Data object update and diagnostics fix

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: serodynamics
 Title: What the Package Does (One Line, Title Case)
-Version: 0.0.0.9033
+Version: 0.0.0.9034
 Authors@R: c(
     person("Peter", "Teunis", , "p.teunis@emory.edu", role = c("aut", "cph"),
            comment = "Author of the method and original code."),

NEWS.md

@@ -4,6 +4,7 @@
 
 ## New features
 
+* Replacing old data object with new run_mod output (#102)
 * Adding class assignment to run_mod output (#76)
 * Making prep_priors modifiable (#78)
 * Changes to `run_mod()` output:

R/nepal_sees_jags_output.R

@@ -8,15 +8,25 @@
 #' which is the diagnosis type (typhoid or
 #' paratyphoid). Keeping only IDs `"newperson"`, `"sees_npl_1"`, `"sees_npl_2"`.
 #'
-#' @format ## `nepal_sees_jags_output`
-#' A [list] consisting of the following named elements:
-#' \describe{
-#'  \item{curve_params}{A [data.frame] titled `curve_params` that contains the
+#' @format An S3 object of class `sr_model`: A [dplyr::tbl_df] that contains the
 #'   posterior predictive distribution of the person-specific parameters for a
 #'   "new person" with no observed data (`Subject = "newperson"`) and posterior
 #'   distributions of the person-specific parameters for two arbitrarily-chosen
-#'   subjects (`"sees_npl_1"` and `"sees_npl_2"`)}
-#'  \item{attributes}{A [list] of `attributes` that summarize the jags inputs}
+#'   subjects (`"sees_npl_1"` and `"sees_npl_2"`).
+#'   Contains 40,000 `rows`, 7 `columns`, and model `attributes`.
+#' \describe{
+#'  \item{Iteration}{Number of sampling iterations: 500 iterations}
+#'  \item{Chain}{Number of MCMC chains run: 2 chains run}
+#'  \item{Parameter}{Parameter being estimated}
+#'  \item{Iso_type}{Antibody/antigen type combination being evaluated:
+#'  `HlyE_IgA` and `HlyE_IgG`}
+#'  \item{Stratification}{The variable used to stratify jags model: `typhi` and
+#'  `paratyphi`}
+#'  \item{Subject}{ID of subject being evaluated: `newperson`, `sees_npl_1`, 
+#'  `sees_npl_2`}
+#'  \item{value}{Estimated value of the parameter}
+#'  \item{attributes}{A [list] of `attributes` that summarize the jags inputs, 
+#'  priors, and optional jags_post mcmc object}
 #' }
 #' @source reference study: <https://doi.org/10.1016/S2666-5247(22)00114-8>
 "nepal_sees_jags_output"

R/plot_jags_densitydx.R

@@ -32,16 +32,16 @@
 #' @example inst/examples/examples-plot_jags_densitydx.R
 
 plot_jags_dens <- function(data,
-                           iso = unique(data$curve_params$Iso_type),
-                           param = unique(data$curve_params$Parameter_sub),
-                           strat = unique(data$curve_params$Stratification)) {
-  visualize_jags <- data[["curve_params"]]
+                           iso = unique(data$Iso_type),
+                           param = unique(data$Parameter),
+                           strat = unique(data$Stratification)) {
+
   attributes_jags <- data[["attributes"]]
 
   dens_strat_list <- list()
   for (i in strat) {
 
-    visualize_jags_sub <- visualize_jags |>
+    visualize_jags_sub <- data |>
       dplyr::filter(.data$Stratification == i) |>
       dplyr::filter(.data$Subject == "newperson")
 
@@ -55,12 +55,12 @@ plot_jags_dens <- function(data,
       # Will not loop through parameters, as we may want each to show on the
       # same plot by default.
       visualize_jags_plot <- visualize_jags_plot |>
-        dplyr::filter(.data$Parameter_sub %in% param)
+        dplyr::filter(.data$Parameter %in% param)
 
       visualize_jags_plot <- visualize_jags_plot |>
         # Changing parameter name to reflect the input
         dplyr::mutate(Parameter = paste0("iso = ", j, ", parameter = ",
-                                         .data$Parameter_sub, ", strat = ",
+                                         .data$Parameter, ", strat = ",
                                          i),
                       value = log(.data$value))
       # Assigning attributes, which are needed to run ggs_density

R/plot_jags_effectivedx.R

@@ -36,16 +36,16 @@
 
 
 plot_jags_effect <- function(data,
-                             iso = unique(data$curve_params$Iso_type),
-                             param = unique(data$curve_params$Parameter_sub),
-                             strat = unique(data$curve_params$Stratification)) {
-  visualize_jags <- data[["curve_params"]]
+                             iso = unique(data$Iso_type),
+                             param = unique(data$Parameter),
+                             strat = unique(data$Stratification)) {
+
   attributes_jags <- data[["attributes"]]
 
   eff_strat_list <- list()
   for (i in strat) {
 
-    visualize_jags_sub <- visualize_jags |>
+    visualize_jags_sub <- data |>
       dplyr::filter(.data$Stratification == i) |>
       dplyr::filter(.data$Subject == "newperson")
 
@@ -59,11 +59,11 @@ plot_jags_effect <- function(data,
       # Will not loop through parameters, as we may want each to show on the
       # same plot by default.
       visualize_jags_plot <- visualize_jags_plot |>
-        dplyr::filter(.data$Parameter_sub %in% param)
+        dplyr::filter(.data$Parameter %in% param)
 
       visualize_jags_plot <- visualize_jags_plot |>
         # Changing parameter name to reflect the input
-        dplyr::mutate(Parameter = .data$Parameter_sub)
+        dplyr::mutate(Parameter = .data$Parameter)
       # Assigning attributes, which are needed to run ggs_density
       attributes(visualize_jags_plot) <- c(attributes(visualize_jags_plot),
                                            attributes_jags)

R/plot_jags_rhatdx.R

@@ -35,16 +35,16 @@
 #' @example inst/examples/examples-plot_jags_rhatdx.R
 
 plot_jags_Rhat <- function(data,  # nolint: object_name_linter
-                           iso = unique(data$curve_params$Iso_type),
-                           param = unique(data$curve_params$Parameter_sub),
-                           strat = unique(data$curve_params$Stratification)) {
-  visualize_jags <- data[["curve_params"]]
+                           iso = unique(data$Iso_type),
+                           param = unique(data$Parameter),
+                           strat = unique(data$Stratification)) {
+
   attributes_jags <- data[["attributes"]]
 
   rhat_strat_list <- list()
   for (i in strat) {
 
-    visualize_jags_sub <- visualize_jags |>
+    visualize_jags_sub <- data |>
       dplyr::filter(.data$Stratification == i) |>
       dplyr::filter(.data$Subject == "newperson")
 
@@ -58,11 +58,11 @@ plot_jags_Rhat <- function(data,  # nolint: object_name_linter
       # Will not loop through parameters, as we may want each to show on the
       # same plot by default.
       visualize_jags_plot <- visualize_jags_plot |>
-        dplyr::filter(.data$Parameter_sub %in% param)
+        dplyr::filter(.data$Parameter %in% param)
 
       visualize_jags_plot <- visualize_jags_plot |>
         # Changing parameter name to reflect the input
-        dplyr::mutate(Parameter = .data$Parameter_sub,
+        dplyr::mutate(Parameter = .data$Parameter,
                       value = log(.data$value))
       # Assigning attributes, which are needed to run ggs_rhat
       attributes(visualize_jags_plot) <- c(attributes(visualize_jags_plot),

R/plot_jags_tracedx.R

@@ -32,16 +32,16 @@
 #' @example inst/examples/examples-plot_jags_tracedx.R
 
 plot_jags_trace <- function(data,
-                            iso = unique(data$curve_params$Iso_type),
-                            param = unique(data$curve_params$Parameter_sub),
-                            strat = unique(data$curve_params$Stratification)) {
-  visualize_jags <- data[["curve_params"]]
+                            iso = unique(data$Iso_type),
+                            param = unique(data$Parameter),
+                            strat = unique(data$Stratification)) {
+
   attributes_jags <- data[["attributes"]]
 
   trace_strat_list <- list()
   for (i in strat) {
 
-    visualize_jags_sub <- visualize_jags |>
+    visualize_jags_sub <- data |>
       dplyr::filter(.data$Stratification == i) |>
       dplyr::filter(.data$Subject == "newperson")
 
@@ -55,12 +55,12 @@ plot_jags_trace <- function(data,
       # Will not loop through parameters, as we may want each to show on the
       # same plot by default.
       visualize_jags_plot <- visualize_jags_plot |>
-        dplyr::filter(.data$Parameter_sub %in% param)
+        dplyr::filter(.data$Parameter %in% param)
 
       visualize_jags_plot <- visualize_jags_plot |>
         # Changing parameter name to reflect the input
         dplyr::mutate(Parameter = paste0("iso = ", j, ", parameter = ",
-                                         .data$Parameter_sub, ", strat = ",
+                                         .data$Parameter, ", strat = ",
                                          i))
       # Assigning attributes, which are needed to run ggs_density
       attributes(visualize_jags_plot) <- c(attributes(visualize_jags_plot),

R/post_summ.R

@@ -34,19 +34,18 @@
 #' post_summ(data = serodynamics::nepal_sees_jags_output)
 
 post_summ <- function(data,
-                      iso = unique(data$curve_params$Iso_type),
-                      param = unique(data$curve_params$Parameter_sub),
-                      strat = unique(data$curve_params$Stratification)) {
-  summarize_jags <- data[["curve_params"]]
+                      iso = unique(data$Iso_type),
+                      param = unique(data$Parameter),
+                      strat = unique(data$Stratification)) {
 
-  summarize_jags <- summarize_jags |>
+  summarize_jags <- data |>
     filter(.data$Iso_type %in% iso) |>
-    filter(.data$Parameter_sub %in% param) |>
+    filter(.data$Parameter %in% param) |>
     filter(.data$Stratification %in% strat)  |>
     dplyr::filter(.data$Subject == "newperson")
 
   summarize_jags <- summarize_jags |>
-    dplyr::group_by(.data$Iso_type, .data$Parameter_sub, 
+    dplyr::group_by(.data$Iso_type, .data$Parameter, 
                     .data$Stratification) |>
     dplyr::summarize(Mean = mean(.data$value), 
                      SD = stats::sd(.data$value), 

data-raw/nepal_sees_jags_output.R

@@ -9,12 +9,11 @@ nepal_sees_jags_output <- run_mod(
   nmc = 500,
   niter = 1000, # Number of iterations
   strat = "bldculres", # Stratification
-  with_post = FALSE,
-  include_subs = TRUE
+  with_post = FALSE
 )
 
 # Filtering to keep only 2 subjects + newperson
-nepal_sees_jags_output$curve_params <- nepal_sees_jags_output$curve_params |>
+nepal_sees_jags_output <- nepal_sees_jags_output |>
   filter(Subject %in% c("newperson", "sees_npl_1", "sees_npl_2"))
 
 usethis::use_data(nepal_sees_jags_output, overwrite = TRUE)

tests/testthat/_snaps/post_summ.md

@@ -4,27 +4,27 @@
       results
     Output
       # A tibble: 20 x 11
-         Iso_type Parameter_sub Stratification    Mean      SD  Median  `2.5%` `25.0%`
-         <chr>    <chr>         <chr>            <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-       1 HlyE_IgA alpha         paratyphi      2.29e-3 3.75e-3 1.27e-3 1.11e-4 5.79e-4
-       2 HlyE_IgA alpha         typhi          3.78e-3 6.16e-3 2.17e-3 3.08e-4 1.12e-3
-       3 HlyE_IgA shape         paratyphi      1.66e+0 2.74e-1 1.61e+0 1.29e+0 1.47e+0
-       4 HlyE_IgA shape         typhi          1.65e+0 3.68e-1 1.57e+0 1.23e+0 1.42e+0
-       5 HlyE_IgA t1            paratyphi      3.85e+0 1.34e+0 3.70e+0 1.86e+0 2.92e+0
-       6 HlyE_IgA t1            typhi          7.62e+0 4.83e+0 6.67e+0 2.26e+0 4.61e+0
-       7 HlyE_IgA y0            paratyphi      2.55e+0 9.07e-1 2.41e+0 1.23e+0 1.87e+0
-       8 HlyE_IgA y0            typhi          2.90e+0 2.92e+0 2.25e+0 5.76e-1 1.52e+0
-       9 HlyE_IgA y1            paratyphi      1.02e+3 6.84e+3 1.54e+2 8.85e+0 5.45e+1
-      10 HlyE_IgA y1            typhi          1.73e+3 7.50e+3 2.69e+2 8.41e+0 9.10e+1
-      11 HlyE_IgG alpha         paratyphi      2.57e-3 2.60e-3 1.74e-3 2.29e-4 8.76e-4
-      12 HlyE_IgG alpha         typhi          1.75e-3 1.69e-3 1.22e-3 2.38e-4 7.06e-4
-      13 HlyE_IgG shape         paratyphi      1.36e+0 2.03e-1 1.32e+0 1.10e+0 1.19e+0
-      14 HlyE_IgG shape         typhi          1.52e+0 3.88e-1 1.42e+0 1.11e+0 1.27e+0
-      15 HlyE_IgG t1            paratyphi      4.78e+0 2.09e+0 4.37e+0 1.93e+0 3.30e+0
-      16 HlyE_IgG t1            typhi          9.76e+0 8.02e+0 7.67e+0 1.87e+0 4.59e+0
-      17 HlyE_IgG y0            paratyphi      1.72e+0 7.66e-1 1.54e+0 7.09e-1 1.19e+0
-      18 HlyE_IgG y0            typhi          2.29e+0 1.94e+0 1.82e+0 3.80e-1 1.09e+0
-      19 HlyE_IgG y1            paratyphi      8.33e+2 2.13e+3 2.88e+2 1.82e+1 1.09e+2
-      20 HlyE_IgG y1            typhi          4.02e+2 6.91e+2 2.21e+2 3.02e+1 1.07e+2
+         Iso_type Parameter Stratification       Mean       SD  Median  `2.5%` `25.0%`
+         <chr>    <chr>     <chr>               <dbl>    <dbl>   <dbl>   <dbl>   <dbl>
+       1 HlyE_IgA alpha     paratyphi         0.00229  3.75e-3 1.27e-3 1.11e-4 5.79e-4
+       2 HlyE_IgA alpha     typhi             0.00378  6.16e-3 2.17e-3 3.08e-4 1.12e-3
+       3 HlyE_IgA shape     paratyphi         1.66     2.74e-1 1.61e+0 1.29e+0 1.47e+0
+       4 HlyE_IgA shape     typhi             1.65     3.68e-1 1.57e+0 1.23e+0 1.42e+0
+       5 HlyE_IgA t1        paratyphi         3.85     1.34e+0 3.70e+0 1.86e+0 2.92e+0
+       6 HlyE_IgA t1        typhi             7.62     4.83e+0 6.67e+0 2.26e+0 4.61e+0
+       7 HlyE_IgA y0        paratyphi         2.55     9.07e-1 2.41e+0 1.23e+0 1.87e+0
+       8 HlyE_IgA y0        typhi             2.90     2.92e+0 2.25e+0 5.76e-1 1.52e+0
+       9 HlyE_IgA y1        paratyphi      1024.       6.84e+3 1.54e+2 8.85e+0 5.45e+1
+      10 HlyE_IgA y1        typhi          1726.       7.50e+3 2.69e+2 8.41e+0 9.10e+1
+      11 HlyE_IgG alpha     paratyphi         0.00257  2.60e-3 1.74e-3 2.29e-4 8.76e-4
+      12 HlyE_IgG alpha     typhi             0.00175  1.69e-3 1.22e-3 2.38e-4 7.06e-4
+      13 HlyE_IgG shape     paratyphi         1.36     2.03e-1 1.32e+0 1.10e+0 1.19e+0
+      14 HlyE_IgG shape     typhi             1.52     3.88e-1 1.42e+0 1.11e+0 1.27e+0
+      15 HlyE_IgG t1        paratyphi         4.78     2.09e+0 4.37e+0 1.93e+0 3.30e+0
+      16 HlyE_IgG t1        typhi             9.76     8.02e+0 7.67e+0 1.87e+0 4.59e+0
+      17 HlyE_IgG y0        paratyphi         1.72     7.66e-1 1.54e+0 7.09e-1 1.19e+0
+      18 HlyE_IgG y0        typhi             2.29     1.94e+0 1.82e+0 3.80e-1 1.09e+0
+      19 HlyE_IgG y1        paratyphi       833.       2.13e+3 2.88e+2 1.82e+1 1.09e+2
+      20 HlyE_IgG y1        typhi           402.       6.91e+2 2.21e+2 3.02e+1 1.07e+2
       # i 3 more variables: `50.0%` <dbl>, `75.0%` <dbl>, `97.5%` <dbl>