serodynamics/R/plot_jags_densitydx.R at d4ab703581a2a6acf2adb1e756e3690a23b4f468 · UCD-SERG/serodynamics · GitHub

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#' @title Density Plot Diagnostics
#' @author Sam Schildhauer
#' @description
#'  plot_jags_dens() takes a [list] output from [serodynamics::run_mod()]
#'  to create density plots for each chain run in the mcmc estimation.
#'  Defaults will produce every combination of antigen/antibody, parameters,
#'  and stratifications, unless otherwise specified.
#'  Antigen/antibody combinations and stratifications will vary by analysis.
#'  The antibody dynamic curve includes the following parameters:
#'  - y0 = baseline antibody concentration
#'  - y1 = peak antibody concentration
#'  - t1 = time to peak
#'  - r = shape parameter
#'  - alpha = decay rate
#' @param data A [list] outputted from run_mod().
#' @param iso Specify [character] string to produce plots of only a
#' specific antigen/antibody combination, entered with quotes. Default outputs
#' all antigen/antibody combinations.
#' @param param Specify [character] string to produce plots of only a
#' specific parameter, entered with quotes. Options include:
#' - `alpha` = posterior estimate of decay rate
#' - `r` = posterior estimate of shape parameter
#' - `t1` = posterior estimate of time to peak
#' - `y0` = posterior estimate of baseline antibody concentration
#' - `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
#' @example inst/examples/examples-plot_jags_densitydx.R

plot_jags_dens <- function(data,
                           iso = unique(data$Iso_type),
                           param = unique(data$Parameter),
                           strat = unique(data$Stratification),
                           id = c("newperson")) {

  attributes_jags <- data[["attributes"]]

  dens_id_list <- list()
  for (h in id) {

    visualize_jags_sub <- data |>
      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)

        visualize_jags_plot <- visualize_jags_plot |>
          dplyr::mutate(Parameter = paste0("iso = ", j, ", parameter = ",
                                           .data$Parameter, ", strat = ",
                                           i),
                        value = log(.data$value))

        visualize_jags_plot <- add_jags_attrs(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
      }
      dens_strat_list[[i]] <- density_out
    }
    dens_id_list[[h]] <- dens_strat_list
  }
  dens_id_list
}