Merge pull request #418 from spsanderson/development

Fixes #417

NAMESPACE

@@ -61,6 +61,7 @@ export(tidy_inverse_weibull)
 export(tidy_kurtosis_vec)
 export(tidy_logistic)
 export(tidy_lognormal)
+export(tidy_mcmc_sampling)
 export(tidy_mixture_density)
 export(tidy_multi_dist_autoplot)
 export(tidy_multi_single_dist)

NEWS.md

@@ -7,6 +7,8 @@ None
 1. Fix #405 - Add function `quantile_normalization()` to normalize data using quantiles.
 2. Fix #409 - Add function `check_duplilcate_rows()` to check for duplicate rows in a data frame.
 3. Fix #414 - Add function `util_chisquare_param_estimate()` to estimate the parameters of the chi-square distribution.
+4. Fix #417 - Add function `tidy_mcmc_sampling()` to sample from a distribution using MCMC.
+This outputs the function sampled data and a diagnostic plot.
 
 ## Minor Fixes and Improvements
 1. Fix #401 - Update `tidy_multi_single_dist()` to respect the `.return_tibble` parameter

R/utils-mcmc-sampling.R

@@ -0,0 +1,110 @@
+#' Tidy MCMC Sampling
+#'
+#' Perform MCMC sampling and return tidy data and a plot.
+#'
+#' @family Utility
+#'
+#' @author Steven P. Sanderson II, MPH
+#'
+#' @description
+#' This function performs Markov Chain Monte Carlo (MCMC) sampling on the input
+#' data and returns tidy data and a plot representing the results.
+#'
+#' @details
+#' The function takes a data vector as input and performs MCMC sampling with the
+#' specified number of simulations. It applies user-defined functions to each
+#' MCMC sample and to the cumulative MCMC samples. The resulting data is
+#' formatted in a tidy format, suitable for further analysis. Additionally, a
+#' plot is generated to visualize the MCMC samples and cumulative statistics.
+#'
+#' @param .x The data vector for MCMC sampling.
+#' @param .fns The function(s) to apply to each MCMC sample. Default is "mean".
+#' @param .cum_fns The function(s) to apply to the cumulative MCMC samples. Default is "cmean".
+#' @param .num_sims The number of simulations. Default is 2000.
+#'
+#' @return A list containing tidy data and a plot.
+#'
+#' @examples
+#' # Generate MCMC samples
+#' set.seed(123)
+#' data <- rnorm(100)
+#' result <- tidy_mcmc_sampling(data, "median", "cmedian", 500)
+#' result
+#'
+#' @rdname tidy_mcmc_sampling
+NULL
+
+#' @name tidy_mcmc_sampling
+#' @export
+tidy_mcmc_sampling <- function(.x, .fns = "mean", .cum_fns = "cmean",
+                               .num_sims = 2000) {
+
+  # Error handling for data argument
+  if (!is.vector(.x)) {
+    rlang::abort(
+      message = "Error: '.x' argument must be a vector.",
+      use_cli_format = TRUE
+    )
+  }
+
+  # Error handling for function arguments
+  if (!exists(.fns)) {
+    rlang::abort(
+      message = "Error: '.fns' argument must be a valid function name. Make sure
+      any necessary libraries are loaded.",
+      use_cli_format = TRUE
+    )
+  }
+
+  if (!exists(.cum_fns)) {
+    rlang::abort(
+      message = "Error: '.cum_fns' argument must be a valid function name. Make sure
+      any necessary libraries are loaded.",
+      use_cli_format = TRUE
+    )
+  }
+
+  # Validate number of simulations
+  nsims <- ifelse(.num_sims > 0, as.integer(.num_sims), 1L)
+
+  fns <- match.fun(.fns)
+  fns_name <- as.character(.fns)
+  cum_fns <- match.fun(.cum_fns)
+  cum_fns_name <- as.character(.cum_fns)
+  nsims <- as.integer(.num_sims)
+  fctr_lvl_nms <- c(
+    paste0(".sample_", fns_name),
+    paste0(".cum_stat_", cum_fns_name)
+  )
+
+  df <- TidyDensity::tidy_bootstrap(.x = .x, .num_sims = nsims) |>
+    dplyr::mutate(.sample = purrr::map(bootstrap_samples, \(x) fns(x))) |>
+    dplyr::select(sim_number, .sample) |>
+    tidyr::unnest(cols = .sample) |>
+    dplyr::rename_with(~paste0(., "_", fns_name), .cols = .sample)
+
+  mcmc_data <- df |>
+    dplyr::mutate(.cum_stat = cum_fns(df[[2]])) |>
+    dplyr::rename_with(~paste0(., "_", cum_fns_name), .cols = .cum_stat) |>
+    tidyr::pivot_longer(-sim_number) |>
+    dplyr::mutate(name = factor(name, levels = fctr_lvl_nms))
+
+  plt <- mcmc_data |>
+    ggplot2::ggplot(ggplot2::aes(x = as.numeric(sim_number), y = value)) +
+    ggplot2::facet_wrap(~name, scales = "free") +
+    ggplot2::geom_line() +
+    ggplot2::labs(
+      y = "Value",
+      x = "Simulation Number",
+      title = "MCMC Sampling"
+    ) +
+    ggplot2::theme_minimal()
+
+  # Return
+  mcmc_list <- list(
+    mcmc_data = mcmc_data,
+    plt = plt
+  )
+
+  return(mcmc_list)
+}