Removed ess_rfun

Author: VisruthSK

R/effective_sample_sizes.R

@@ -63,14 +63,14 @@ relative_eff.array <- function(x, ..., cores = getOption("mc.cores", 1)) {
   S <- prod(dim(x)[1:2]) # posterior sample size = iter * chains
 
   if (cores == 1) {
-    n_eff_vec <- apply(x, 3, ess_rfun)
+    n_eff_vec <- apply(x, 3, posterior::ess_mean)
   } else {
     if (!os_is_windows()) {
       n_eff_list <-
         parallel::mclapply(
           mc.cores = cores,
           X = seq_len(dim(x)[3]),
-          FUN = function(i) ess_rfun(x[, , i, drop = TRUE])
+          FUN = function(i) posterior::ess_mean(x[, , i, drop = TRUE])
         )
     } else {
       cl <- parallel::makePSOCKcluster(cores)
@@ -79,7 +79,7 @@ relative_eff.array <- function(x, ..., cores = getOption("mc.cores", 1)) {
         parallel::parLapply(
           cl = cl,
           X = seq_len(dim(x)[3]),
-          fun = function(i) ess_rfun(x[, , i, drop = TRUE])
+          fun = function(i) posterior::ess_mean(x[, , i, drop = TRUE])
         )
     }
     n_eff_vec <- unlist(n_eff_list, use.names = FALSE)
@@ -192,15 +192,3 @@ psis_n_eff.matrix <- function(w, r_eff = NULL, ...) {
   }
   1 / ss * r_eff
 }
-
-#' MCMC effective sample size calculation
-#'
-#' @noRd
-#' @param sims An iterations by chains matrix of draws for a single parameter.
-#'   In the case of the **loo** package, this will be the **exponentiated**
-#'   log-likelihood values for the ith observation.
-#' @return MCMC effective sample size based on RStan's calculation.
-#'
-ess_rfun <- function(sims) {
-  posterior::ess_basic(sims)
-}

tests/testthat/test_relative_eff.R

@@ -16,7 +16,7 @@ test_that("relative_eff is equal to ESS / S", {
   ess <- r_eff <- rep(NA, dims[3])
   for (j in 1:dims[3]) {
     r_eff[j] <- relative_eff(LLarr[,,1, drop=FALSE])
-    ess[j] <- ess_rfun(LLarr[,,1])
+    ess[j] <- posterior::ess_mean(LLarr[,,1])
   }
   S <- prod(dim(LLarr)[1:2])
   expect_equal(r_eff, ess / S)