R/post_rank.R

In pumBayes: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data

#' @title Generate Quantile Ranks for Legislators
#' @description This function calculates quantile ranks for each legislator based on posterior samples of beta parameters from MCMC.
#' The function can handle any specified quantiles, such as median (0.5), and is flexible to support other quantiles provided as input.
#' @param beta A matrix of posterior samples of beta obtained from MCMC, with columns representing legislators.
#' @param quantiles A numeric vector specifying the quantiles to be calculated for the ranks (default is `c(0.5)` for median rank).
#' @return A data frame containing the legislators' names, party affiliations, states, and their ranks at each specified quantile.
#' If the median is included, it will be named `median` in the output. The output data frame is sorted in ascending order based on the values in the median column.
#' @examples
#' data(h116)
#' h116.c = preprocess_rollcall(h116)
#' hyperparams <- list(beta_mean = 0, beta_var = 1, alpha_mean = c(0, 0),
#'                     alpha_scale = 5, delta_mean = c(-2, 10), delta_scale = sqrt(10))
#' control <- list(num_iter = 2, burn_in = 0, keep_iter = 1, flip_rate = 0.1)
#' h116.c.pum <- sample_pum_static(h116.c, hyperparams,
#'                                   control, pos_leg = grep("SCALISE", rownames(h116.c$votes)),
#'                                   verbose = FALSE, pre_run = NULL, appended = FALSE)
#' h116.c.beta.pum.rank = post_rank(beta = h116.c.pum$beta, quantiles = c(0.5))
#' @export

post_rank = function(beta, quantiles = c(0.5)){

  rank_matrix <- apply(beta, 1, function(x) rank(x))
  rank_matrix <- t(rank_matrix)

  rank_quantiles <- apply(rank_matrix, 2, function(x) {
    quantiles_result <- quantile(x, probs = quantiles, names = FALSE)
    rounded_result <- round(quantiles_result)
    positive_result <- ifelse(rounded_result > 0, rounded_result, NA)
    return(positive_result)
  })
  if (length(quantiles) == 1) {
    rank_quantiles <- t(matrix(rank_quantiles))
  }

  name_vector = colnames(beta)

  rank_data <- data.frame(name = name_vector, t(rank_quantiles))
  colnames(rank_data)[-1] <- quantiles

  rank_data$`0.5` <- rank(rank_data$`0.5`, ties.method = "first")
  rownames(rank_data) = NULL
  return(rank_data)
}