Adding ability to run diagnostic functions on individuals

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: serodynamics
 Title: What the Package Does (One Line, Title Case)
-Version: 0.0.0.9043
+Version: 0.0.0.9044
 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
 
+* Adding `id` parameter to diagnostic functions (#121)
 * Including fitted and residual values as data frame in run_mod output. (#101)
 * Added  `plot_predicted_curve()` with support for faceting by multiple IDs (#68)
 * Replacing old data object with new run_mod output (#102)

R/add_jags_atts.R

@@ -0,0 +1,12 @@
+#' @title Adds attributes
+#' @description
+#'  `add_jags_attrs` adds specified attributes to a [data.frame].
+#' @param df A [data.frame].
+#' @param attrs [attributes] to attach to the [data.frame].
+#' @param df A [data.frame] of the original input dataset.
+#' @returns A [data.frame] with specified [attributes] attached.
+#' @keywords internal
+add_jags_attrs <- function(df, attrs) {
+  attributes(df) <- c(attributes(df), attrs)
+  df
+}

R/plot_jags_densitydx.R

@@ -26,6 +26,9 @@
 #' - `y1` = posterior estimate of peak antibody concentration
 #' @param strat Specify [character] string to produce plots of specific
 #' stratification entered in quotes.
+#' @param id Specify [character] id in a [vector] format to produce plots for
+#' specific individuals. Default is the `newperson` referring to the predictive
+#' distribution.
 #' @return A [base::list()] of [ggplot2::ggplot()] objects producing density
 #' plots for all the specified input.
 #' @export
@@ -34,45 +37,56 @@
 plot_jags_dens <- function(data,
                            iso = unique(data$Iso_type),
                            param = unique(data$Parameter),
-                           strat = unique(data$Stratification)) {
+                           strat = unique(data$Stratification),
+                           id = c("newperson")) {
 
   attributes_jags <- data[["attributes"]]
-
-  dens_strat_list <- list()
-  for (i in strat) {
+  
+  dens_id_list <- list()
+  for (h in id) {
 
     visualize_jags_sub <- data |>
-      dplyr::filter(.data$Stratification == i) |>
-      dplyr::filter(.data$Subject == "newperson")
+      dplyr::filter(.data$Subject == h)
+
+    stratify <- dplyr::intersect(unique(visualize_jags_sub$Stratification), 
+                                 strat)
+
+    dens_strat_list <- list()
+    for (i in stratify) {
+
+      visualize_jags_strat <- visualize_jags_sub |>
+        dplyr::filter(.data$Stratification == i)
+
+      # Creating open list to store ggplots
+      density_out <- list()
+      # Looping through the isos
+      for (j in iso) {
+        visualize_jags_plot <- visualize_jags_strat |>
+          dplyr::filter(.data$Iso_type == j)
+
+        # 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 %in% param)
 
-    # Creating open list to store ggplots
-    density_out <- list()
-    # Looping through the isos
-    for (j in iso) {
-      visualize_jags_plot <- visualize_jags_sub |>
-        dplyr::filter(.data$Iso_type == j)
+        visualize_jags_plot <- visualize_jags_plot |>
+          dplyr::mutate(Parameter = paste0("iso = ", j, ", parameter = ",
+                                           .data$Parameter, ", strat = ",
+                                           i),
+                        value = log(.data$value))
 
-      # 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 %in% param)
+        visualize_jags_plot <- add_jags_attrs(visualize_jags_plot, 
+                                              attributes_jags)
 
-      visualize_jags_plot <- visualize_jags_plot |>
-        # Changing parameter name to reflect the input
-        dplyr::mutate(Parameter = paste0("iso = ", j, ", parameter = ",
-                                         .data$Parameter, ", strat = ",
-                                         i),
-                      value = log(.data$value))
-      # Assigning attributes, which are needed to run ggs_density
-      attributes(visualize_jags_plot) <- c(attributes(visualize_jags_plot),
-                                           attributes_jags)
-      # Creating density plot
-      densplot <- ggmcmc::ggs_density(visualize_jags_plot) +
-        ggplot2::theme_bw() +
-        ggplot2::labs(x = "log(value)")
-      density_out[[j]] <- densplot
+        # Creating density plot
+        densplot <- ggmcmc::ggs_density(visualize_jags_plot) +
+          ggplot2::theme_bw() +
+          ggplot2::labs(x = "log(value)")
+        density_out[[j]] <- densplot
+      }
+      dens_strat_list[[i]] <- density_out
     }
-    dens_strat_list[[i]] <- density_out
+    dens_id_list[[h]] <- dens_strat_list
   }
-  dens_strat_list
+  dens_id_list
 }

R/plot_jags_effectivedx.R

@@ -27,6 +27,9 @@
 #' - `alpha` = posterior estimate of decay rate
 #' @param strat Specify [character] string to produce plots of specific
 #' stratification entered in quotes.
+#' @param id Specify [character] id in a [vector] format to produce plots for
+#' specific individuals. Default is the `newperson` referring to the predictive
+#' distribution.
 #' @return A [list] of [ggplot2::ggplot] objects showing the 
 #' proportion of effective samples taken/total samples taken for all parameter
 #' iso combinations. The estimate with the highest proportion of effective
@@ -38,51 +41,62 @@
 plot_jags_effect <- function(data,
                              iso = unique(data$Iso_type),
                              param = unique(data$Parameter),
-                             strat = unique(data$Stratification)) {
+                             strat = unique(data$Stratification),
+                             id = c("newperson")) {
 
   attributes_jags <- data[["attributes"]]
+
+  eff_id_list <- list()
+  for (h in id) {
+
+    visualize_jags_sub <- data |>
+      dplyr::filter(.data$Subject == h)
 
-  eff_strat_list <- list()
-  for (i in strat) {
+    stratify <- dplyr::intersect(unique(visualize_jags_sub$Stratification), 
+                                 strat)
 
-    visualize_jags_sub <- data |>
-      dplyr::filter(.data$Stratification == i) |>
-      dplyr::filter(.data$Subject == "newperson")
+    eff_strat_list <- list()
+    for (i in stratify) {
+
+      visualize_jags_strat <- visualize_jags_sub |>
+        dplyr::filter(.data$Stratification == i)
 
-    # Creating open list to store ggplots
-    eff_out <- list()
-    # Looping through the isos
-    for (j in iso) {
-      visualize_jags_plot <- visualize_jags_sub |>
-        dplyr::filter(.data$Iso_type == j)
+      # Creating open list to store ggplots
+      eff_out <- list()
+      # Looping through the isos
+      for (j in iso) {
+        visualize_jags_plot <- visualize_jags_strat |>
+          dplyr::filter(.data$Iso_type == j)
 
-      # 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$Parameter %in% param)
+        # Will not loop through parameters, as we may want each to show on the
+        # same plot by default.
+        visualize_jags_plot <- visualize_jags_plot |>
+          dplyr::filter(.data$Parameter %in% param)
 
-      visualize_jags_plot <- visualize_jags_plot |>
-        # Changing parameter name to reflect the input
-        dplyr::mutate(Parameter = .data$Parameter)
-      # Assigning attributes, which are needed to run ggs_density
-      attributes(visualize_jags_plot) <- c(attributes(visualize_jags_plot),
-                                           attributes_jags)
+        visualize_jags_plot <- visualize_jags_plot |>
+          # Changing parameter name to reflect the input
+          dplyr::mutate(Parameter = .data$Parameter)
+        # Assigning attributes, which are needed to run ggs_density
+        visualize_jags_plot <- add_jags_attrs(visualize_jags_plot, 
+                                              attributes_jags)
 
-      # Creating density plot
-      eff <- ggmcmc::ggs_effective(visualize_jags_plot) +
-        ggplot2::theme_bw()  +
-        ggplot2::labs(title = "Effective sample size",
-                      subtitle = plot_title_fun(i, j),
-                      x = "Proportion of effective samples") +
-        ggplot2::scale_y_discrete(limits = c("alpha", "shape", "t1", "y1", 
-                                             "y0"))
-      eff_out[[j]] <- eff
+        # Creating density plot
+        eff <- ggmcmc::ggs_effective(visualize_jags_plot) +
+          ggplot2::theme_bw()  +
+          ggplot2::labs(title = "Effective sample size",
+                        subtitle = plot_title_fun(i, j),
+                        x = "Proportion of effective samples") +
+          ggplot2::scale_y_discrete(limits = 
+                                      unique(visualize_jags_plot$Parameter))
+        eff_out[[j]] <- eff
+      }
+      eff_strat_list[[i]] <- eff_out
     }
-    eff_strat_list[[i]] <- eff_out
+    #Printing only one plot if only one exists.
+    if (sum(lengths(eff_strat_list)) == 1) {
+      eff_strat_list <- eff_strat_list[[1]][[iso]]
+    } 
+    eff_id_list[[h]] <- eff_strat_list
   }
-  #Printing only one plot if only one exists.
-  if (sum(lengths(eff_strat_list)) == 1) {
-    eff_strat_list <- eff_strat_list[[1]][[iso]]
-  } 
-  eff_strat_list
+  eff_id_list
 }

R/plot_jags_rhatdx.R

@@ -29,6 +29,9 @@
 #' - `alpha` = posterior estimate of decay rate
 #' @param strat Specify [character] string to produce plots of specific
 #' stratification entered in quotes.
+#' @param id Specify [character] id in a [vector] format to produce plots for
+#' specific individuals. Default is the `newperson` referring to the predictive
+#' distribution.
 #' @return A [list] of [ggplot2::ggplot] objects producing dotplots with rhat
 #' values for all the specified input.
 #' @export
@@ -37,51 +40,64 @@
 plot_jags_Rhat <- function(data,  # nolint: object_name_linter
                            iso = unique(data$Iso_type),
                            param = unique(data$Parameter),
-                           strat = unique(data$Stratification)) {
+                           strat = unique(data$Stratification),
+                           id = c("newperson")) {
 
   attributes_jags <- data[["attributes"]]
 
-  rhat_strat_list <- list()
-  for (i in strat) {
+  rhat_id_list <- list()
+  for (h in id) {
 
     visualize_jags_sub <- data |>
-      dplyr::filter(.data$Stratification == i) |>
-      dplyr::filter(.data$Subject == "newperson")
+      dplyr::filter(.data$Subject == h)
+
+    stratify <- dplyr::intersect(unique(visualize_jags_sub$Stratification), 
+                                 strat)
+
+    rhat_strat_list <- list()
+    for (i in stratify) {
+
+      visualize_jags_strat <- visualize_jags_sub |>
+        dplyr::filter(.data$Stratification == i)
 
-    # Creating open list to store ggplots
-    rhat_out <- list()
-    # Looping through the isos
-    for (j in iso) {
-      visualize_jags_plot <- visualize_jags_sub |>
-        dplyr::filter(.data$Iso_type == j)
+      # Creating open list to store ggplots
+      rhat_out <- list()
+      # Looping through the isos
+      for (j in iso) {
+        visualize_jags_plot <- visualize_jags_strat |>
+          dplyr::filter(.data$Iso_type == j)
 
-      # 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$Parameter %in% param)
+        # Will not loop through parameters, as we may want each to show on the
+        # same plot by default.
+        visualize_jags_plot <- visualize_jags_plot |>
+          dplyr::filter(.data$Parameter %in% param)
 
-      visualize_jags_plot <- visualize_jags_plot |>
-        # Changing parameter name to reflect the input
-        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),
-                                           attributes_jags)
-      # Creating rhat dotplots
-      rhatplot <- ggmcmc::ggs_Rhat(visualize_jags_plot) +
-        ggplot2::theme_bw() +
-        ggplot2::labs(title = "Rhat value",
-                      subtitle = plot_title_fun(i, j),
-                      x = "Rhat value") +
-        ggplot2::scale_y_discrete(limits = c("alpha", "shape", "t1", "y1", 
-                                             "y0"))
-      rhat_out[[j]] <- rhatplot
+        visualize_jags_plot <- visualize_jags_plot |>
+          # Changing parameter name to reflect the input
+          dplyr::mutate(Parameter = .data$Parameter,
+                        value = log(.data$value))
+        # Assigning attributes, which are needed to run ggs_rhat
+        visualize_jags_plot <- add_jags_attrs(visualize_jags_plot, 
+                                              attributes_jags)
+        # Default order of main parameters
+        param_levels <- c("alpha", "shape", "t1", "y1", "y0")
+        # Creating rhat dotplots
+        rhatplot <- ggmcmc::ggs_Rhat(visualize_jags_plot) +
+          ggplot2::theme_bw() +
+          ggplot2::labs(title = "Rhat value",
+                        subtitle = plot_title_fun(i, j),
+                        x = "Rhat value") +
+          ggplot2::scale_y_discrete(limits = intersect(param_levels,
+                                                       param))
+        rhat_out[[j]] <- rhatplot
+      }
+      rhat_strat_list[[i]] <- rhat_out
     }
-    rhat_strat_list[[i]] <- rhat_out
+    #Printing only one plot if only one exists.
+    if (sum(lengths(rhat_strat_list)) == 1) {
+      rhat_strat_list <- rhat_strat_list[[1]][[iso]]
+    } 
+    rhat_id_list[[h]] <- rhat_strat_list
   }
-  #Printing only one plot if only one exists.
-  if (sum(lengths(rhat_strat_list)) == 1) {
-    rhat_strat_list <- rhat_strat_list[[1]][[iso]]
-  } 
-  rhat_strat_list
+  rhat_id_list
 }