16 10 22 62 3852 54 Urbaan 0.07117 +2 6 8 7 9 12 44 2447 55 Landbouw 0.94382 +1 2 3 0 1 2 9 417 55 Urbaan 0.05618 +0 19 14 14 6 14 67 3837 56 Bos 0.01909 +1 5 10 11 12 16 55 3222 56 Heide en duin 0.01986 +0 11 14 26 48 252 351 25425 56 Landbouw 0.864 +2 3 13 7 11 13 49 2851 56 Moeras en water 0.01447 +0 1 3 5 6 34 49 3352 56 Suburbaan 0.02295 +0 0 2 2 3 67 74 5577 56 Urbaan 0.05963 +3 10 5 7 2 4 31 1457 57 Bos 0.00915 +3 9 7 5 6 9 39 1797 57 Heide en duin 0.01459 +8 20 38 62 62 153 343 21729 57 Landbouw 0.87492 +4 7 11 6 5 6 39 1906 57 Moeras en water 0.01193 +0 4 2 5 11 28 50 3312 57 Suburbaan 0.02426 +0 1 2 3 2 70 78 5780 57 Urbaan 0.06513 +0 2 5 13 24 112 156 11289 58 Bos 0.04012 +0 4 3 7 9 63 86 6108 58 Heide en duin 0.02803 +0 3 2 10 21 341 377 28501 58 Landbouw 0.83748 +0 0 0 2 11 50 63 4773 58 Moeras en water 0.01679 +0 1 3 3 8 43 58 3867 58 Suburbaan 0.02451 +0 2 2 7 11 51 73 5323 58 Urbaan 0.05309 +1 4 1 0 3 1 10 381 59 Bos 0.0594 +1 3 2 2 3 1 12 570 59 Landbouw 0.62715 +2 3 1 0 1 1 8 263 59 Moeras en water 0.04787 +1 0 2 3 0 4 10 673 59 Suburbaan 0.09761 +0 2 0 3 2 2 9 590 59 Urbaan 0.16798 +5 5 9 3 3 0 25 1115 60 Bos 0.02083 +0 11 7 4 5 6 33 1791 60 Heide en duin 0.03485 +5 24 26 27 21 17 120 6871 60 Landbouw 0.86375 +1 5 16 6 6 13 47 2759 60 Moeras en water 0.04058 +0 5 3 2 1 1 12 593 60 Suburbaan 0.01643 +4 5 2 0 0 0 11 274 60 Urbaan 0.02356 +2 13 6 5 2 0 28 730 61 Bos 0.66761 +0 3 3 3 1 1 11 565 61 Heide en duin 0.33239 +2 4 1 1 1 0 9 234 62 Bos 0.01128 +2 4 2 2 3 3 16 480 62 Heide en duin 0.02859 +5 26 11 4 3 2 51 1353 62 Landbouw 0.62118 +3 7 2 2 3 5 22 745 62 Moeras en water 0.03214 +1 5 4 6 2 7 25 916 62 Suburbaan 0.05561 +2 5 3 8 11 34 63 2646 62 Urbaan 0.2512 +0 1 1 1 11 149 163 12089 63 Bos 0.0429 +1 3 5 5 14 60 88 6019 63 Heide en duin 0.02936 +7 12 17 36 53 239 364 25387 63 Landbouw 0.82768 +0 3 2 5 9 43 62 4436 63 Moeras en water 0.01691 +0 1 0 7 9 41 58 3859 63 Suburbaan 0.02509 +0 0 0 5 4 69 78 5785 63 Urbaan 0.05806 +0 0 0 0 4 158 162 12149 64 Bos 0.04323 +2 1 4 4 2 66 79 5658 64 Heide en duin 0.02671 +1 3 16 21 45 278 364 26476 64 Landbouw 0.83901 +0 6 2 4 3 44 59 4113 64 Moeras en water 0.01631 +0 1 5 5 8 31 50 3055 64 Suburbaan 0.02192 +1 3 7 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0.57539 +5 4 2 3 0 3 17 754 105 Moeras en water 0.1676 +0 1 4 3 3 3 14 477 106 Heide en duin 0.68159 +1 3 2 2 0 1 9 293 106 Moeras en water 0.31841 diff --git a/inst/results/stratum_weight.yml b/inst/results/stratum_weight.yml index 58ce8a6..ea5ec38 100644 --- a/inst/results/stratum_weight.yml +++ b/inst/results/stratum_weight.yml @@ -1,12 +1,13 @@ ..generic: - git2rdata: 0.4.0 + git2rdata: 0.5.2 optimize: yes NA string: NA sorting: - analysis - stratum - hash: 557dd7ba5967f8055f2fc33a70603e5f9258be06 - data_hash: f7414ccbbb0bb754803f5184b1e3a51d5da1db23 + hash: 3dda32679e99ab6fcae64b364927a4494c8dc820 + data_hash: e5ab72693220b8af6d2ba7bd4a26ffa0151c7bf6 + name: stratum_weight points_1: class: integer points_2: @@ -26,103 +27,209 @@ visits: analysis: class: factor labels: - - 02650104e02b12a9a70a250a6b9d125f09fb1e16 - - 042bbc7494ef5cb0ca7e6465e9f6d6c0141647a7 - - 072d449dc164e98c19805f67ee6a5519b780fd2d - - 09522a037bb7d4eb4eb30d98b17266dc6982e402 - - 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- 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + - 16 + - 17 + - 18 + - 19 + - 20 + - 21 + - 22 + - 23 + - 24 + - 25 + - 26 + - 27 + - 28 + - 29 + - 30 + - 31 + - 32 + - 33 + - 34 + - 35 + - 36 + - 37 + - 38 + - 39 + - 40 + - 41 + - 42 + - 43 + - 44 + - 45 + - 46 + - 47 + - 48 + - 49 + - 50 + - 51 + - 52 + - 53 + - 54 + - 55 + - 56 + - 57 + - 58 + - 59 + - 60 + - 61 + - 62 + - 63 + - 64 + - 65 + - 66 + - 67 + - 68 + - 69 + - 70 + - 71 + - 72 + - 73 + - 74 + - 75 + - 76 + - 77 + - 78 + - 79 + - 80 + - 81 + - 82 + - 83 + - 84 + - 85 + - 86 + - 87 + - 88 + - 89 + - 90 + - 91 + - 92 + - 93 + - 94 + - 95 + - 96 - 97 - 98 - 99 @@ -133,94 +240,9 @@ analysis: - 104 - 105 - 106 - - 107 - - 108 - - 109 - - 110 - - 111 - - 112 - - 113 - - 114 - - 115 - - 116 - - 117 - - 118 - - 119 - - 120 - - 121 - - 122 - - 123 - - 124 - - 125 - - 126 - - 127 - - 128 - - 129 - - 130 - - 131 - - 132 - - 133 - - 134 - - 135 - - 136 - - 137 - - 138 - - 139 - - 140 - - 141 - - 142 - - 143 - - 144 - - 145 - - 146 - - 147 - - 148 - - 149 - - 150 - - 151 - - 152 - - 153 - - 154 - - 155 - - 156 - - 157 - - 158 - - 159 - - 160 - - 161 - - 162 - - 163 - - 164 - - 165 - - 166 - - 167 - - 168 - - 169 - - 170 - - 171 - - 172 - - 173 - - 174 - - 175 - - 176 - - 177 - - 178 - - 179 - - 180 - - 181 - - 182 - - 183 - - 184 - - 185 - - 186 - - 187 - - 188 - - 189 - - 190 - - 191 - - 192 ordered: no stratum: class: character weight: class: numeric + digits: 6 diff --git a/inst/website/00_abstract.Rmd b/inst/website/00_abstract.Rmd deleted file mode 100644 index 4bcefef..0000000 --- a/inst/website/00_abstract.Rmd +++ /dev/null @@ -1,23 +0,0 @@ -\benglish - -# English abstract {-} - -This report publishes the most recent analyses based on the census work carried out for the project "Common Breeding Bird Survey Flanders". -This project is a joint initiative of the Research Institute for Nature and Forest (INBO) and Natuurpunt Studie in cooperation with the local bird working groups. -The cooperation between these organisations guarantees a good support of and communication to the volunteer network by Natuurpunt Studie, and solid professional scientific support and data processing by INBO. -Since 2016, this monitoring scheme makes an integral part of the larger Soortenmeetnetten program that is financially supported by INBO and Agency Nature and Forest (ANB). - -The project was started in 2007 and aims at describing the trend in numbers of a set of ca. 80 general breeding bird species in Flanders. -Both annual fluctuations and multi-annual trends are interesting results in function of local, regional and international policy. - -This report serves as a technical background report. -It consists of three parts: -the first part describes the methodology of processing and presenting the results; -the second part presents a number of indicators based on trends of several types; -the third part describes the time series of individual species. -In parts two and three, we limit ourselves to a standardised representation of the most relevant analysis results without an ecological interpretation. -For an ecological interpretation of the results we refer to other publications such as [INBO Vogelnieuws](https://www.vlaanderen.be/inbo/publicaties/vogelnieuws). - -\eenglish - -\mainmatter diff --git a/inst/website/0_methodologie.Rmd b/inst/website/0_methodologie.Rmd deleted file mode 100644 index 90dece3..0000000 --- a/inst/website/0_methodologie.Rmd +++ /dev/null @@ -1,580 +0,0 @@ -# (PART) Methodologie {-} - -# Inzameling van de tellingen in het veld - -Hier beschrijven we beknopt het veldwerk, voor de details verwijzen we naar @ABVmethode. - -## Steekproef {#s:steekproef} - -De UTM 1 x 1 km hokken vormen de basis waaruit de steekproef getrokken is. -Bij de start van het meetnet hebben we de hokken opgedeeld in een aantal strata. -De regels zijn gebaseerd op het oppervlakteaandeel van een bepaald landgebruik op basis van de Biologische Waarderingkaart [@BWK]. - -1. Landbouw: minstens 80% landbouw. `r strata$n[strata$description == "Landbouw"]` hokken. -1. Urbaan: minstens 80% urbaan. `r strata$n[strata$description == "Urbaan"]` hokken. -1. Bos: minstens 80% bos. `r strata$n[strata$description == "Bos"]` hokken. -1. Suburbaan: minstens 80% suburbaan. `r strata$n[strata$description == "Suburbaan"]` hokken. -1. Heide en duin: minstens 20% heide of duin. `r strata$n[strata$description == "Heide en duin"]` hokken. -1. Moeras en water: minstens 20% moeras en water. `r strata$n[strata$description == "Moeras en water"]` hokken. - -Uit deze set trekken we een aselecte, gestratificeerde steekproef van 1200 hokken waarbij zeldzamere habitats overbemonsterd worden. -We streven er naar om jaarlijks 300 hokken te bemonsteren in een driejarige rotatie. -De waarnemers mochten in het eerste jaar 300 hokken kiezen uit de set van 1200. -Deze set van hokken komen in principe opnieuw aan bod in jaren 4, 7, 10, ... -In jaar 2 kiezen ze 300 hokken uit de overgebleven 900 hokken. -Deze set komt opnieuw aan bod in jaren 5, 8, 11, ... -Tenslotte kiezen de waarnemers in het derde jaar een laatste set van 300 hokken uit de laatste 900 hokken. -Deze set hokken bemonsteren we in de jaren 3, 6, 9, 12, ... - -Figuur \@ref(fig:inspanning) geeft de effectieve monitoringsinspanning weer. -In deze figuur hebben we de hokken gesorteerd volgens 1) het eerste jaar met gegevens, 2) het laatste jaar met gegevens, 3) het tweede jaar met gegevens, 4) het derde jaar met gegevens, ... -Hierdoor staan hokken met een meer gelijkende onderzoeksgeschiedenis dicht bij elkaar. -Merk op dat de driejarige cyclus voor de meest hokken wordt gerespecteerd. -Voor sommige hokken is de inspanning variabel, soms frequenter dan om de drie jaar, soms zit er meer tijd tussen. -Sommige hokken werden slechts in een of twee jaar onderzocht (fig. \@ref(fig:inspanning-hist)). -Voor het onderscheid tussen mogelijk bruikbaar en voorlopig niet bruikbaar verwijzen we naar §\@ref(s:relevant). - -```{r inspanning, fig.cap = "Overzicht van de effectieve monitoringsinspanning. Elk punt is een hok dat onderzocht is in een bepaald jaar. De horizontale lijnen verbinden het eerste en meest recente bezoek aan eenzelfde hok.", fig.height = 9.5} -effort %>% - arrange(.data$year) %>% - group_by(.data$location) %>% - transmute( - .data$year, end = max(.data$year), year2 = lead(.data$year), - year3 = lead(.data$year2), year4 = lead(.data$year3), - year5 = lead(.data$year4), year6 = lead(.data$year5), - year7 = lead(.data$year6) - ) %>% - slice(1) %>% - ungroup() %>% - arrange( - .data$year, .data$end, .data$year2, .data$year3, .data$year4, .data$year5, - .data$year6, .data$year7 - ) %>% - rowid_to_column("hok") %>% - transmute( - .data$location, hok = factor(.data$hok), - bruikbaar = ((.data$year - 2007) %/% 3) < ((end - 2007) %/% 3), - bruikbaar = factor( - .data$bruikbaar, levels = c(TRUE, FALSE), - labels = c("mogelijk", "voorlopig\nniet") - ) - ) %>% - inner_join(effort, by = "location") -> inspanning -ggplot(inspanning, aes(x = year, y = hok, group = hok, colour = bruikbaar)) + - geom_line(alpha = 0.2) + - geom_point(alpha = 0.5, size = ifelse(output_format == "html", 4, 1)) + - facet_wrap(~stratum, scales = "free_y") + - scale_x_continuous(breaks = pretty) + - theme( - axis.title.x = element_blank(), - axis.text.x = element_text(angle = 90), - axis.text.y = element_blank() - ) -``` - -```{r inspanning-hist, fig.cap = "Histogram van het aantal jaren waarin een hok onderzocht is opgesplitst per stratum."} -inspanning %>% - count(stratum, location, bruikbaar) %>% - ggplot(aes(x = n, fill = bruikbaar)) + - geom_histogram(binwidth = 1) + - facet_wrap(~stratum) + - scale_x_continuous( - "Aantal jaren waarin een hok onderzocht is", breaks = pretty, - limits = c(0, NA) - ) + - ylab("Aantal hokken") -``` - -## Steekproefeenheid - -De steekproefeenheid bestaat uit een hok van 1 x 1 km. -Binnen dit hok worden zes punten vastgelegd in een vaste patroon (fig. \@ref(fig:patroon)). -Indien de punten in de praktijk niet bereikbaar zijn, mag de waarnemer ze verplaatsen naar het dichtstbijzijnde bereikbare punt. -De waarnemer documenteert deze wijziging zodat we in de toekomst steeds op dezelfde punten blijven waarnemen. - -```{r patroon, fig.cap = "Standaardlocatie van meetpunten binnen een km hok.", fig.width = 2, fig.height = 2} -expand.grid( - x = seq(1 / 4, by = 1 / 2, length = 2), - y = seq(1 / 6, by = 1 / 3, length = 3) -) %>% - ggplot(aes(x = x, y = y)) + - geom_rect( - data = tribble(~x, ~y, ~xend, ~yend, 0, 0, 1, 1), - aes(xmin = x, ymin = y, xmax = xend, ymax = yend), - fill = NA, colour = inbo_steun_blauw - ) + - geom_point() + - coord_fixed() + - theme_void() -``` - -## Tellingen - -In het jaar dat we een hok bemonsteren zal de waarnemer het hok in drie periodes bezoeken: 1 maart - 15 april, 16 april - 31 mei, 1 juni - 15 juli. -Tussen twee opeenvolgende bezoeken moet er minstens twee weken liggen. -Alle meetpunten van een hok worden op dezelfde dag onderzocht tussen zonsopgang en 4 uur na zonsopgang. -Op elk meetpunt telt de waarnemer gedurende 5 minuten het aantal volwassen vogels per soort. -Overvliegende groepen vogels worden hierbij niet meegeteld. - -# Verwerking van de gegevens - -## Selectie van relevante gegevens per soort {#s:relevant} - -Hoewel we alle waargenomen vogelsoorten op een meetpunt noteren, toch zijn niet alle hokken even relevant voor een bepaalde soort. -Om de redenering op te bouwen, beschouwen we een fictieve soort die enkel op een afgelegen eiland voorkomt. -Alle vogeltellingen buiten dit eiland zijn uiteraard niet informatief voor wijzigingen in de aantallen van deze fictieve soort. -Buiten het eiland zullen de getelde aantallen immers per definitie nul zijn. -Wanneer we deze tellingen toch zouden in rekening brengen, dan zullen ze de globale trend afvlakken. -De globale trend is min of meer equivalent met een "gemiddelde" trend over de verschillende locaties. -Als een locatie steeds nul geeft, dan zijn lokaal de aantallen wiskundig "stabiel" (geen lokale trend). -Daarom is het noodzakelijk om de locaties die niet relevant zijn voor een soort buiten beschouwing te houden. - -In Vlaanderen is de situatie niet zo zwart-wit als bij het voorbeeld met het afgelegen eiland. -Daarom hebben nood aan een set regels die we toepassen op de beschikbare gegevens. - -1. We beschouwen een hok als relevant voor een soort wanneer we deze soort tijdens minstens twee verschillende driejarige cycli waarnemen. -Op deze manier sluiten we hokken uit waar de soort nooit of slechts toevallig waargenomen is. -Hokken die slechts binnen één driejarige cyclus onderzocht zijn, kunnen we hierdoor voorlopig niet gebruiken. -Figuur \@ref(fig:potentieel) geeft een overzicht van het aantal hokken die gedurende minstens twee cycli onderzocht zijn. -1. Binnen een hok is een meetpunt pas relevant wanneer we de soort er minstens eenmaal waarnemen. -1. Een stratum is relevant voor een soort wanneer het minstens drie relevante hokken bevat. -1. Op basis van de overblijvende gegevens berekenen we het (meetkundig[^geometrisch]) gemiddelde van de aantallen in elk van de drie perioden. -De periode met het hoogste gemiddelde gebruiken we als referentieperiode. -We houden enkel de periodes waarbij het gemiddelde minstens 15% van de referentieperiode bedraagt. -Op deze manier sluiten we periodes uit waarbij de soort zelden gezien wordt. -Bijvoorbeeld een trekvogel die pas in de loop van de tweede periode toekomt en waarvoor de eerste periode bijgevolg niet relevant is. -1. We passen de regels voor relevante hokken en strata opnieuw toe. -Het negeren van een of twee irrelevante periodes kan er voor zorgen dat een hok niet langer relevant is. - -[^geometrisch]: Het meetkundig gemiddelde of geometrisch gemiddelde van $n$ getallen wordt verkregen door de getallen met elkaar te vermenigvuldigen en vervolgens van het product de n^de^ machtswortel te nemen. $$m=\left(\prod _{i=1}^{n}a_{i}\right)^{1/n}$$ - -```{r potentieel, fig.cap = "Aantal onderzochte hokken per stratum en per jaar waarbij we enkel rekening houden met hokken die in minstens twee verschillende driejarige cycli onderzocht zijn."} -inspanning %>% - mutate(cyclus = (year - 2007) %/% 3) %>% - distinct(location, cyclus) %>% - count(location) %>% - filter(n >= 2) %>% - semi_join(x = inspanning, by = "location") %>% - count(year, stratum, name = "hokken") %>% - mutate(stratum = reorder(stratum, -hokken)) %>% - ggplot(aes(x = year, y = hokken, colour = stratum)) + - geom_line() + - scale_x_continuous(breaks = pretty) + - scale_y_continuous("Aantal potentieel relevante hokken", limits = c(0, NA)) + - theme(axis.title.x = element_blank()) -``` - -Na het toepassen van deze regels gaan we na of we minstens 100 waarnemingen van de soort hebben. -Hierbij is een waarneming de telling van minstens één dier in een combinatie van jaar, periode, hok en meetpunt. -Stel dat we een soort hebben waarbij we de drie periodes weerhouden en de waarnemer ziet de soort op elk van de zes meetpunten tijdens alle periodes van een bepaald jaar, dan hebben we $3 \times 6 = 18$ waarnemingen voor dat hok in dat jaar. - -Als we minder dan 100 waarnemingen voorhouden voor een soort, dan stopt voorlopig het verhaal voor deze soort. -We hebben dan te weinig waarnemingen voor een betrouwbare trendberekening. -Dit wil niet zeggen dat we deze soort permanent afschrijven! -Wanneer we deze regels in de toekomst opnieuw toepassen hebben we mogelijk wel voldoende waarnemingen. -Ook hokken of strata in de toekomst gekoloniseerd worden, worden dan opgepikt. - -## Trendberekening {#s:trendberekening} - -### Niet-lineaire trends - -Om de trends te berekenen, voorspellen we de waargenomen aantallen aan de hand van een statistisch model. -$Y_{jpshm}$ is de voorspelling voor jaar $j$, periode $p$, stratum $s$, hok $h$ en meetpunt $m$. -Veronderstellen dat deze aantallen uit een `zero-inflated negative binomial`^[Een negatief binomiale verdeling met een overmaat aan nullen.] verdeling komen met parameters $\mu_{jpshm}$, $\nu$ en $\pi$. - -$$Y_{jpshm} \sim ZINB(\mu_{jpshm}, \nu, \pi)$$ - -Laat ons eerst focussen op $\mu_{jpshm}$. -Deze parameter is via een $\log$ link gerelateerd aan de `lineaire predictor` $\eta_{jpshm}$. -Door deze $\log$ link kunnen we de individuele termen van de `lineare predictor` interpreteren als relatieve effecten. - -$$\log\mu_{jpshm} = \eta_{jpshm}$$ - -En nu wordt het interessant want deze `lineaire predictor` hangt af van vijf termen: - -- $\beta_s$: het effect van stratum $s$. -- $\beta_p$: het verschil tussen periode $p$ en de referentieperiode. -- $b_h$: het effect van hok $h$. -Deze effecten komen uit een Gaussiaanse verdeling met gemiddelde 0 en variantie $\sigma^2_h$. -- $b_m$: het effect van meetpunt $m$. -Deze effecten komen uit een Gaussiaanse verdeling met gemiddelde 0 en variantie $\sigma^2_m$. -- $b_{j,s}$: het effect van jaar $j$ in stratum $s$. -Dit effect modelleert een eerste orde random walk per stratum. -Het verschil tussen twee opeenvolgende jaren komt uit een Gaussiaanse verdeling met gemiddelde 0 en variantie $\sigma^2_j$. - -$$\eta_{jpshm} = \beta_s + \beta_p + b_h + b_m + b_{j,s}$$ -$$b_h \sim \mathcal{N}(0, \sigma^2_h)$$ -$$b_m \sim \mathcal{N}(0, \sigma^2_m)$$ -$$b_{j,s} - b_{j-1, s} = \Delta b_{j,s} \sim \mathcal{N}(0, \sigma^2_j)$$ - -De random walk $b_{j,s}$ is de term die ons het meest interesseert aangezien deze modelleert hoe de aantallen in de tijd wijzigen. -Het komt er op neer dat de aantallen in jaar $j$ en stratum $s$ een factor $e^{\Delta b_{j,s}}$ verschillen van het voorgaande jaar in datzelfde stratum. -De variantie $\sigma^2_j$ bepaalt hoe groot de sterkste schommelingen tussen de opeenvolgende jaren kunnen zijn. -Het model laat toe dat elk stratum zijn eigen trend heeft. - -### Statistische verdelingen - -Een standaard distributie voor aantallen is de Poisson verdeling. -Deze heeft als kenmerk dat de variantie gelijk is aan het gemiddelde ($\sigma^2 = \mu$). -Bij veel ecologisch gegevens zien we dat de variantie in de praktijk groter is dan het gemiddelde, een fenomeen dat we overdispersie noemen. -In dat geval kunnen we een negatief binomiale verdeling gebruiken. -Deze heeft als variantie $\sigma^2 = \mu + \mu^2/\nu$, waarbij de parameter $\nu$ is een maat voor overdispersie. -Merk op dat als de overdispersieparameter zeer groot wordt ($\nu = \infty$), dan wordt de term $\mu/\nu = 0$ en bijgevolg reduceert de negatief binomiale verdeling dan tot een Poisson verdeling. - -Sommige ecologische gegevens vertonen een "overmaat" aan nullen, dat zijn meer nulwaarnemingen dan de Poisson of negatief binomiale verdeling kan modelleren. -In dergelijke gevallen kunnen we overschakelen naar de `zero-inflated` versie van deze verdelingen. -Deze hebben een parameter $\pi$ die een maat is voor de kans op een overmatige nul. - -Bij het modelleren zullen we in eerste instantie deze twee parameters instellen op $\nu = \infty$ en $\pi = 0$, m.a.w. geen overdispersie en geen overmaat aan nullen zodat we een Poisson verdeling krijgen. -Vervolgens gaan we na of er voldoende aanwijzingen zijn voor overdispersie of een overmaat aan nullen. -In het geval van overdispersie laten we $\nu$ door het model schatten waardoor we overgaan van een Poisson naar een negatief binomiaal. -In het geval van een overmaat aan nullen laten we $\pi$ door het model schatten waardoor we overgaan van een Poisson naar een `zero-inflated` Poisson. -Soms hebben we zowel aanwijzingen voor overdispersie als een overmaat aan nullen. -In die gevallen kiezen we de negatief binomiaal of `zero-inflated` Poisson naargelang welke de sterkste aanwijzingen heeft. -Vervolgens gaan we na of we de andere parameter ook nog een probleem vormt. -Zo ja, gaan we over naar een `zero-inflated` negatief binomiaal waarbij het model zowel $\nu$ als $\pi$ zal schatten. - -### Lineaire trends {#s:lineairetrend} - -Lineaire trends veronderstellen dat er een constante wijziging is over de volledige looptijd. -Het model dat we hiervoor gebruiken is nagenoeg identiek aan het niet-lineaire model. -Het enige verschil zit in de `lineaire predictor` waar we de first order random walk ($b_{j,s}$) vervangen door een lineaire trend per stratum ($\beta_{s1} j$). - -$$\eta_{jpshm} = \beta_s + \beta_p + b_h + b_m + \beta_{s1} j$$ - -Het lineaire model is een vereenvoudiging van het niet-lineaire model. -We kunnen deze modellen met elkaar vergelijken op basis van het `Wantanabe-Akaike Information Criterion` (WAIC) [@gelmanUnderstandingPredictiveInformation2014]. -De WAIC waarde daalt naarmate het model de gegevens beter kan beschrijven en stijgt wanneer het model complexer wordt. -Als we modellen met elkaar vergelijken op basis van WAIC, zal het model met de laagste WAIC de beste mix zijn tussen een goede beschrijving van de gegevens en een zo eenvoudig mogelijk model. -We beschouwen de trend als **lineair** wanneer het lineaire model de laagste WAIC heeft. -Wanneer de WAIC van het niet-lineaire model minder dan 2 eenheden lager is dan deze van het lineaire model, beschouwen de trend als **mogelijk niet-lineair**. -Pas wanneer de WAIC van het niet-lineaire model duidelijk lager is dan het lineaire model, stellen we dat de trend **niet-lineair** is. -In dat geval de gebruiker moet de gerapporteerde lineaire trend met de nodige voorzichtigheid interpreteren. -Kijk zeker naar de bijhorende niet-lineaire trend vooraleer de cijfers te interpreteren. - -### Resultaten per driejarige cyclus - -Zoals aangegeven in §\@ref(s:steekproef) zullen we een bepaald hok in principe om de drie jaar herbezoeken. -Hierdoor krijgen we drie sets van hokken. -Er bestaat een kans dat de schatting voor een bepaald jaar beïnvloed is door de set van hokken die in dat jaar onderzocht worden. -Om dit effect uit te schakelen, analyseren we de gegevens tevens op basis van de driejarige cyclus $c$ i.p.v. jaar $j$. - -De eerste cyclus omvat de eerste drie jaar sinds de start van het meetnet (2007-2009). -De volgende cyclus omvat telkens de volgende drie jaar aansluitend op de vorige cyclus. -Aangezien we alle beschikbare gegevens gebruiken bij de analyse bevat de laatste cyclus mogelijk minder dan drie jaar. -Dit is duidelijk zichtbaar doordat het laatste jaar van de cyclus op dat ogenblik in de toekomst ligt. -Als bijvoorbeeld 2019 het meest recente jaar met gegevens is, dan is de laatste cyclus 2019-2021. - -Hieronder geven we aan op welke manier we de eerste beschreven modellen aanpassen. - -$$Y_{cpshm} \sim ZINB(\mu_{cpshm}, \nu, \pi)$$ -$$\log\mu_{cpshm} = \eta_{cpshm}$$ - -**Niet-lineaire trend** - -$$\eta_{cpshm} = \beta_s + \beta_p + b_h + b_m + b_{c,s}$$ -$$b_{c,s} - b_{c-1, s} = \Delta b_{c,s} \sim \mathcal{N}(0, \sigma^2_c)$$ - -$b_{c,s}$: het effect van driejarige cyclus $c$ in stratum $s$. -Dit effect modelleert een eerste orde random walk per stratum. -Het verschil tussen twee opeenvolgende driejarige cycli komt uit een Gaussiaanse verdeling met gemiddelde 0 en variantie $\sigma^2_c$. - -**Lineaire trend** - -$$\eta_{cpshm} = \beta_s + \beta_p + b_h + b_m + \beta_{s1} c$$ - - -### Modellen fitten - -We fitten de statische modellen in R [@R] met het INLA package [@rueINLAFunctionsWhich2009]. -INLA gebruikt een Bayesiaanse benadering om de modellen te fitten. -Daarom moeten we priors specificeren voor de parameters en hyperparameters. - -- $\beta_s$, $\beta_p$ en $\beta_{s1}$ krijgen een Gaussiaanse prior $\mathcal{N}(0, 5)$. -- $\sigma^2_h$ krijgt een `penalised complexity` prior [@simpson.rue.ea2017PenalisingModelComponent] zodat $Prob(\sigma_h > 1) = 0.01$. -- $\sigma^2_m$ krijgt een `penalised complexity` prior zodat $Prob(\sigma_m > 1) = 0.01$. -- $\sigma^2_j$ krijgt een `penalised complexity` prior zodat $Prob(\sigma_j > 0.5) = 0.01$. -- $\sigma^2_c$ krijgt een `penalised complexity` prior zodat $Prob(\sigma_c > 0.5) = 0.01$. -- $\nu$ krijgt een Gamma prior zodat $\nu \sim \Gamma(1/7, 1/7)$. -- $\pi$ wordt intern geparametriseerd als $\theta = \log\frac{\pi}{1 - \pi}$ met Gaussiaanse prior $\theta ~ \sim \mathcal{N}(-1, 5)$. - -## Gemiddeld aantal dieren per meetpunt - -Een gemiddeld hok heeft als effect $b_h = 0$ en een gemiddeld meetpunt $b_m = 0$. -In de referentieperiode is $\beta_p = 0$. -In deze gevallen vereenvoudigt de `lineaire predictor` tot het effect van stratum $s$ en de trend in dat stratum: - -$$\eta_{js} = \beta_s + b_{j,s}$$ - -Door de effecten van de strata te vermenigvuldigen met hun stratumgewicht ($\gamma_s$) krijgen we een schatting voor Vlaanderen. - -$$\eta_{j} = \sum_s(\gamma_s\beta_s + \gamma_sb_{j,s})$$ - -Om de schatting van het gemiddelde aantal in Vlaanderen te krijgen, moeten we de `lineaire predictor` terug omzetten van de $\log$ schaal naar de natuurlijke schaal en corrigeren voor de eventuele overmaat aan nullen. -We krijgen dan: - -$$E[Y_j] = (1 - \pi) e^{\eta_j} \prod_s(e^{\gamma_s \beta_s} e ^ {\gamma_s b_{j,s}})$$ - -## Vergelijken van jaren - -We kunnen twee jaren $a$ en $b$ met elkaar vergelijken door hun `lineaire predictoren` voor het gemiddeld aantal dieren van elkaar af te trekken. - -$$\eta_a - \eta_b = \sum_s(\gamma_s\beta_s + \gamma_sb_{a,s}) - \sum_s(\gamma_s\beta_s + \gamma_sb_{b,s})$$ - -Aangezien het globale stratumeffecten en de stratumgewichten niet wijzigen in de tijd, kunnen we dit vereenvoudigen tot - -$$\eta_a - \eta_b = \sum_s\gamma_s(b_{a,s} -b_{b,s})$$ - -De wijziging in Vlaanderen is het gewogen gemiddelde van de wijzigingen in de strata. -Na omzetting van de $log$ schaal naar de natuurlijke schaal krijgen we het relatieve aantal $I_{a|b}$ van jaar $a$ t.o.v. jaar $b$ - -$$I_{a|b}=\frac{e^{\nu_a}}{e^{\nu_b}} = \prod_s\left(\frac{e^{\gamma_sb_{a,s}}}{e^{\gamma_sb_{b,s}}}\right)$$ - -Wanneer we een jaar met zichzelf vergelijken krijgen we per definitie $I_{a|a} = 1 = 100\%$. -Wanneer we meerdere jaren met eenzelfde referentiejaar vergelijken krijgen we een indexwaarde: het relatieve verschil van elk jaar t.o.v. van een bepaald referentiejaar. - -## Gewicht van de strata {#s:stratumgewicht} - -In het vorige onderdeel hebben gebruikt gemaakt van stratumgewichten $\gamma_s$ zonder deze te definiëren. -We bepalen het gewicht van een stratum op basis van drie kenmerken: het totaal aantal hokken van het stratum in Vlaanderen ($N_s$), het aantal onderzochte hokken in het stratum ($T_s$) ongeacht of ze al dan niet relevant zijn voor de soort en het aantal relevante hokken voor de soort in het stratum ($R_s$). - -Het aandeel relevante hokken per stratum ($R_s/T_s$) is een goede maat van de frequentie waarmee een soort aanwezig is binnen een stratum. -Wanneer een soort in nagenoeg alle onderzochte hokken van het stratum waargenomen wordt is $R_s/T_s \simeq 1$. -Is de soort zeer zeldzaam (komt slechts in een paar van de onderzochte hokken voor) dan is $R_s/T_s \simeq 0$. - -Het basisgewicht $\gamma_{sb}$ voor stratum $s$ is het aandeel relevante hokken vermenigvuldigd met het totaal aantal hokken in het stratum. -Dit is een schatting van het totaal aantal hokken in het stratum waar de soort voldoende frequent voorkomt. - -$$\gamma_{sb} = \frac{R_s}{T_s}N_s$$ - -Om makkelijker te kunnen rekenen delen we de basisgewichten door hun som zodat de stratumgewichten sommeren tot 1. - -$$\gamma_s = \frac{\gamma_{sb}}{\sum_s\gamma_{sb}}$$ - -Merk op dat het aantal relevante hokken per stratum ($R_s$) soortafhankelijk is. -Bijgevolg zijn de stratumgewichten eveneens soortafhankelijk. - -## Samengestelde indices {#s:samengesteld} - -We berekenen een aantal samengestelde indices die de trends voor een groep van soorten aggregeren. -Deze indices zijn het meetkundig gemiddelde van de verschillen tussen jaren voor alle soorten van de groep. -Bij de berekening maken we gebruik van het trucje dat we een product kunnen schrijven als een som van logaritmes die we nadien terug exponentiëren. -Een meetkundig gemiddelde in de natuurlijke schaal kunnen we dat schrijven als een rekenkundig gemiddelde in de log-schaal. - -$$\sqrt[n]{\prod_{i+1}^na_i} = \exp\left(\frac{\sum_{i = 1}^n\log a_i}{n}\right)$$ -Het voordeel aan deze techniek is dat de schattingen van de paarsgewijze verschillen tussen de jaren reeds beschikbaar zijn in de log-schaal. -Bovendien beschikken we tevens over hun variantie in de log-schaal. -Dit laat ons toe om makkelijk het betrouwbaarheidsinterval te berekenen aan de hand van een paar vuistregels. - -1. Bij onafhankelijke variabelen geldt dat de variantie van hun som gelijk is aan de som van de varianties. $\sigma^2_{\sum X_i} = \sum \sigma^2_{X_i}$ -1. De variantie van het product van een variabele en een constante is gelijk aan de variantie van de variabele vermenigvuldigd met die constante. $\sigma^2_{aX} = a \sigma^2_X$ -1. Het model veronderstelt dat de individuele modelparameters een Gaussiaanse verdeling volgen. -Hierdoor volgen de paarsgewijze verschillen tussen de jaren en hun gemiddelde (telkens in de log-schaal) een Gaussiaanse verdeling. -Aangezien we zowel het gemiddeld als de variantie van deze verdeling kennen, kunnen we hieruit de gewenste kwantielen voor de betrouwbaarheidsintervallen berekenen (nog steeds in de log-schaal). -1. De log-transformatie is een monotoon stijgende functie aangezien $\log(x)$ steeds groter wordt wanneer $x$ groter wordt. -Een kenmerk van een monotoon stijgende transformatie is dat ze de volgorde behouden: het 5% kleinste element zal ook na de transformatie het 5% kleinste element zijn. -Waardoor we de kwantielen van de betrouwbaarheidsintervallen zonder probleem kunnen terugrekenen naar de natuurlijke schaal. - -# Voorstelling van de gegevens - -## Onzekerheid {#s:onzekerheid} - -Alle resultaten zijn gebaseerd op een steekproef en op de waarnemingen zit onvermijdelijk een zekere meetfout. -Vandaar dat we bij de puntschattingen tevens een betrouwbaarheidsinterval weergeven. -In de tekst gebruiken we het 90% (5%; 95%) interval waarbij er 5% kans is dat de werkelijke waarde kleiner is dan de ondergrens en 5% dat ze groter is dan de bovengrens. -Het 90% interval is iets smaller dan het traditionele 95% (2.5%; 97.5%) interval. -Door een smaller interval te kiezen zullen we sneller uitspraken kunnen doen, waardoor de kans kleiner wordt dat we ten onrechte stellen dat er geen effect is. -De prijs die we hiervoor betalen is dat de kans dat we ten onrechte stellen dat er een significant effect is, stijgt van 5% naar 10%. -De doelstelling van de algemene broedvogelmonitoring is zo spoedig mogelijk detecteren wanneer er iets aan de hand is de broedvogels. -Vanuit dat oogpunt is het vermijden van vals negatieve signalen (ten onrechte stellen dat er niets aan de hand is) belangrijker dan het vermijden van vals positieve signalen (ten onrechte stellen dat er iets aan de hand is). - -Wanneer we het interval op een figuur (bijvoorbeeld fig. \@ref(fig:trendklasse)) weergeven, vullen we het 90% interval aan met de smallere intervallen in stappen van 5%. -@britton.fisher.ea1998InflationReportProjections waren de inspiratie voor deze manier van weergeven. -Het het donkerste deel van het interval komt overeen met het 5% interval en geeft de meeste waarschijnlijke locatie van de werkelijke waarde weer. -Naarmate het interval lichter wordt, neemt de onzekerheid toe. - -## Opdeling van de effecten in een aantal klassen {#s:trendklasse} - -Bij het niet-lineaire model berekenen we alle paarsgewijze relatieve verschillen tussen de verschillende jaren. -Bij het lineaire model hebben we de gemiddelde jaarlijkse relatieve verandering. -Deze laatste rekenen we tevens om naar de totale wijziging over de looptijd van het meetnet omdat dit eenvoudiger te interpreteren is. -Vergelijk een daling met -5% per jaar of een daling met -50% over 15 jaar. -Deze laatste klinkt dramatischer door het grotere cijfer, terwijl -5% per jaar overeenkomt met -54% over 15 jaar. -De totale wijziging over de looptijd van het lineaire model is tevens vergelijkbaar met de wijziging tussen het eerste en laatste jaar van het niet-lineaire model. - -Om de interpretatie makkelijker te maken, delen we de wijzigingen op in tien klassen door hun 90% interval te vergelijken met een referentie, ondergrens en bovengrens. -Een effect is significant wanneer de referentie buiten het 90% interval ligt. -We spreken over een **toename** (**afname**) als het interval volledig boven (onder) de referentie ligt. -Niet significante effect is ook informatief wanneer het bijhorende interval voldoende smal is. -Bijvoorbeeld als het interval volledig tussen de ondergrens en bovengrens ligt. -In dat geval kunnen we stellen dat het effect niet significant en klein is, het immers zeker minder sterk dan de ondergrens en minder sterk dan de bovengrens. -Dergelijk effect krijgt de naam **stabiel**. -Heeft het effect een breed interval dat zowel de boven- als ondergrens bevat, spreken we over een **onduidelijk** effect. -Daarnaast is er nog de mogelijkheid dat het interval zowel de bovengrens (ondergrens) als de referentie bevat maar niet de ondergrens (bovengrens). -Dan spreken we over een **mogelijke toename** (**mogelijke afname**). -We kunnen de boven- en ondergrens eveneens gebruiken om een verder onderscheid te maken binnen de significante effecten. -Een interval volledig boven (onder) de bovengrens (ondergrens) wordt dan een **sterke toename** (**sterke afname**). -Een interval volledig tussen de referentie en de de bovengrens (ondergrens) wordt dan een **matige toename** (**matige afname**). -Een interval dat de referentie niet bevat maar wel de bovengrens (ondergrens) blijft een **toename** (**afname**). -Merk op dat de indeling volledig gebaseerd is op de onzekerheid rond het effect en niet op de puntschatting van het effect zelf. -We vatten de opdeling met bijhorende afkortingen en regels samen in tabel \@ref(tab:regels). -Figuur \@ref(fig:trendklasse) geeft een grafische voorstelling waarbij we de afkortingen in combinatie met aangepaste symbolen gebruiken. -De afkortingen zelf zijn te fijn om als symbool te gebruiken. - -(ref:regels) Overzicht van de benamingen van de tien effectklassen met hun afkorting en de regels. $R$: referentie, $L$: ondergrens, $B$: bovengrens, $l$: ondergrens van het 90% interval, $b$: bovengrens van het 90% interval. $L < R < B$ en $l < b$. - -```{r regels} -tribble( - ~ benaming, ~afkorting, ~regels, - "sterke toename", "`++`", "$B < l$", - "toename", "`+`", "$R < l < B$ en $B < b$", - "matige toename", "`+~`", "$R < l < B$ en $b < B$", - "stabiel", "`~`", "$L < l < R$ en $R < b < B$", - "matige afname", "`-~`", "$L < l < R$ en $b < R$", - "afname", "`-`", "$l < L$ en $L < b < R$", - "sterke afname", "`--`", "$l < L$", - "mogelijke toename", "`?+`", "$L < l < R$ en $B < b$", - "mogelijke afname", "`?-`", "$l < L$ en $R < b < B$", - "onduidelijk", "`?`", "$l < L$ en $B < b$" -) %>% - kable( - caption = "(ref:regels)", format = "pandoc", escape = FALSE, align = "lcc" - ) -``` - -(ref:trendklasse) Voorbeeld van de tien mogelijke interpretaties van een effect door het 90% interval te vergelijken met een referentie, ondergrens en bovengrens. - -```{r trendklasse, fig.cap = "(ref:trendklasse)"} -ggplot(effect, aes(x = x_int, y = y, ymin = lcl, ymax = ucl, link_sd = s)) + - annotate( - geom = "rect", xmin = -Inf, xmax = Inf, ymin = -1, - ymax = 1, alpha = 0.5, fill = inbo_grijs - ) + - geom_hline(yintercept = 0, linetype = 2) + - stat_fan(geom = "rect") + - scale_x_continuous(breaks = effect$x_int, labels = effect$x) + - scale_y_continuous( - "effect", breaks = c(-1, 0, 1), - labels = c("ondergrens", "referentie", "bovengrens") - ) + - coord_flip() + - theme(axis.title.y = element_blank()) -``` - -Uiteraard hangt de opdeling sterk af van de keuze van de boven- en ondergrens. -De soortenmeetnetten voor de Natura 2000 monitoring streven er naar om een daling in populatiegrootte met -25% over 25 jaar tijd vast te kunnen stellen. -Hierbij wordt een wijziging in de populatiegrootte van -25% als belangrijk aanzien. -Daarom hanteren we voor de algemene broedvogelmonitoring ook -25% als ondergrens, zowel bij de paarsgewijze verschillen tussen de jaren als de lineaire wijziging over de looptijd van het meetnet. -Een van daling van -25% komt overeen met aantallen die nog drie kwart van de uitgangssituatie bedragen. -Een even sterke wijziging in de omgekeerde richting zorgt er voor dat de aantallen toenemen tot vier derde van de uitgangssituatie, of een toename met +33% wat we als bovengrens gebruiken. - -Bij de samengestelde indices hebben we aangepaste grenswaarden nodig. -Veronderstel een samengestelde index op basis van $n$ soorten. -We berekenen het rekenkundig gemiddelde in de log-schaal, dan is de variantie van dit gemiddelde een factor $n$ kleiner dan de som van de varianties. -De breedte van een betrouwbaarheidsinterval hangt samen met de standaard afwijking, wat de vierkantswortel van de variantie is. -Hierdoor zullen de breedtes van de betrouwbaarheidsintervallen van de samengestelde index een factor $\sqrt{n}$ kleiner zijn. -Vandaar dat we de grenswaarden van de indices tevens aanpassen door ze met een factor $\sqrt{n}$ te verkleinen. -Elke samengestelde index heeft zijn eigen soortenlijst met een variabele aantal soorten. -Daarom zullen we bij elke samengestelde index zijn aangepaste grenswaarden vermelden. - -## Overzicht van de lineaire trends - -Tabellen \@ref(tab:driejaarlijkse-trends) en \@ref(tab:jaarlijkse-trends) geven een overzicht van de lineaire trends voor elke soort. -We hebben de trends gesorteerd volgens opdeling van de klassen en binnen de klasse volgens puntschatting van de trend. -Hierdoor start de tabel met de soorten met de sterkste positieve trends. -Bij de volgende soorten zal de trend minder sterk worden tot we aan de soorten met een stabiele trend komen. -Daarna volgen de soorten met een negatieve trend waarbij de trend steeds sterker negatief wordt. -We sluiten de tabel af met de soorten met een mogelijke of onduidelijke trend. -De tabel bevat de trend uitgedrukt als een jaarlijkse wijziging en als een wijziging over de volledige looptijd van het meetnet. -Verder bevat de tabel de opdeling van de trend in klassen en een indicatie of de trend al dan niet lineair is (zie §\@ref(s:lineairetrend)). -De naam van de soort is een snelkoppeling naar de detail van de soort zelf. -We raden aan om hiervan gebruikt te maken bij de interpretatie van niet-lineaire trends. - -## Evolutie van gemiddelde aantallen per soort - -Deze figuur geeft de evolutie van de gemiddelde aantallen per meetpunt weer volgens het niet-lineair model. -Indien het model lineair is, zal het patroon van de niet-lineaire trend dicht bij een lineaire trend liggen. -De lijn bevat de puntschatting van het gemiddelde aantal in elk jaar. -Rond de lijn tonen we het 30%, 60% en 90% interval (zie §\@ref(s:onzekerheid)). -In de online versie van het rapport is de figuur interactief. -Wanneer de gebruiker met de muis over de figuur gaat verschijnt er in de buurt van de lijn een pop-up met de exacte schatting van dat jaar inclusief het 90% betrouwbaarheidsinterval en het jaartal. - -## Paarsgewijze vergelijking van jaren - -Deze informatie hebben we telkens in twee figuren samengevat. -De eerste figuur geeft het verschil van elk jaar t.o.v. 2007, het jaar waarin de metingen gestart zijn. -Voor 2007 geven we geen cijfer omdat het per definitie 1 is en geen informatie bevat. -Het symbool geeft de puntschatting van het relatieve verschil t.o.v. 2007 weer. -De vorm van het symbool geeft de opdeling van de sterkte van het effect weer (zie fig. \@ref(fig:trendklasse)). -Rond elke puntschatting tonen we het 30%, 60% en 90% interval (zie §\@ref(s:onzekerheid)). -De horizontale streepjeslijn geeft de referentie van 0% verschil weer. -De horizontale puntlijnen geven de bovengrens (+33%) en ondergrens (-25%) weer. -Deze laten toe om de vlot zelf de betrouwbaarheidsintervallen te vergelijken met de referentie, boven- en ondergrens. -De figuur heeft twee y-assen. -Beide assen geven dezelfde informatie weer, enkel de formattering van de labels is anders. -De linkeras toont procentuele verschillen terwijl de rechteras de relatieve verschillen toont. -Een procentueel verschil van +50% is equivalent met een relatieve verschil (verhouding) met een factor 1,5. - -We stellen vast dat veel gebruikers ook andere jaren met elkaar willen vergelijken. -Een correcte vergelijking is enkel mogelijk indien we een van deze jaren als referentie gebruiken. -Voor elk jaar een afzonderlijke figuur maken, zou het rapport onoverzichtelijk groot maken. -Om de vergelijkingen toch mogelijk te maken hebben we alle paarsgewijze verschillen tussen de jaren in een raster weergeven. -Elke rij in het raster staat voor een ander referentiejaar. -De kolommen geven de verschillen van een bepaald jaar weer t.o.v. de verschillende referentiejaren. -Op de diagonaal staan geen waarden omdat we daar een jaar met zichzelf vergelijken. -De kleur van de symbolen geeft de sterkte van het verschil (gebaseerd op de puntschatting). -Zwakke verschillen zijn grijs, sterke positieve verschillen rood, sterke negatieve verschillen blauw. -De vorm van de symbolen geeft zicht op de sterkte en onzekerheid van het effect (zie tab. \@ref(tab:regels)). -Wanneer een rij volledig rood (blauw) is, zijn alle verschillen met dit referentiejaar positief (negatief) m.a.w. dit is het referentiejaar met de laagste (hoogste) aantallen. -Wanneer een kolom volledig rood (blauw) is, zijn alle verschillen van dit jaar t.o.v. alle referentiejaren negatief (positief) m.a.w. dit is het jaar met de hoogste (laagste) aantallen. -Clusters van punten met een gelijkaardige kleur geven periodes aan waarin de aantallen geleidelijk wijzigen. -In de online versie van dit rapport is deze figuur interactief. -Wanneer de gebruiker met de muis over de figuur gaat verschijnt er in de buurt van de lijn een pop-up met de exacte schatting van dat punt inclusief het 90% betrouwbaarheidsinterval en het jaartal en referentiejaar. - -# Reproduceerbaarheid en traceerbaarheid van de gegevensverwerking - -## Versiebeheer - -De waarnemers voeren hun waarnemingen via een webapplicatie toe aan de databank. -De analyse start met het importeren van de relevante gegevens uit de databank aan de hand van een R-script. -De bekomen dataset bewaren we als een collectie van tekstbestanden met behulp van het `git2rdata` package [@git2rdata]. -Zowel deze code als de tekstbestanden bewaren we onze versiebeheer met [git](https://git-scm.com/). -Git is een gratis en open source gedistribueerd versiebeheersysteem dat is ontworpen om alles van kleine tot zeer grote projecten snel en efficiëntie te beheren. -De code zit vervat in het `abvanalysis` package [@abvanalysis], dat vrij online beschikbaar is. -De tekstbestanden met de gegevens zijn momenteel niet publiek toegankelijk. -De ruwe gegevens worden geaggregreerd per hok en met drie jaar vertraging publiek ontsloten via [GBIF](https://doi.org/10.15468/xj0ikb) [@vermeersch.anselin.ea2018ABVCommonBreeding]. - -## Reproduceerbaarheid - -Om de reproduceerbaarheid te garanderen starten we de analyses steeds vanaf de tekstbestanden. -Aan de hand van code in het `abvanalysis` package definiëren we hoe we de tekstbestanden omzetten naar de verschillende analyses (§\@ref(s:trendberekening)). -Dit resulteert in een resem op zichzelf staande analyseobjecten van het `n2kanalysis` package [@n2kanalysis]. -Dergelijk analyseobject bevat naast de nodige gegevens en de modeldefinitie tevens de nodige metadata zoals alle gebruikte R packages, inclusief hun versie, en een verwijzing naar de voorafgaande analyses waarvan deze analyse afhangt. -Zo verwijst de analyse van een samengestelde index (§\@ref(s:samengesteld)) naar de trendberekening van de bijhorende soorten (§\@ref(s:trendberekening)). -De individuele trendberekeningen verwijzen naar de "analyse" die de gegevens importeerde uit de databank. -Deze laatste bevat de nodige links naar de tekstbestanden en hun versie. - -## Traceerbaarheid {#s:traceerbaarheid} - -De `n2kanalysis` objecten hebben elk twee data hashes. -Elk data hash is een reeks van 40 hexadecimale^[Hexadecimaal betekent letterlijk zestientallig. Het is een talstelsel waarbij niet, zoals gebruikelijk, met tien cijfers wordt gewerkt, maar met zestien cijfers. De cijfers 0 t.e.m. 9 worden daarom uitgebreid met `a` (=10) t.e.m. `f` (=15).] cijfers die het resultaat zijn van de cryptografische [hashfunctie](https://nl.wikipedia.org/wiki/Hashfunctie) [SHA-1](https://nl.wikipedia.org/wiki/SHA-familie). -Deze hashfunctie heeft een aantal belangrijke eigenschappen: - -1. Ze zetten elke invoer om naar een uitvoer met vaste lengte (40 hexadecimale cijfers). -1. De uitvoer is stabiel: als je de hash van een bepaalde invoer opnieuw berekent krijg je steeds dezelfde uitvoer. -1. Het is niet mogelijk om de invoer te reconstrueren op basis van de uitvoer. -1. Eender welke kleine wijziging aan de invoer resulteert in een sterke wijziging van de uitvoer. -1. De kans dat twee verschillende invoeren dezelfde uitvoer opleveren is zeer klein. - -De eerste data hash van het analyseobject is gebaseerd op alle informatie die gekend is op het moment dat we het analyseobject definiëren en die nooit zal wijzigen tijdens de analyse. -Denk hierbij aan de definitie van de analyse, de gegevens, de soort, ... -Gezien de eigenschappen van de hashfunctie kunnen we deze data hash gebruiken om ondubbelzinnig te verwijzen naar een specifieke analyse (inclusief de gebruikte gegevens.) - -De tweede data hash van het analyseobject baseren we enerzijds op de eerste data hash en anderzijds van alle onderdelen van het analyseobject die wijzigen in de loop van de analyse. -Dit is o.a. het resultaat van de statistische analyse, de gebruikte software, ... -De analyse opnieuw uitrekenen met software van een andere versie zal de tweede data hash aanpassen. - -De combinatie van deze twee data hashes laat enerzijds toe om naar een specifieke versie van de analyse te verwijzen. -Anderzijds bieden ze een garantie over de inhoud van het analyseobject. -In het geval van twijfel over een analyse kunnen we teruggrijpen naar de analyseobject in kwestie. -De data hashes bewijzen dan dat we het correcte analyseobject hebben. diff --git a/inst/website/1_samengesteld.Rmd b/inst/website/1_samengesteld.Rmd deleted file mode 100644 index a96628d..0000000 --- a/inst/website/1_samengesteld.Rmd +++ /dev/null @@ -1,46 +0,0 @@ -# (PART) Indicatoren {-} - -```{r bewaar-indicatoren, eval = output_format == "html"} -soortenlijst %>% - transmute( - .data$soort, indicator = .data$speciesgroup, - ondergrens = round(exp(.data$drempel), 3), - bovengrens = round(exp(-.data$drempel), 3) - ) %>% - write_vc( - file = "samengesteld_soorten", root = output_dir, strict = strict, - sorting = c("indicator", "soort"), optimize = FALSE - ) -> samensteld_groep_vc -composite_index %>% - inner_join(meta, by = "analysis") %>% - transmute( - indicator = factor(.data$speciesgroup), - frequentie = factor(ifelse(.data$cycle, "driejaarlijks", "jaarlijks")), - .data$referentie, naar = .data$periode, - schatting = round(exp(.data$estimate), 3), - lcl = round(exp(.data$lcl), 3), - ucl = round(exp(.data$ucl), 3), - klasse = .data$klasse, .data$interpretatie, .data$wijziging, - analyse = .data$analysis, status = .data$status_fingerprint - ) %>% - write_vc( - file = "samengesteld", root = output_dir, optimize = FALSE, strict = strict, - sorting = c("indicator", "frequentie", "referentie", "naar") - ) -> samengesteld_vc -``` - -```{r composite-indices, results='asis', cache = FALSE, eval = TRUE} -meta %>% - filter(.data$composite, .data$status == "converged") %>% - arrange(.data$speciesgroup) %>% - distinct(.data$hash) %>% - pull(.data$hash) %>% - sapply( - function(id) { - knit_expand("_samengesteld.Rmd", id = id) - } - ) %>% - paste(collapse = "\n") -> rmd -knit(text = rmd, quiet = TRUE) %>% - cat() -``` diff --git a/inst/website/2_trend_per_soort.Rmd b/inst/website/2_trend_per_soort.Rmd deleted file mode 100644 index 5f63f68..0000000 --- a/inst/website/2_trend_per_soort.Rmd +++ /dev/null @@ -1,144 +0,0 @@ -# (PART) Resultaten per soort {-} - -```{r relevante_soort} -meta %>% - filter(!.data$composite, .data$status == "converged") %>% - distinct(.data$hash, .data$speciesgroup) -> relevant -``` - -```{r bewaar-soorten, eval = output_format == "html"} -estimate %>% - inner_join(meta, by = "analysis") %>% - transmute( - soort = factor(.data$speciesgroup), - frequentie = factor(ifelse(.data$cycle, "driejaarlijks", "jaarlijks")), - jaar = .data$year, - schatting = round(exp(.data$estimate), 3), - lcl = round(exp(.data$lower_confidence_limit), 3), - ucl = round(exp(.data$upper_confidence_limit), 3), - tekst = format_ci( - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, link = "log" - ), - .data$analysis, status = .data$status_fingerprint - ) %>% - arrange(.data$lcl) %>% - write_vc( - file = "aantallen", root = output_dir, optimize = FALSE, - sorting = c("soort", "frequentie", "jaar"), strict = strict - ) -linear_trend %>% - inner_join(meta, by = c("hash", "analysis", "cycle")) %>% - transmute( - soort = factor(.data$speciesgroup), - frequentie = factor(ifelse(.data$cycle, "driejaarlijks", "jaarlijks")), - schatting = round(exp(.data$estimate), 3), - lcl = round(exp(.data$lcl), 3), - ucl = round(exp(.data$ucl), 3), - .data$jaarlijks, .data$looptijd, .data$klasse, - verloop = .data$interpretatie, .data$analysis, - status = .data$status_fingerprint - ) %>% - write_vc( - file = "lineaire_trend", root = output_dir, optimize = FALSE, - sorting = c("soort", "frequentie"), strict = strict - ) -index %>% - inner_join(meta, by = "analysis") %>% - transmute( - soort = factor(.data$speciesgroup), - frequentie = factor(ifelse(.data$cycle, "driejaarlijks", "jaarlijks")), - .data$referentie, naar = .data$periode, - schatting = round(exp(.data$estimate), 3), - lcl = round(exp(.data$lcl), 3), - ucl = round(exp(.data$ucl), 3), - klasse = .data$klasse, .data$interpretatie, .data$wijziging, - .data$analysis, status = .data$status_fingerprint - ) %>% - write_vc( - file = "index", root = output_dir, optimize = FALSE, strict = strict, - sorting = c("soort", "frequentie", "referentie", "naar") - ) -stratum %>% - inner_join(meta, by = "analysis") %>% - transmute( - soort = factor(.data$speciesgroup), - frequentie = factor(ifelse(.data$cycle, "driejaarlijks", "jaarlijks")), - .data$stratum, gewicht = round(.data$gewicht, 3), - aanwezig = round(.data$aanwezig, 1), .data$relevant, .data$onderzocht, - .data$totaal, .data$bezoeken - ) %>% - write_vc( - file = "stratum_gewicht", root = output_dir, optimize = FALSE, - sorting = c("soort", "frequentie", "stratum"), strict = strict - ) -``` - -# Overzicht van de trends - -Tabel \@ref(tab:jaarlijkse-trends) geeft een overzicht van de lineaire trends voor elke soort. -We hebben de trends gesorteerd volgens opdeling van de klassen (zie \@ref(s:trendklasse) voor de verklaring) en binnen de klasse volgens puntschatting van de trend. -Hierdoor start de tabel met de soorten met de sterkste positieve trends. -Bij de volgende soorten zal de trend minder sterk worden tot we aan de soorten met een stabiele trend komen. -Daarna volgen de soorten met een negatieve trend waarbij de trend steeds sterker negatief wordt. -We sluiten de tabel af met de soorten met een mogelijke of onduidelijke trend. -De tabel bevat de trend uitgedrukt als een jaarlijkse wijziging en als een wijziging over de volledige looptijd van het meetnet. -Verder bevat de tabel de opdeling van de trend in klassen en een indicatie of de trend al dan niet lineair is (zie §\@ref(s:lineairetrend)). -De naam van de soort is een snelkoppeling naar de detail van de soort zelf. -We raden aan om hiervan gebruikt te maken bij de interpretatie van niet-lineaire trends. - -(ref:driejaarlijkse-trends) Gemiddelde wijziging in veronderstelling van een lineaire trend gebaseerd op driejaarlijkse gegevens. `++`: sterke toename, `+`: toename, `+~`: matige toename, `~`: stabiel, `~-`: matige afname, `-`: afname, `--`: sterke afname, `?+`: mogelijke toename, `?-`: mogelijke afname, `?`: onduidelijke trend. Referentie: 0%, ondergrens: -25% over de looptijd, bovengrens: +33% over de looptijd. - -```{r driejaarlijkse-trends, results = "asis"} -linear_trend |> - filter(.data$cycle) |> - inner_join(meta, by = c("hash", "analysis", "cycle")) |> - arrange(.data$klasse, desc(.data$estimate)) |> - transmute( - soort = tolower(.data$speciesgroup) |> - str_replace_all(" ", "-") |> - str_remove_all("'") |> - sprintf(fmt = "[%2$s](#soort-%1$s)", .data$speciesgroup), - klasse = format(.data$klasse, type = "markdown"), .data$interpretatie, - `jaarlijse wijziging` = .data$jaarlijks, - `wijziging over de looptijd` = .data$looptijd - ) |> - kable( - caption = "(ref:driejaarlijkse-trends)", format = "pipe", escape = FALSE - ) -``` - -(ref:jaarlijkse-trends) Gemiddelde wijziging in veronderstelling van een lineaire trend gebaseerd op jaarlijkse gegevens. `++`: sterke toename, `+`: toename, `+~`: matige toename, `~`: stabiel, `~-`: matige afname, `-`: afname, `--`: sterke afname, `?+`: mogelijke toename, `?-`: mogelijke afname, `?`: onduidelijke trend. Referentie: 0%, ondergrens: -25% over de looptijd, bovengrens: +33% over de looptijd. - -```{r jaarlijkse-trends, results = "asis"} -linear_trend |> - filter(!.data$cycle) |> - inner_join(meta, by = c("hash", "analysis", "cycle")) |> - arrange(.data$klasse, desc(.data$estimate)) |> - transmute( - soort = tolower(.data$speciesgroup) |> - str_replace_all(" ", "-") |> - str_remove_all("'") |> - sprintf(fmt = "[%2$s](#soort-%1$s)", .data$speciesgroup), - klasse = format(.data$klasse, type = "markdown"), .data$interpretatie, - `jaarlijse wijziging` = .data$jaarlijks, - `wijziging over de looptijd` = .data$looptijd - ) |> - kable(caption = "(ref:jaarlijkse-trends)", format = "pipe", escape = FALSE) -``` - -```{r indices, results='asis', cache = FALSE, eval = TRUE} -meta %>% - filter(!.data$composite, .data$status == "converged") %>% - arrange(.data$speciesgroup) %>% - distinct(.data$hash) %>% - pull(.data$hash) %>% - sapply( - function(id) { - knit_expand("_soort.Rmd", id = id) - } - ) %>% - paste(collapse = "\n") -> rmd -knit(text = rmd, quiet = TRUE) %>% - cat() -``` diff --git a/inst/website/LICENSE.md b/inst/website/LICENSE.md deleted file mode 100644 index 7e5a52a..0000000 --- a/inst/website/LICENSE.md +++ /dev/null @@ -1,161 +0,0 @@ -## creative commons - -# Attribution 4.0 International - -Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. 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Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at [creativecommons.org/policies](http://creativecommons.org/policies), Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. - -Creative Commons may be contacted at [creativecommons.org](http://creativecommons.org/). -``` diff --git a/inst/website/_bookdown.yml b/inst/website/_bookdown.yml deleted file mode 100755 index e8b7056..0000000 --- a/inst/website/_bookdown.yml +++ /dev/null @@ -1,22 +0,0 @@ -book_filename: "abv_standaard_rapport.Rmd" -output_dir: "../output/website" -rmd_files: - html: ["index.Rmd", "00_abstract.Rmd", "0_methodologie.Rmd", "download_html.Rmd", "1_samengesteld.Rmd", "2_trend_per_soort.Rmd", "zzz_bibliografietitel.Rmd"] - latex: ["index.Rmd", "00_abstract.Rmd", "0_methodologie.Rmd", "download_non_html.Rmd", "1_samengesteld.Rmd", "2_trend_per_soort.Rmd", "zzz_bibliografietitel.Rmd"] - epub: ["index.Rmd", "00_abstract.Rmd", "0_methodologie.Rmd", "download_non_html.Rmd", "1_samengesteld.Rmd", "2_trend_per_soort.Rmd", "zzz_bibliografietitel.Rmd"] - word: ["index.Rmd", "00_abstract.Rmd", "0_methodologie.Rmd", "download_non_html.Rmd", "1_samengesteld.Rmd", "2_trend_per_soort.Rmd", "zzz_bibliografietitel.Rmd"] -delete_merged_file: TRUE -language: - label: - fig: 'Figuur ' - tab: 'Tabel ' - eq: 'Vergelijking ' - thm: 'Theorema ' - lem: 'Lemma ' - def: 'Definitie ' - cor: 'Gevolgtrekking ' - prp: 'Bewering ' - ex: 'Voorbeeld ' - proof: 'Bewijs. ' - remark: 'Opmerking. ' -edit: https://github.com/inbo/abvanalysis/edit/main/inst/website/%s diff --git a/inst/website/_samengesteld.Rmd b/inst/website/_samengesteld.Rmd deleted file mode 100644 index f2e3f3d..0000000 --- a/inst/website/_samengesteld.Rmd +++ /dev/null @@ -1,300 +0,0 @@ -```{r {{id}}-set} -if (interactive()) { - meta |> - filter(.data$composite, .data$status == "converged") |> - sample_n(1) |> - pull(.data$hash) -> id -} else { - id <- "{{id}}" -} -``` - -```{r {{id}}-basis} -meta |> - filter(.data$hash == id) -> deze_analyse -deze_analyse |> - slice(1) |> - pull(.data$speciesgroup) -> soort_naam -soortenlijst |> - filter(.data$hash == id) |> - mutate( - soort = tolower(.data$soort) |> - str_replace_all(" ", "-") |> - str_remove_all("'") |> - sprintf(fmt = "[%2$s](#soort-%1$s)", .data$soort) - ) |> - arrange(.data$soort) |> - pull(.data$soort) -> deze_soorten -soortenlijst |> - filter(.data$hash == id) |> - distinct(.data$drempel) |> - pull(.data$drempel) -> deze_threshold -deze_analyse |> - select("analysis", "cycle") |> - inner_join(composite_index, by = "analysis") -> deze_wijziging -``` - -# `r sprintf("%s {#index-%s}", soort_naam, tolower(soort_naam))` - -Deze indicator is gebaseerd op volgende `r length(deze_soorten)` soorten: `r paste(deze_soorten, collapse = ", ")`. -De indicator bestaat uit `r length(deze_soorten)` soorten. -De aangepaste grenswaarden zijn `r sprintf("%+.1f%%", exp(deze_threshold) * 100 - 100)` en `r sprintf("%+.1f%%", exp(-deze_threshold) * 100 - 100)`. - -(ref:{{id}}-index) Wijzigingen t.o.v. de start van het meetnet voor `r soort_naam`. Zie §\@ref(s:onzekerheid) voor een verklaring van de intervallen en §\@ref(s:trendklasse) voor uitleg over de symbolen en referentielijnen. - -```{r {{id}}-wijziging-jaar-statisch, fig.cap = "(ref:{{id}}-index)", eval = nrow(deze_wijziging) > 0 && (interactive() || opts_knit$get("rmarkdown.pandoc.to") != "html"), warning = FALSE} -deze_wijziging |> - filter(!.data$cycle, .data$referentie == min(.data$referentie)) |> - mutate(sd = (.data$ucl - .data$lcl) / 2 / qnorm(0.95)) -> jaarlijks -change_breaks(n = 4, extra = c(deze_threshold, -deze_threshold))( - c(jaarlijks$lcl, jaarlijks$ucl) -) -> deze_breaks -ggplot(jaarlijks, aes(x = periode, y = estimate)) + - annotate( - geom = "rect", xmin = -Inf, xmax = Inf, ymin = deze_threshold, - ymax = -deze_threshold, alpha = 0.2, fill = inbo_grijs - ) + - geom_hline(yintercept = 0) + - stat_fan(aes(link_sd = sd), geom = "rect") + - annotate( - geom = "text", x = Inf, y = 0, hjust = 1, vjust = -0.1, - label = "toestand in 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = deze_threshold, hjust = 1, vjust = 1, - label = "belangrijke afname t.o.v. 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = -deze_threshold, hjust = 1, vjust = 0, - label = "belangrijke toename t.o.v. 2007", colour = inbo_hoofd - ) + - theme(axis.title = element_blank()) + - scale_y_continuous(breaks = deze_breaks, labels = change_labels) -``` - -```{r {{id}}-wijziging-jaar, fig.cap = "(ref:{{id}}-index)", eval = nrow(deze_wijziging) > 0 && (interactive() || opts_knit$get("rmarkdown.pandoc.to") == "html"), warning = FALSE} -deze_wijziging |> - filter(!.data$cycle) -> nadien -nadien |> - mutate( - old = .data$referentie, referentie = .data$periode, periode = .data$old, - estimate = -.data$estimate, old = .data$lcl, lcl = -.data$ucl, - ucl = -.data$old, - interpretatie = classification( - .data$lcl, .data$ucl, threshold = deze_threshold - ) |> - as.character() |> - recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" - ), - wijziging = format_ci( - .data$estimate, lcl = .data$lcl, ucl = .data$ucl, percent = TRUE, - sign = TRUE, change = TRUE, link = "log" - ) - ) -> voordien -voordien |> - bind_rows( - nadien, - nadien |> - distinct(.data$referentie) |> - mutate( - periode = .data$referentie, estimate = 0, lcl = 0, ucl = 0, - wijziging = "", interpretatie = "referentie" - ) - ) |> - arrange(.data$referentie, .data$periode) -> jaarlijks -change_breaks(n = 4, extra = c(deze_threshold, -deze_threshold))( - c(jaarlijks$lcl, jaarlijks$ucl) -) -> deze_breaks -jaarlijks |> - transmute( - .data$referentie, .data$periode, .data$estimate, .data$lcl, .data$ucl, - sd = (.data$ucl - .data$lcl) / 2 / qnorm(0.95), - wijziging = sprintf( - "%.0f t.o.v. %i\n%s\n%s", floor(.data$periode), .data$referentie, - .data$wijziging, .data$interpretatie - ) |> - str_replace_all(".*\n\nreferentie", "referentie") - ) |> - group_by(.data$referentie) |> - mutate( - ref_y = c(0, deze_threshold, -deze_threshold) |> - c(rep(NA, n() - 3)), - ref_text = c( - "toestand in", "belangrijke afname t.o.v.", "belangrijke toename t.o.v." - ) |> - paste(.data$referentie), - textposition = c(-0.1, 1, 0) |> - rep(n()) |> - head(n()) - ) |> - ungroup() -> index_jaarlijks -p <- ggplot( - index_jaarlijks, aes(x = periode, y = estimate, frame = referentie) -) + - annotate( - geom = "rect", xmin = -Inf, xmax = Inf, ymin = deze_threshold, - ymax = -deze_threshold, alpha = 0.2, fill = inbo_grijs - ) + - geom_hline(yintercept = 0) + - stat_fan(aes(link_sd = sd), geom = "rect") + - geom_text( - aes(x = Inf, y = ref_y, label = ref_text, vjust = textposition), hjust = 1, - colour = inbo_hoofd - ) + - theme(axis.title = element_blank()) + - scale_y_continuous(breaks = deze_breaks, labels = change_labels) -ggplotly(p) |> - animation_opts(frame = 1000, transition = 0, redraw = TRUE) |> - animation_slider( - font = list(color = "black"), - currentvalue = list( - prefix = "geselecteerd referentiejaar: ", - font = list(color = inbo_hoofd), xanchor = "center" - ) - ) |> - animation_button(label = "\u25B6") |> - plotly::config( - modeBarButtonsToRemove = list( - "lasso2d", "select2d", "autoScale2d", "hoverClosestCartesian", - "hoverCompareCartesian", "toggleSpikelines" - ), - displaylogo = FALSE - ) -``` - -(ref:{{id}}-index-cyclus) Wijzigingen per driejarige cyclus voor `r soort_naam`. Zie §\@ref(s:onzekerheid) voor een verklaring van de intervallen en §\@ref(s:trendklasse) voor uitleg over de symbolen en referentielijnen. - -```{r {{id}}-wijziging-cyclus-statisch, fig.cap = "(ref:{{id}}-index-cyclus)", eval = nrow(deze_wijziging) > 0 && (interactive() || opts_knit$get("rmarkdown.pandoc.to") != "html"), warning = FALSE} -deze_wijziging |> - filter(.data$cycle, .data$referentie == min(.data$referentie)) |> - mutate(sd = (.data$ucl - .data$lcl) / 2 / qnorm(0.95)) -> cyclus -change_breaks(n = 4, extra = c(deze_threshold, -deze_threshold))( - c(cyclus$lcl, cyclus$ucl) -) -> deze_breaks -ggplot(cyclus, aes(x = periode, y = estimate)) + - annotate( - geom = "rect", xmin = -Inf, xmax = Inf, ymin = deze_threshold, - ymax = -deze_threshold, alpha = 0.2, fill = inbo_grijs - ) + - geom_hline(yintercept = 0) + - stat_fan(aes(link_sd = sd), geom = "rect") + - annotate( - geom = "text", x = Inf, y = 0, hjust = 1, vjust = -0.1, - label = "toestand in 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = deze_threshold, hjust = 1, vjust = 1, - label = "belangrijke afname t.o.v. 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = -deze_threshold, hjust = 1, vjust = 0, - label = "belangrijke toename t.o.v. 2007", colour = inbo_hoofd - ) + - theme(axis.title = element_blank()) + - scale_x_continuous( - breaks = unique(cyclus$periode), labels = periode_labels - ) + - scale_y_continuous(breaks = deze_breaks, labels = change_labels) -``` - -(ref:{{id}}-raster) Paarsgewijze vergelijking tussen jaren voor `r soort_naam`. - -```{r {{id}}-wijziging-jaar-alles2, fig.cap = "(ref:{{id}}-raster)", eval = output_format != "html" && nrow(deze_wijziging) > 0} -deze_wijziging |> - filter(!.data$cycle) |> - rename(periode = .data$referentie, referentie = .data$periode) |> - mutate( - klasse = classification(-.data$ucl, -.data$lcl, threshold = deze_threshold) - ) |> - bind_rows( - deze_wijziging |> - filter(!.data$cycle) - ) |> - ggplot(aes(x = periode, y = referentie, label = klasse)) + - geom_point(aes(colour = klasse), size = 4, show.legend = FALSE) + - scale_colour_manual(values = kleurgradient) + - geom_text( - aes(label = klasse), hjust = 0.5, vjust = 0.5, colour = "white", size = 2 - ) + - coord_fixed() + - theme( - axis.title.x = element_blank(), - axis.text.x = element_text(angle = 90, vjust = 0.5) - ) -``` - -(ref:{{id}}-raster3) Paarsgewijze vergelijking tussen driejarige cycli voor `r soort_naam`. - -```{r {{id}}-wijziging-jaar-alles3, fig.cap = "(ref:{{id}}-raster3)", eval = output_format != "html" && nrow(deze_wijziging) > 0} -deze_wijziging |> - filter(.data$cycle) |> - rename(periode = .data$referentie, referentie = .data$periode) |> - mutate( - klasse = classification(-.data$ucl, -.data$lcl, threshold = deze_threshold) - ) |> - bind_rows( - deze_wijziging |> - filter(.data$cycle) - ) |> - ggplot(aes(x = periode, y = referentie, label = klasse)) + - geom_point(aes(colour = klasse), size = 6, show.legend = FALSE) + - scale_colour_manual(values = kleurgradient) + - geom_text( - aes(label = klasse), - hjust = 0.5, vjust = 0.5, colour = "white" - ) + - scale_x_continuous( - breaks = function(x) { - seq(2007, x[2], by = 3) - }, - labels = function(x) { - sprintf("%02i-%02i", x, x + 2) - } - ) + - scale_y_continuous( - breaks = function(x) { - seq(2007, x[2], by = 3) - }, - labels = function(x) { - sprintf("%02i-%02i", x, x + 2) - } - ) + - coord_fixed() + - theme( - axis.title.x = element_blank(), - axis.text.x = element_text(angle = 90, vjust = 0.5) - ) -``` - -(ref:{{id}}-hash) Data-hashes van de analyse in het kader van traceerbaarheid (zie §\@ref(s:traceerbaarheid)). - -```{r {{id}}-hash, results = "asis"} -deze_analyse |> - arrange(.data$cycle, .data$linear) |> - transmute( - frequentie = factor( - .data$cycle, levels = c(TRUE, FALSE), - labels = c("driejaarlijks", "jaarlijks") - ), - model = ifelse(.data$linear, "lineair", "niet-lineair"), - analyse = str_replace(.data$analysis, "(.{20})", "\\1 "), - status = str_replace(.data$status_fingerprint, "(.{20})", "\\1 ") - ) |> - pandoc.table(caption = "(\\#tab:{{id}}-hash) (ref:{{id}}-hash)") -``` - -```{js {{id}}-change-js, eval = opts_knit$get("rmarkdown.pandoc.to") == "html"} -function filter_default() { - document.getElementById("ref_jaar").getElementsByClassName("selectized")[0].selectize.removeOption(""); - document.getElementById("ref_jaar").getElementsByClassName("selectized") -[0].selectize.setValue("2007", false); - document.getElementById("ref_cyclus").getElementsByClassName("selectized")[0].selectize.removeOption(""); - document.getElementById("ref_cyclus").getElementsByClassName("selectized") -[0].selectize.setValue("2007-2009", false); - } -$(document).ready(filter_default); -``` diff --git a/inst/website/_soort.Rmd b/inst/website/_soort.Rmd deleted file mode 100644 index 8d74bad..0000000 --- a/inst/website/_soort.Rmd +++ /dev/null @@ -1,459 +0,0 @@ -```{r {{id}}-set} -if (interactive()) { - meta |> - filter(!.data$composite, .data$status == "converged") |> - sample_n(1) |> - pull(.data$hash) -> id -} else { - id <- "{{id}}" -} -``` - -```{r {{id}}-basis} -meta |> - filter(.data$hash == id) -> deze_analyse -deze_analyse |> - select("analysis", "cycle") |> - inner_join(x = estimate, by = "analysis") |> - filter( - !.data$cycle | .data$year < max(.data$year) | - (.data$year == max(.data$year) & (max(.data$year) - 2007) %% 3 == 2) - ) |> - mutate( - text_median = format_ci( - estimate = .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, link = "log" - ) - ) -> dit_aantal -looptijd <- diff(range(dit_aantal$year)) -linear_trend |> - semi_join(deze_analyse, by = "analysis") -> deze_lineair -deze_analyse |> - slice(1) |> - pull(.data$speciesgroup) -> soort_naam -tolower(soort_naam) |> - str_replace_all(" ", "-") |> - str_remove_all("'") |> - sprintf(fmt = "# %2$s {#soort-%1$s}", soort_naam) -> titel -deze_analyse |> - select("analysis", "cycle") |> - inner_join(x = index, by = "analysis") |> - filter( - !.data$cycle | .data$periode < max(.data$periode) | - (.data$periode == max(.data$periode) & (.data$periode - 2007) %% 3 == 2) - ) -> deze_wijziging -stratum |> - semi_join(deze_analyse, by = "analysis") -> dit_stratum -trend_tekst <- function(x) { - if (x == "stabiel") { - return("een stabiele toestand. De trend blijft beperkt tot") - } - if (x == "onduidelijk") { - return("een onduidelijke toestand. De geschatte trend bedraagt") - } - paste("een", x, "met") -} -``` - -`r titel` - -## Analyse per jaar - -Op basis van jaarlijkse gegevens zien we gemiddeld `r trend_tekst(deze_lineair$tekst[!deze_lineair$cycle])` `r deze_lineair$jaarlijks[!deze_lineair$cycle]` per jaar of `r deze_lineair$looptijd[!deze_lineair$cycle]` over de volledige looptijd van het meetnet. -Deze trend is `r deze_lineair$interpretatie[!deze_lineair$cycle]`. - -(ref:{{id}}-aantal) Evolutie van het gemodelleerde gemiddeld aantal waargenomen dieren op een meetpunt voor `r soort_naam` tijdens de referentieperiode. Zie §\@ref(s:onzekerheid) voor een verklaring van de intervallen. - -```{r {{id}}-aantal, fig.cap = "(ref:{{id}}-aantal)", eval = nrow(dit_aantal) > 0, warning = FALSE} -dit_aantal |> - filter(!.data$cycle) |> - mutate( - sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / 2 - / qnorm(0.95), - estimate = exp(.data$estimate), - aantal = sprintf("%s in %i", .data$text_median, .data$year) - ) |> - ggplot(aes(x = year, y = estimate, label = aantal)) + - stat_fan(aes(link_sd = sd), link = "log") + - scale_y_continuous("gemiddeld aantal dieren per meetput", limits = c(0, NA)) + - theme(axis.title.x = element_blank()) + - ggtitle(soort_naam) -``` - -(ref:{{id}}-index) Wijzigingen tussen jaren voor `r soort_naam`. Zie §\@ref(s:onzekerheid) voor een verklaring van de intervallen en §\@ref(s:trendklasse) voor uitleg over de symbolen en referentielijnen. - -```{r {{id}}-wijziging-jaar-statisch, fig.cap = "(ref:{{id}}-index)", eval = nrow(dit_aantal) > 0 && (interactive() || output_format != "html"), warning = FALSE} -deze_wijziging |> - filter(!.data$cycle, .data$referentie == min(.data$referentie)) |> - mutate(sd = (.data$ucl - .data$lcl) / 2 / qnorm(0.95)) -> jaarlijks -change_breaks(n = 4, extra = c(log(threshold), -log(threshold)))( - c(jaarlijks$lcl, jaarlijks$ucl) -) -> deze_breaks -ggplot(jaarlijks, aes(x = periode, y = estimate)) + - annotate( - geom = "rect", xmin = -Inf, xmax = Inf, ymin = log(threshold), - ymax = -log(threshold), alpha = 0.2, fill = inbo_grijs - ) + - geom_hline(yintercept = 0) + - stat_fan(aes(link_sd = sd), geom = "rect") + - annotate( - geom = "text", x = Inf, y = 0, hjust = 1, vjust = -0.1, - label = "toestand in 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = log(threshold), hjust = 1, vjust = 1, - label = "belangrijke afname t.o.v. 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = -log(threshold), hjust = 1, vjust = 0, - label = "belangrijke toename t.o.v. 2007", colour = inbo_hoofd - ) + - theme(axis.title = element_blank()) + - scale_y_continuous(breaks = deze_breaks, labels = change_labels) -``` - -```{r {{id}}-wijziging-jaar, fig.cap = "(ref:{{id}}-index)", eval = FALSE && nrow(dit_aantal) > 0 && (interactive() || output_format == "html"), warning = FALSE} -deze_wijziging |> - filter(!.data$cycle) -> nadien -nadien |> - mutate( - old = .data$referentie, referentie = .data$periode, periode = .data$old, - estimate = -.data$estimate, old = .data$lcl, lcl = -.data$ucl, - ucl = -.data$old, - interpretatie = classification( - .data$lcl, .data$ucl, threshold = deze_threshold - ) |> - as.character() |> - recode( - "++" = "sterke toename", "+" = "toename", "+~" = "matige toename", - "~" = "stabiel", "-~" = "matige afname", "-" = "afname", - "--" = "sterke afname", "?+" = "mogelijke toename", - "?-" = "mogelijke afname", "?" = "onduidelijk", "R" = "referentie" - ), - wijziging = format_ci( - .data$estimate, lcl = .data$lcl, ucl = .data$ucl, percent = TRUE, - sign = TRUE, change = TRUE, link = "log" - ) - ) -> voordien -voordien |> - bind_rows( - nadien, - nadien |> - distinct(.data$referentie) |> - mutate( - periode = .data$referentie, estimate = 0, lcl = 0, ucl = 0, - wijziging = "", interpretatie = "referentie" - ) - ) -> jaarlijks -range(jaarlijks$periode) + c(-1, 1) -> bereik_x -jaarlijks |> - mutate(periode = .data$periode - 0.5, volgorde = 0) |> - bind_rows( - jaarlijks |> - mutate(volgorde = 0.5), - jaarlijks |> - mutate(periode = .data$periode + 0.499999, volgorde = 1) - ) |> - arrange(.data$referentie, .data$periode, .data$volgorde) |> - transmute( - .data$referentie, .data$periode, .data$estimate, .data$lcl, .data$ucl, - sd = (.data$ucl - .data$lcl) / 2 / qnorm(0.95), - wijziging = sprintf( - "%.0f t.o.v. %i\n%s\n%s", floor(.data$periode), .data$referentie, - .data$wijziging, .data$interpretatie - ) |> - str_replace_all(".*\n\nreferentie", "referentie"), - key = sprintf("%.0f-%i", floor(.data$periode), .data$referentie) - ) |> - group_by(.data$referentie) |> - mutate( - ref_x = rep(bereik_x[2], 3) |> - c(rep(NA, n() - 3)), - ref_y = c(0, deze_threshold, -deze_threshold) |> - c(rep(NA, n() - 3)), - ref_text = c( - "toestand in", "belangrijke afname t.o.v.", "belangrijke toename t.o.v." - ) |> - paste(.data$referentie), - textposition = c("top left", "bottom left") |> - rep(n()) |> - head(n()) - ) |> - SharedData$new(key = ~key) -> index_jaarlijks -c( - index_jaarlijks$data(withFilter = FALSE)$lcl, - index_jaarlijks$data(withFilter = FALSE)$ucl, - deze_threshold, -deze_threshold -) |> - range(na.rm = TRUE) -> bereik -change_breaks(n = 5, extra = c(-deze_threshold, deze_threshold))( - bereik -) -> deze_breaks -bscols( - widths = c(10, 2), - plot_ly(index_jaarlijks, x = ~periode, y = ~estimate) |> - add_fan(sd = ~sd, text = ~wijziging) |> - add_text( - x = ~ref_x, y = ~ref_y, text = ~ref_text, textposition = ~textposition - ) |> - config( - displaylogo = FALSE, - modeBarButtonsToRemove = c( - "autoScale", "hoverCompareCartesian", "lasso2d", "select2d" - ) - ) |> - layout( - title = soort_naam, - xaxis = list(title = list(font = list(size = 0)), range = bereik_x), - yaxis = list( - title = list(font = list(size = 0)), zerolinewidth = 1, - tickvals = deze_breaks, ticktext = change_labels(deze_breaks) - ), - shapes = list( - type = "rect", x0 = 0, x1 = 1, xref = "paper", y0 = deze_threshold, - y1 = -deze_threshold, line = list(width = 0), opacity = 0.1, - fillcolor = "black", layer = "below" - ) - ), - filter_select( - id = "ref_jaar", label = "Referentiejaar", sharedData = index_jaarlijks, - group = ~referentie, multiple = FALSE - ) -) %>% - `class<-`(c(class(.), "htmlwidget")) -``` - -(ref:{{id}}-raster2) Paarsgewijze vergelijking tussen jaren voor `r soort_naam`. Uitleg van de symbolen in tabel \@ref(tab:regels). - -```{r {{id}}-wijziging-jaar-alles2, fig.cap = "(ref:{{id}}-raster2)", eval = output_format != "html" && nrow(deze_wijziging) > 0} -deze_wijziging |> - filter(!.data$cycle) |> - rename(periode = .data$referentie, referentie = .data$periode) |> - mutate( - klasse = classification(-.data$ucl, -.data$lcl, threshold = log(threshold)) - ) |> - bind_rows( - deze_wijziging |> - filter(!.data$cycle) - ) |> - ggplot(aes(x = periode, y = referentie, label = klasse)) + - geom_point(aes(colour = klasse), size = 4, show.legend = FALSE) + - scale_colour_manual(values = kleurgradient) + - geom_text( - aes(label = klasse), hjust = 0.5, vjust = 0.5, colour = "white", size = 2 - ) + - coord_fixed() + - theme( - axis.title.x = element_blank(), - axis.text.x = element_text(angle = 90, vjust = 0.5) - ) -``` - -## Analyse per driejaarlijkse cyclus - -Op basis van driejaarlijkse gegevens zien we gemiddeld `r trend_tekst(deze_lineair$tekst[deze_lineair$cycle])` `r deze_lineair$jaarlijks[deze_lineair$cycle]` per jaar of `r deze_lineair$looptijd[deze_lineair$cycle]` over de volledige looptijd van het meetnet. -Deze trend is `r deze_lineair$interpretatie[deze_lineair$cycle]`. - -```{r {{id}}-aantal-cyclus, fig.cap = "(ref:{{id}}-aantal)", eval = nrow(dit_aantal) > 0, warning = FALSE} -dit_aantal |> - filter(.data$cycle) |> - mutate( - sd = (.data$upper_confidence_limit - .data$lower_confidence_limit) / 2 - / qnorm(0.95), - estimate = exp(.data$estimate) - ) -> cyclus_aantal -ggplot(cyclus_aantal, aes(x = year, y = estimate)) + - stat_fan(aes(link_sd = sd), link = "log", geom = "rect") + - scale_x_continuous(breaks = cyclus_aantal$year, labels = periode_labels) + - scale_y_continuous("gemiddeld aantal dieren per meetput", limits = c(0, NA)) + - theme(axis.title.x = element_blank()) + - ggtitle(soort_naam) -``` - -(ref:{{id}}-index3) Wijzigingen per driejarige cyclus voor `r soort_naam`. Zie §\@ref(s:onzekerheid) voor een verklaring van de intervallen en §\@ref(s:trendklasse) voor uitleg over de symbolen en referentielijnen. - -```{r {{id}}-wijziging-cyclus-statisch, fig.cap = "(ref:{{id}}-index3)", eval = nrow(dit_aantal) > 0 && (interactive() || output_format != "html"), warning = FALSE} -deze_wijziging |> - filter(.data$cycle, .data$referentie == min(.data$referentie)) |> - mutate(sd = (.data$ucl - .data$lcl) / 2 / qnorm(0.95)) -> jaarlijks -change_breaks(n = 4, extra = c(log(threshold), -log(threshold)))( - c(jaarlijks$lcl, jaarlijks$ucl) -) -> deze_breaks -ggplot(jaarlijks, aes(x = periode, y = estimate)) + - annotate( - geom = "rect", xmin = -Inf, xmax = Inf, ymin = log(threshold), - ymax = -log(threshold), alpha = 0.2, fill = inbo_grijs - ) + - geom_hline(yintercept = 0) + - stat_fan(aes(link_sd = sd), geom = "rect") + - annotate( - geom = "text", x = Inf, y = 0, hjust = 1, vjust = -0.1, - label = "toestand in 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = log(threshold), hjust = 1, vjust = 1, - label = "belangrijke afname t.o.v. 2007", colour = inbo_hoofd - ) + - annotate( - geom = "text", x = Inf, y = -log(threshold), hjust = 1, vjust = 0, - label = "belangrijke toename t.o.v. 2007", colour = inbo_hoofd - ) + - theme(axis.title = element_blank()) + - scale_x_continuous(breaks = jaarlijks$periode, labels = periode_labels) + - scale_y_continuous(breaks = deze_breaks, labels = change_labels) -``` - -```{r {{id}}-wijziging-cyclus, fig.cap = "(ref:{{id}}-index3)", eval = nrow(deze_wijziging) > 0 && (interactive() || output_format == "html")} -deze_wijziging |> - filter(.data$cycle) |> - mutate( - klasse = factor(.data$klasse, levels = c(levels(.data$klasse), "R")), - interpretatie = as.character(.data$interpretatie) - ) |> - complete( - .data$referentie, periode = unique(.data$referentie, .data$periode), - fill = list( - klasse = "R", estimate = 0, interpretatie = "referentie", - wijziging = "n.v.t." - ) - ) |> - mutate( - naar_0 = .data$periode - 1.5, naar_1 = .data$periode + 1.5, - alpha_punt = ifelse(.data$periode < .data$referentie, 0, 1), - alpha_vlak = ifelse(.data$periode < .data$referentie, 0, ribbon_opacity), - klasse = ifelse( - .data$periode < .data$referentie, NA, as.character(.data$klasse) - ), - referentie = periode_labels(.data$referentie), - naar = .data$periode, periode = periode_labels(.data$periode) - ) -> driejaarlijks -if (!interactive() && opts_knit$get("rmarkdown.pandoc.to") != "html") { - driejaarlijks |> - filter(.data$referentie == "2007 - 2009") -> driejaarlijks -} -p <- ggplot( - driejaarlijks, - aes( - x = naar, xmin = naar_0, xmax = naar_1, y = estimate, frame = referentie, - periode = periode, wijziging = wijziging, interpretatie = interpretatie - ) -) + - geom_hline(yintercept = 0) + - geom_hline(yintercept = log(threshold) * c(-1, 1), linetype = 2) + - geom_rect( - aes(ymin = lcl, ymax = ucl, fill = klasse), - alpha = driejaarlijks$alpha_vlak, show.legend = FALSE - ) + - geom_point(size = 6, alpha = driejaarlijks$alpha_punt) + - geom_text(aes(label = klasse), hjust = 0.5, vjust = 0.5, colour = "white") + - scale_x_continuous( - breaks = unique(driejaarlijks$naar), labels = periode_labels - ) + - scale_y_continuous( - "procentuele wijziging t.o.v. referentieperiode", - breaks = change_breaks(n = 2), - labels = change_labels - ) + - scale_fill_manual(values = kleurgradient) + - theme(axis.title.x = element_blank()) -if (interactive() || opts_knit$get("rmarkdown.pandoc.to") == "html") { - ggplotly( - p, - tooltip = c("referentie", "periode", "wijziging", "interpretatie") - ) |> - animation_opts(frame = 1000, transition = 0, redraw = TRUE) |> - animation_button(label = "\u25B6") |> - animation_slider( - len = 0.9, font = list(color = "black"), - currentvalue = list( - prefix = "geselecteerd referentieperiode: ", - font = list(color = inbo_hoofd), xanchor = "center" - ) - ) |> - layout(showlegend = FALSE) |> - plotly::config( - modeBarButtonsToRemove = list( - "lasso2d", "select2d", "autoScale2d", "hoverClosestCartesian", - "hoverCompareCartesian", "toggleSpikelines" - ), - displaylogo = FALSE - ) -} else { - p -} -``` - -(ref:{{id}}-raster3) Paarsgewijze vergelijking tussen driejarige cycli voor `r soort_naam`. Uitleg van de symbolen in tabel \@ref(tab:regels). - -```{r {{id}}-wijziging-jaar-alles3, fig.cap = "(ref:{{id}}-raster3)", eval = output_format != "html" && nrow(deze_wijziging) > 0} -deze_wijziging |> - filter(.data$cycle) |> - rename(periode = .data$referentie, referentie = .data$periode) |> - mutate( - klasse = classification(-.data$ucl, -.data$lcl, threshold = log(threshold)) - ) |> - bind_rows( - deze_wijziging |> - filter(.data$cycle) - ) |> - ggplot(aes(x = periode, y = referentie, label = klasse)) + - geom_point(aes(colour = klasse), size = 6, show.legend = FALSE) + - scale_colour_manual(values = kleurgradient) + - geom_text( - aes(label = klasse), - hjust = 0.5, vjust = 0.5, colour = "white" - ) + - scale_x_continuous( - breaks = function(x) { - seq(2007, x[2], by = 3) - }, - labels = function(x) { - sprintf("%02i-%02i", x, x + 2) - } - ) + - scale_y_continuous( - breaks = function(x) { - seq(2007, x[2], by = 3) - }, - labels = function(x) { - sprintf("%02i-%02i", x, x + 2) - } - ) + - coord_fixed() + - theme( - axis.title.x = element_blank(), - axis.text.x = element_text(angle = 90, vjust = 0.5) - ) -``` - -## Kenmerken van de gegevens - -(ref:{{id}}-sg) Stratumgewicht, raming van het aantal hokken waarin de soort aanwezig is, aantal relevante hokken voor de analyse, aantal onderzochte hokken in het stratum, totaal aantal hokken van het stratum in Vlaanderen, aantal bezoeken aan een meetpunt en het gemiddeld aandeel relevante punten per hok voor `r soort_naam` (zie §\@ref(s:stratumgewicht)). - -```{r {{id}}-sd} -dit_stratum |> - transmute( - .data$stratum, - gewicht = sprintf("%.1f%%", 100 * .data$gewicht), - aanwezig = round(.data$aanwezig, 1), .data$relevant, .data$onderzocht, - .data$totaal, .data$bezoeken, punten = sprintf("%.1f%%", 100 * .data$punten) - ) |> - kable(caption = "(ref:{{id}}-sg)", align = "lrrrrrrr") -``` - -(ref:{{id}}-hash) Data-hashes van de analyses in het kader van traceerbaarheid (zie §\@ref(s:traceerbaarheid)). - -```{r {{id}}-hash, results = "asis"} -deze_analyse |> - arrange(.data$cycle, .data$linear) |> - transmute( - frequentie = factor( - .data$cycle, levels = c(TRUE, FALSE), - labels = c("driejaarlijks", "jaarlijks") - ), - model = ifelse(.data$linear, "lineair", "niet-lineair"), - analyse = str_replace(.data$analysis, "(.{20})", "\\1 "), - status = str_replace(.data$status_fingerprint, "(.{20})", "\\1 ") - ) |> - pandoc.table(caption = "(\\#tab:{{id}}-hash) (ref:{{id}}-hash)") -``` diff --git a/inst/website/cover.pdf b/inst/website/cover.pdf deleted file mode 100644 index 573c913..0000000 Binary files a/inst/website/cover.pdf and /dev/null differ diff --git a/inst/website/download_html.Rmd b/inst/website/download_html.Rmd deleted file mode 100644 index 1e07d04..0000000 --- a/inst/website/download_html.Rmd +++ /dev/null @@ -1,72 +0,0 @@ -## Downloadbaar cijfermateriaal - -Deze sectie bevat links naar het nodige cijfermateriaal om de figuren in dit rapport te maken. -Om de resultaten zo toegankelijk mogelijk te maken, bieden we ze aan als [platte tekstbestanden](https://nl.wikipedia.org/wiki/Platte_tekst). -Elke dataset bestaat uit twee bestanden: - -- een bestand met `.csv` extensie. - Dit bevat de eigenlijke resultaten als kommagescheiden platte tekstformaat. - Je kan dit openen met je favoriete rekenbladprogramma. -- een bestand met `.yml` extensie. - Dit platte tekstbestand bevat metadata over de inhoud van het `.csv` bestand. - Je hebt dit bestand nodig indien de gegevens wilt inlezen met het [`git2rdata`](https://ropensci.github.io/git2rdata) package. - Daarnaast bevat het een data-hash waarmee je de integriteit van het databestand kan nagaan. - Daarom raden we aan om dit bestand toch te downloaden, ook al heb je het strikt genomen niet nodig. - -### Indicatoren - -- **samengestelde indices:** [`samengesteld.csv`](samengesteld.csv) en [`samengesteld.yml`](samengesteld.yml). - - `indicator`: naam van de indicator; - - `frequentie`: index op basis van jaarlijkse of driejaarlijkse gegevens; - - `referentie`: het referentiejaartal; - - `naar`: het jaartal waarvoor we verhouding het referentiejaar berekenen; - - `schatting`: de geschatte verhouding van dit jaar t.o.v. het referentiejaar; - - `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; - - `klasse`: interpretatie van de wijziging (zie §\@ref(s:trendklasse)); - - `wijziging`: tekstuele beschrijving van de evolutie; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **soortenlijst samengestelde indices:** [`samengesteld_soorten.csv`](samengesteld_soorten.csv) en [`samengesteld_soorten.ym`l](samengesteld_soorten.yml). - - `soort`: naam van de soort; - - `indicator`: naam van de samengestelde indicator; - - `ondergrens` en `bovengrens`: grenzen voor de opdelingen van de effecten in klassen (zie §\@ref(s:trendklasse)) - -### Individuele soorten - -- **gemiddeld aantal op een meetpunt:** [`aantallen.csv`](aantallen.csv) en [`aantallen.yml`](aantallen.yml). - - `soort`: Nederlandse soortnaam; - - `frequentie`: trend op basis van jaarlijkse of driejaarlijkse gegevens; - - `jaar`: jaartal of startjaar van de driejaarlijkse periode; - - `schatting`: schatting van het gemiddeld aantal dieren op een meetpunt; - - `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **lineaire trends:** [`lineaire_trend.csv`](lineaire_trend.csv) en [`lineaire_trend.yml`](lineaire_trend.yml). - - `soort`: Nederlandse soortnaam; - - `frequentie`: trend op basis van jaarlijkse of driejaarlijkse gegevens; - - `jaarlijks`: gemiddelde jaarlijkse wijziging (zie §\@ref(s:lineairetrend); - - `looptijd`: gemiddelde wijziging over de looptijd van de tijdsreeks; - - `klasse`: interpretatie van de wijziging (zie §\@ref(s:trendklasse)); - - `verloop`: geeft aan of de trend lineair verloopt; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **indices:** [`index.csv`](index.csv) en [`index.yml`](index.yml). - - `soort`: naam van de indicator; - - `frequentie`: index op basis van jaarlijkse of driejaarlijkse gegevens; - - `referentie`: het referentiejaartal; - - `naar`: het jaartal waarvoor we verhouding het referentiejaar berekenen; - - `schatting`: de geschatte verhouding van dit jaar t.o.v. het referentiejaar; - - `lcl` en `ucl`: de ondergrens en bovengrens van het 90% betrouwbaarheidsinterval rond de schatting; - - `klasse`: interpretatie van de wijziging (zie §\@ref(s:trendklasse)); - - `wijziging`: tekstuele beschrijving van de evolutie; - - `analyse`: data-hash van de analyse; - - `status`: data-hash van de status van de analyse -- **stratumgewichten:** [`stratum_gewicht.csv`](stratum_gewicht.csv) en [`stratum_gewicht.yml`](stratum_gewicht.yml). - - `soort`: Nederlandse soortnaam; - - `stratum`: naam van het stratum; - - `gewicht`: gewicht van het stratum voor deze soort (zie §\@ref(s:stratumgewicht)); - - `aanwezig`: geschatte aantal hokken van het stratum waar de soort aanwezig is; - - `relevant`: aantal relevante hokken voor deze soort in het stratum; - - `onderzocht`: aantal onderzochte hokken in het stratum; - - `totaal`: totaal aantal hokken in het stratum; - - `bezoeken`: totaal aantal bezoeken aan de meetpunten in het stratum; diff --git a/inst/website/download_non_html.Rmd b/inst/website/download_non_html.Rmd deleted file mode 100644 index 3be94ed..0000000 --- a/inst/website/download_non_html.Rmd +++ /dev/null @@ -1,3 +0,0 @@ -## Downloadbaar cijfermateriaal - -We stellen het cijfermateriaal om de figuren te genereren beschikbaar in de online versie van dit rapport: https://doi.org/`r rmarkdown::yaml_front_matter("index.Rmd")$doi`. diff --git a/inst/website/index.Rmd b/inst/website/index.Rmd deleted file mode 100644 index 8a60c9a..0000000 --- a/inst/website/index.Rmd +++ /dev/null @@ -1,325 +0,0 @@ ---- -title: "Trends op basis van de Algemene Broedvogelmonitoring Vlaanderen (ABV)" -subtitle: "Technisch achtergrondrapport voor de periode 2007-2023" -author: - - name: - given : Thierry - family: Onkelinx - email: thierry.onkelinx@inbo.be - orcid: 0000-0001-8804-4216 - affiliation: Research Institute for Nature and Forest (INBO) - corresponding: true - - name: - given: Olivier - family: Dochy - email: olivier.dochy@inbo.be - orcid: 0009-0002-2822-5634 - affiliation: Research Institute for Nature and Forest (INBO) - - name: - given: Glenn - family: Vermeersch - email: glenn.vermeersch@inbo.be - orcid: 0000-0002-9176-8917 - affiliation: Research Institute for Nature and Forest (INBO) - - name: - given: Koen - family: Devos - email: koen.devos@inbo.be - orcid: 0000-0001-7265-6349 - affiliation: Research Institute for Nature and Forest (INBO) -reviewer: - - name: - given: Hans - family: Van Calster - email: hans.vancalster@inbo.be - orcid: 0000-0001-8595-8426 - affiliation: Research Institute for Nature and Forest (INBO) -lang: nl -style: INBO -funder: Research Institute for Nature and Forest (INBO) -rightsholder: Research Institute for Nature and Forest (INBO) -community: inbo -keywords: natuurindicator; trend; broedvogel; citizen science -publication_type: report -reportnr: 15 -year: 2024 -doi: 10.21436/inbor.102669823 -depotnr: D/2024/3241/046 -bibliography: references.bib -link-citations: TRUE -site: bookdown::bookdown_site -cooperation: - - Natuurpunt Studie vzw - - Coxiestraat 11, 2800 Mechelen - - https://www.natuurpunt.be -cooperation_logo: natuurpunt.png -cover_photo: https://img2.vildaphoto.net/asset/l/86103.jpg -cover_description: Putter (_Carduelis carduelis_) eet zaden uit een kaardebol. (© Lars Soerink, Vildaphoto) -cover: cover.pdf -embargo: 2024-03-29 -floatbarrier: section -output: - INBOmd::gitbook: default - INBOmd::pdf_report: default - INBOmd::epub_book: default - bookdown::dontrun: default ---- - -```{r setup, include = FALSE} -library(digest) -library(effectclass) -library(git2rdata) -library(INBOtheme) -library(knitr) -library(pander) -library(plotly) -library(scales) -library(tidyverse) -conflicted::conflicts_prefer(dplyr::pull, plotly::filter, plotly::layout) -opts_chunk$set( - echo = FALSE, eval = TRUE, cache = FALSE, warning = FALSE, error = FALSE, - message = TRUE -) -output_format <- ifelse( - interactive(), "html", opts_knit$get("rmarkdown.pandoc.to") -) -output_dir <- ifelse( - interactive(), file.path("..", "output", "website"), - opts_knit$get("output.dir") -) -switch( - output_format, - html = { - opts_chunk$set(dev = "png", dpi = 72, fig.height = 8, fig.width = 10.5) - update_geom_defaults("point", list(size = 4)) - theme_set(theme_inbo(base_size = 14)) - }, - latex = { - opts_chunk$set(dev = "cairo_pdf") - update_geom_defaults("point", list(size = 2)) - update_geom_defaults("text", list(size = 2)) - theme_set(theme_inbo(base_size = 8)) - }, { - opts_chunk$set(dev = "png", dpi = 72, fig.height = 8, fig.width = 10.5) - update_geom_defaults("point", list(size = 4)) - theme_set(theme_inbo(base_size = 14)) - } -) -strict <- TRUE -ribbon_opacity <- 0.7 -threshold <- 0.75 -``` - -```{r helper-functies} -c(rev(traffic_palette(7)), "grey65", "grey35", "grey50") |> - setNames( - c("++", "+", "+~", "~", "-~", "-", "--", "?+", "?-", "?") - ) -> kleurgradient -kleurgradient[4] <- inbo_steun_blauw -periode_labels <- function(x) { - sprintf("%i - %i", x, x + 2) -} -``` - -```{r document-packages} -package_list <- c("R", "git2rdata", "abvanalysis", "n2kanalysis") -references <- readLines("references.bib") -paste(package_list, collapse = "|") |> - sprintf(fmt = "^@.+\\{(%s),$") |> - grep(references) -> start -end <- grep("^\\}$", references) -lapply( - start, - function(i) { - i:head(end[i < end], 1) - } -) |> - do.call(what = c) -> existing_citation -cite_package <- function(x) { - citation(ifelse(x == "R", "base", x)) |> - toBibtex() |> - str_replace("Manual\\{,", paste0("Manual\\{", x, ",")) -} -package_list |> - map(cite_package) |> - c(references[-existing_citation]) |> - do.call(what = c) |> - writeLines("references.bib") -``` - -```{r import-data} -repo <- system.file("results", package = "abvanalysis") -strata <- read_vc("stratum", root = repo) -effort <- read_vc("effort", root = repo) -read_vc("meta", root = repo) |> - mutate( - speciesgroup = as.character(.data$speciesgroup), - hash = map_chr(.data$speciesgroup, sha1) - ) -> meta -estimate <- read_vc("estimate", root = repo) -moving_trend <- read_vc("moving_trend", root = repo) -composite_moving_trend <- read_vc("composite_moving_trend", root = repo) -parent <- read_vc("parent", root = repo) -tribble( - ~x, ~y, "stabiel", 0, "toename", 1, "afname", -1, "sterke toename", 1.5, - "sterke afname", -1.5, "matige toename", 0.5, "matige afname", -0.5, - "mogelijke toename", 0.5, "mogelijke afname", -0.5, "onduidelijk", 0 -) |> - mutate( - s = c(rep(0.25, 7), 0.5, 0.5, 1), lcl = qnorm(0.05, .data$y, .data$s), - ucl = qnorm(0.95, .data$y, .data$s), - klasse = classification(.data$lcl, .data$ucl, 1) - ) |> - arrange(.data$klasse) |> - mutate( - x = factor(.data$x, levels = rev(.data$x)), x_int = as.integer(.data$x) - ) -> effect -looptijd <- diff(range(estimate$year)) -meta |> - inner_join(read_vc("linear_trend", root = repo), by = "analysis") |> - transmute( - .data$hash, .data$analysis, .data$cycle, - estimate = ifelse(.data$cycle, 1 / 3, 1) * .data$estimate, - lcl = ifelse(.data$cycle, 1 / 3, 1) * .data$lower_confidence_limit, - ucl = ifelse(.data$cycle, 1 / 3, 1) * .data$upper_confidence_limit, - klasse = classification( - .data$lcl * looptijd, .data$ucl * looptijd, log(threshold) - ), - jaarlijks = format_ci( - exp(.data$estimate) - 1, lcl = exp(.data$lcl) - 1, - ucl = exp(.data$ucl) - 1, percent = TRUE, sign = TRUE - ), - looptijd = format_ci( - exp(looptijd * .data$estimate) - 1, lcl = exp(looptijd * .data$lcl) - 1, - ucl = exp(looptijd * .data$ucl) - 1, percent = TRUE, sign = TRUE - ) - ) |> - inner_join( - effect |> - select(.data$klasse, tekst = .data$x), - by = "klasse" - ) |> - inner_join( - meta |> - filter(!.data$composite) |> - select(.data$hash, .data$cycle, .data$linear, .data$waic) |> - pivot_wider(names_from = .data$linear, values_from = .data$waic) |> - transmute( - .data$hash, .data$cycle, - interpretatie = (.data$`FALSE` - .data$`TRUE`) |> - cut( - breaks = c(-Inf, -2, 0, Inf), - labels = c("niet-lineair", "mogelijk niet-lineair", "lineair") - ) - ), - by = c("hash", "cycle") - ) -> linear_trend -read_vc("index", root = repo) |> - transmute( - .data$analysis, referentie = .data$reference, periode = .data$alternative, - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, - wijziging = format_ci( - exp(.data$estimate) - 1, lcl = exp(.data$lower_confidence_limit) - 1, - ucl = exp(.data$upper_confidence_limit) - 1, percent = TRUE, - sign = TRUE - ), - klasse = classification( - .data$lower_confidence_limit, .data$upper_confidence_limit, - threshold = log(threshold) - ) - ) |> - inner_join( - effect |> - select(.data$klasse, interpretatie = .data$x), - by = "klasse" - ) -> index -effort |> - distinct(.data$stratum, .data$location) |> - count(.data$stratum, name = "onderzocht") |> - inner_join( - strata |> - select(stratum = "description", totaal = "n"), - by = "stratum" - ) |> - inner_join(read_vc("stratum_weight", root = repo), by = "stratum") |> - transmute( - .data$analysis, .data$stratum, gewicht = .data$weight, - aanwezig = .data$relevant * .data$totaal / .data$onderzocht, .data$relevant, - .data$onderzocht, .data$totaal, bezoeken = .data$visits, - punten = ( - .data$points_1 / 6 + .data$points_2 / 3 + .data$points_3 / 2 + - .data$points_4 * 2 / 3 + .data$points_5 * 5 / 6 + .data$points_6 - ) / .data$relevant - ) |> - arrange(desc(.data$gewicht)) -> stratum -meta |> - filter(.data$composite, !.data$cycle, !.data$linear) |> - select("speciesgroup", "hash", "analysis") |> - inner_join(parent, by = "analysis") |> - inner_join( - meta |> - select("analysis", soort = "speciesgroup"), - by = c("parent" = "analysis") - ) |> - select("hash", "speciesgroup", "soort") |> - group_by(.data$hash) |> - mutate(drempel = log(threshold) / sqrt(n())) |> - ungroup() -> soortenlijst -read_vc("composite_index", root = repo) |> - inner_join( - meta |> - select("analysis", "hash", "speciesgroup"), - by = "analysis" - ) |> - inner_join( - soortenlijst |> - distinct(.data$hash, .data$drempel), - by = "hash" - ) |> - transmute( - .data$analysis, referentie = .data$reference, periode = .data$alternative, - .data$estimate, lcl = .data$lower_confidence_limit, - ucl = .data$upper_confidence_limit, - wijziging = format_ci( - exp(.data$estimate) - 1, lcl = exp(.data$lower_confidence_limit) - 1, - ucl = exp(.data$upper_confidence_limit) - 1, percent = TRUE, - sign = TRUE - ), - klasse = pmap( - list( - .data$lower_confidence_limit, .data$upper_confidence_limit, - threshold = .data$drempel - ), - classification - ) |> - unlist(), - tmp = as.character(klasse) - ) |> - inner_join( - effect |> - transmute( - tmp = as.character(.data$klasse), interpretatie = .data$x - ), - by = "tmp" - ) |> - select(-"tmp") -> composite_index -``` - -# Samenvatting {-} - -Dit rapport publiceert de meest recente analyses op basis van het telwerk voor het project "Algemene Broedvogelmonitoring Vlaanderen" of kortweg ABV. -Dit project is een gezamenlijk initiatief van het Instituut voor Natuur- en Bosonderzoek (INBO) en Natuurpunt Studie in samenwerking met de lokale vogelwerkgroepen. -De samenwerking tussen deze organisaties staat garant voor een goede ondersteuning van en communicatie naar het vrijwilligersnetwerk door Natuurpunt Studie, en een degelijke professionele wetenschappelijke ondersteuning en dataverwerking door het INBO. -Sinds 2016 maakt het integraal deel uit van het project Soortenmeetnetten, dat door INBO en Agentschap Natuur en Bos (ANB) wordt gefinancierd. - -Het project werd opgestart in 2007 en heeft als doelstelling het beschrijven van aantalsontwikkelingen van een set van ca. 80 algemene broedvogelsoorten in Vlaanderen. -Zowel jaarlijkse schommelingen als meerjarige trends zijn daarbij interessante resultaten in functie van lokaal, regionaal en internationaal beleid. - -Dit rapport is bedoeld als een technisch achtergrondrapport. -Het bestaat uit drie delen: -het eerste deel beschrijft de methodiek van de verwerking en de weergave van de resultaten; -het tweede deel geeft een aantal indicatoren gebaseerd op trends van meerdere soorten; -het derde deel beschrijft de tijdreeks van individuele soorten. -We beperken ons in delen twee en drie tot een gestandaardiseerde weergave van de meest relevante cijfers zonder ecologische interpretatie. -Voor een ecologische interpretatie van de resultaten verwijzen we naar andere publicaties zoals [INBO Vogelnieuws](https://www.vlaanderen.be/inbo/publicaties/vogelnieuws). diff --git a/inst/website/references.bib b/inst/website/references.bib deleted file mode 100755 index 66ef0c3..0000000 --- a/inst/website/references.bib +++ /dev/null @@ -1,101 +0,0 @@ -@Manual{R, - title = {R: A Language and Environment for Statistical Computing}, - author = {{R Core Team}}, - organization = {R Foundation for Statistical Computing}, - address = {Vienna, Austria}, - year = {2024}, - url = {https://www.R-project.org/}, -} -@Manual{git2rdata, - title = {git2rdata: Store and Retrieve Data.frames in a Git Repository. Version 0.4.0}, - author = {Thierry Onkelinx}, - year = {2022}, - url = {https://ropensci.github.io/git2rdata/}, - abstract = {The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette("plain_text", package = "git2rdata"). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette("version_control", package = "git2rdata"). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette("workflow", package = "git2rdata") gives a toy example. 4) vignette("efficiency", package = "git2rdata") provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.}, - keywords = {R package, reproducible research, version control}, -} -@Manual{abvanalysis, - title = {abvanalysis: Analysis of the Common Breeding Bird Survey. Version 0.2.4}, - author = {Thierry Onkelinx}, - year = {2024}, - url = {https://github.com/inbo/abvanalysis/}, - abstract = {Read in the raw data and prepare analysis datasets.}, - keywords = {common breeding birds; monitoring; analysis; reproducible science}, -} -@Manual{n2kanalysis, - title = {n2kanalysis: Generic Functions to Analyse Data from the 'Natura 2000' Monitoring. Version 0.3.2}, - author = {Thierry Onkelinx}, - year = {2024}, - url = {https://github.com/inbo/n2kanalysis/}, - abstract = {All generic functions and classes for the analysis for the 'Natura 2000' monitoring. The classes contain all required data and definitions to fit the model without the need to access other sources. Potentially they might need access to one or more parent objects. An aggregation object might for example need the result of an imputation object. The actual definition of the analysis, using these generic function and classes, is defined in dedictated analysis R packages for every monitoring scheme. For example 'abvanalysis' and 'watervogelanalysis'.}, - keywords = {analysis, reproducible research, natura 2000, monitoring}, - doi = {10.5281/zenodo.3576047}, -} -@TechReport{ABVmethode, - address = {{Brussels, Belgium}}, - title = {Methodehandleiding Bij Het Project '{{Algemene Broedvogelmonitoring Vlaanderen}} ({{ABV}})'}, - number = {2018 93}, - institution = {{Instituut voor Natuur- en Bosonderzoek}}, - author = {Glenn Vermeersch and Hannes Ledegen and Simon Feys}, - year = {2018}, - pages = {12}, - doi = {10.21436/inbor.15674942} -} -@book{BWK, - title = "De biologische waarderingskaart: biotopen en hun verspreiding in vlaanderen en het Brussels Hoofdstedelijk Gewest", - author = "Lieve Vriens and Hans Bosch and {De Knijf}, Geert and {De Saeger}, Steven and Robin Guelinckx and Patrik Oosterlynck and {Van Hove}, Martine and Desiré Paelinckx", - year = "2011", - language = "Nederlands", - series = "Mededelingen van het Instituut voor Natuur- en Bosonderzoek", - publisher = "Instituut voor Natuur- en Bosonderzoek", - number = "INBO.M.2011.1", - address = "België", -} -@Article{gelmanUnderstandingPredictiveInformation2014, - title = {Understanding Predictive Information Criteria for {{Bayesian}} Models}, - volume = {24}, - issn = {15731375}, - abstract = {We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a biascorrected adjustment of within-sample error. We focus on the choices involved in setting up these measures, and we compare them in three simple examples, one theoretical and two applied. The contribution of this review is to put all these information criteria into a Bayesian predictive context and to better understand, through small examples, how these methods can apply in practice.}, - number = {6}, - journal = {Statistics and Computing}, - doi = {10.1007/s11222-013-9416-2}, - author = {Andrew Gelman and Jessica Hwang and Aki Vehtari}, - year = {2014}, - keywords = {AIC,Bayes,Cross-validation,DIC,Prediction,WAIC}, - pages = {997--1016}, -} -@Book{rueINLAFunctionsWhich2009, - title = {{{INLA}}: {{Functions}} Which Allow to Perform Full {{Bayesian}} Analysis of Latent {{Gaussian}} Models Using {{Integrated Nested Laplace Approximation}}}, - author = {Håvard Rue and Sara Martino and Finn Lindgren and Daniel Simpson and Andrea Riebler}, - year = {2009}, -} -@Article{simpson.rue.ea2017PenalisingModelComponent, - title = {Penalising Model Component Complexity: {{A}} Principled, Practical Approach to Constructing Priors}, - volume = {32}, - issn = {0883-4237}, - shorttitle = {Penalising Model Component Complexity}, - language = {English}, - number = {1}, - journal = {Statistical Science}, - doi = {10.1214/16-STS576}, - author = {Daniel P. Simpson and H\aavard Rue and Thiago G. Martins and Andrea Riebler and Sigrunn H. S{\o}rbye}, - month = {feb}, - year = {2017}, - keywords = {INLA}, - pages = {1-28}, - file = {/home/thierry/Zotero/storage/5LQ9Y8FV/Simpson e.a. - 2017 - Penalising model component complexity A principle.pdf;/home/thierry/Zotero/storage/HVDLC23T/penalising-model-component-complexity-a-principled-practical-appr.html}, -} -@Article{britton.fisher.ea1998InflationReportProjections, - title = {The {{Inflation Report}} Projections: Understanding the Fan Chart}, - journal = {Bank of England Quarterly Bulletin}, - author = {Erik Britton and Paul Fisher and John Whitley}, - month = {mar}, - year = {1998}, - pages = {30-37}, -} -@Misc{vermeersch.anselin.ea2018ABVCommonBreeding, - title = {{{ABV}} - {{Common}} Breeding Birds in {{Flanders}}, {{Belgium}}. {{Version}} 1.7}, - publisher = {{Research Institute for Nature and Forest (INBO)}}, - author = {Glenn Vermeersch and Anny Anselin and Mark Herremans and Dimitri Brosens}, - year = {2018}, -} diff --git a/inst/website/website.Rproj b/inst/website/website.Rproj deleted file mode 100644 index cde2e35..0000000 --- a/inst/website/website.Rproj +++ /dev/null @@ -1,18 +0,0 @@ -Version: 1.0 - -RestoreWorkspace: Default -SaveWorkspace: Default -AlwaysSaveHistory: Default - -EnableCodeIndexing: Yes -UseSpacesForTab: Yes -NumSpacesForTab: 2 -Encoding: UTF-8 - -RnwWeave: knitr -LaTeX: XeLaTeX - -AutoAppendNewline: Yes -StripTrailingWhitespace: Yes - -BuildType: Website diff --git a/inst/website/zzz_bibliografietitel.Rmd b/inst/website/zzz_bibliografietitel.Rmd deleted file mode 100755 index d384636..0000000 --- a/inst/website/zzz_bibliografietitel.Rmd +++ /dev/null @@ -1,5 +0,0 @@ -```{r references, results = "asis", echo = FALSE} -# insert the references at this position -# set appendix = FALSE, when the report has no appendix -INBOmd::references(appendix = FALSE) -``` diff --git a/man/copy_manifest_objects.Rd b/man/copy_manifest_objects.Rd new file mode 100644 index 0000000..36896ed --- /dev/null +++ b/man/copy_manifest_objects.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/copy_manifest_objects.R +\name{copy_manifest_objects} +\alias{copy_manifest_objects} +\title{Copy all object of the most recent manifest} +\usage{ +copy_manifest_objects(base, project, target_folder = ".", overwrite = FALSE) +} +\arguments{ +\item{base}{The base location to store the model.} + +\item{project}{Will be a relative path within the base location.} + +\item{target_folder}{Where to store the objects} + +\item{overwrite}{Should an existing object be overwritten? +Defaults to \code{TRUE}.} +} +\description{ +Copy all object of the most recent manifest +} diff --git a/vignettes/analysis.Rmd b/vignettes/analysis.Rmd index bb4ab38..c70dc6a 100644 --- a/vignettes/analysis.Rmd +++ b/vignettes/analysis.Rmd @@ -47,7 +47,7 @@ the common breeding bird census for Flanders (Belgium.) ### Statistical model - Generalised linear mixed model with negative binomial distribution. -- Response: +- Response: - Number of observed breeding bird at each point within the grid cell - Covariates: - `Stratum`: takes the average difference in abundance among strata into account. @@ -55,7 +55,7 @@ the common breeding bird census for Flanders (Belgium.) - `Period`: categorical factor models the overall differences among periods. Only relevant in case multiple periods are selected. - `Location`: i.i.d. random intercept of grid cell. This models effects of the grid cell not captured by `Stratum`, `Period` or `Year`. - `SubLocation`: i.i.d. random intercept of point within grid cell. This models effects of the point within the grid cell not captured by `Stratum`, `Period`, `Year` or `Location`. -- Model fit in [R](https://www.R-project.org) with [INLA](https://www.r-inla.org). +- Model fit in [R](https://www.R-project.org) with [INLA](https://r-inla.org). ### Trend