R/SSVS_MI.R

In sabainter/SSVS: Functions for Stochastic Search Variable Selection (SSVS)

#' Perform SSVS on Multiply Imputed Datasets
#'
#' This function performs Stochastic Search Variable Selection (SSVS) analysis on multiply imputed datasets
#' for a given set of predictors and a response variable. It supports continuous response variables and calculates
#' aggregated results across multiple imputations.
#'
#' @param data A dataframe containing the variables of interest, including an `.imp` column for imputation identifiers.
#' @param y The response variable (character string).
#' @param x A vector of predictor variable names.
#' @param imp The imputation variable.
#' @param imp_num The number of imputations to process (default is 5).
#' @param interval Confidence interval level for summary results (default is 0.9).
#' @param continuous If `TRUE`, treat the response variable as continuous. If
#' `FALSE`, treat the response variable as binary.
#' @param progress Logical indicating whether to display progress (default is FALSE).
#'
#' @examples
#' \donttest{
#' # example 1: continuous response variable
#' data(imputed_mtcars)
#' outcome <- 'qsec'
#' predictors <- c('cyl', 'disp', 'hp', 'drat', 'wt', 'vs', 'am', 'gear', 'carb','mpg')
#' imputation <- '.imp'
#' results <- ssvs_mi(data = imputed_mtcars, y = outcome, x = predictors, imp = imputation)
#'
#' # example 2: binary response variable
#' data(imputed_affairs)
#' outcome <- "hadaffair"
#' predictors <- c("gender", "age", "yearsmarried", "children", "religiousness",
#' "education", "occupation", "rating")
#' imputation <- '.imp'
#' results <- ssvs_mi(data = imputed_affairs, x = predictors, y = outcome, continuous = FALSE, imp = imputation)
#' }
#' @return An ssvs_mi object containing aggregated results across imputations that can be
#' used in [`summary()`][`summary.ssvs_mi()`].
#' @export
ssvs_mi <- function(data, y, x, imp, imp_num = 5,
                    interval = 0.9, continuous = TRUE, progress = FALSE) {
  checkmate::assert_data_frame(data, min.rows = 1)
  checkmate::assert_character(y, len = 1)
  checkmate::assert_character(x, min.len = 1)
  checkmate::assert_character(imp, min.len = 1)
  checkmate::assert_logical(continuous, len = 1)
  checkmate::assert_logical(progress, len = 1)

  final_results <- data.frame(Variables = x)

  for (i in 1:imp_num) {
    ind <- data[, imp]
    temp <- data[which(ind==i), ]
    results <- SSVS::ssvs(data = temp, x = x, y = y, continuous = continuous, progress = progress)
    summary_results <- summary(results, interval = interval, ordered = FALSE)
    final_results <- merge(final_results, summary_results[, c('MIP', 'Avg Beta', 'Avg Nonzero Beta')], by = 0, all = TRUE, sort = FALSE) [-1]
    names(final_results)[names(final_results) == "MIP"] <- paste(i, "MIP")
    names(final_results)[names(final_results) == "Avg Beta"] <- paste(i, "Avg Beta")
    names(final_results)[names(final_results) == 'Avg Nonzero Beta'] <- paste(i,'Avg Nonzero Beta')
  }

  class(final_results) <- c("ssvs_mi", class(final_results))
  attr(final_results, "response") <- y

  final_results
}