Merge pull request #462 from utkarshpawade/warning-message-vs-warn-inform-inconsistency

Standardize PPC messages to warn/inform

Author: jgabry

NEWS.md

@@ -1,5 +1,6 @@
 # bayesplot (development version)
 
+* Use `rlang::warn()` and `rlang::inform()` for selected PPC user messages instead of base `warning()` and `message()`.
 * Standardize input validation errors in `ppc_km_overlay()` and interpolation helpers to use `rlang::abort()` for consistent error handling.
 * Fix assignment-in-call bug in `mcmc_rank_ecdf()` (#).
 * Replaced deprecated `dplyr` and `tidyselect` functions (`top_n`, `one_of`, `group_indices`) with their modern equivalents to ensure future compatibility. (#431)

R/ppc-censoring.R

@@ -126,10 +126,10 @@ ppc_km_overlay <- function(
     abort("`extrapolation_factor` must be greater than or equal to 1.")
   }
   if (extrapolation_factor == 1.2) {
-    message(
+    inform(paste0(
       "Note: `extrapolation_factor` now defaults to 1.2 (20%).\n",
       "To display all posterior predictive draws, set `extrapolation_factor = Inf`."
-    )
+    ))
   }
 
   data <- ppc_data(y, yrep, group = status_y)

R/ppc-loo.R

@@ -202,15 +202,14 @@ ppc_loo_pit_overlay <- function(y,
     )
 
   if (!missing(y) && all(y %in% 0:1)) {
-    warning(
+    warn(paste0(
       "This plot is not recommended for binary data. ",
       "For plots that are more suitable see ",
-      "\nhttps://avehtari.github.io/modelselection/diabetes.html#44_calibration_of_predictions",
-      call. = FALSE
-    )
+      "\nhttps://avehtari.github.io/modelselection/diabetes.html#44_calibration_of_predictions"
+    ))
   }
 
-  message(paste(
+  inform(paste(
     "NOTE: The kernel density estimate assumes continuous observations",
     "and is not optimal for discrete observations."
   ))
@@ -797,7 +796,7 @@ ppc_loo_ribbon <-
   # 1-D Gaussian window filter. This method uses the "reflection method"
   # to estimate these pvalues and helps speed up the code
   if (any(is.infinite(x))) {
-    warning(paste(
+    warn(paste(
       "Ignored", sum(is.infinite(x)),
       "Non-finite PIT values are invalid for KDE boundary correction method"
     ))

R/ppc-test-statistics.R

@@ -449,9 +449,9 @@ Tyrep_label <- function() expression(italic(T)(italic(y)[rep]))
 
 message_if_using_mean <- function(stat) {
   if (is.character(stat) && stat == "mean") {
-    message(
+    inform(paste0(
       "Note: in most cases the default test statistic 'mean' is ",
       "too weak to detect anything of interest."
-    )
+    ))
   }
 }

tests/testthat/test-ppc-loo.R

@@ -59,6 +59,19 @@ test_that("ppc_loo_pit_overlay works with boundary_correction=FALSE", {
   expect_gg(p1)
 })
 
+test_that(".kde_correction warns when PIT values are non-finite", {
+  set.seed(123)
+  pit_vals <- c(stats::runif(500), Inf)
+  expect_warning(
+    out <- .kde_correction(pit_vals, bw = "nrd0", grid_len = 128),
+    "Non-finite PIT values are invalid"
+  )
+  expect_type(out, "list")
+  expect_true(all(c("xs", "bc_pvals") %in% names(out)))
+  expect_equal(length(out$xs), 128)
+  expect_equal(length(out$bc_pvals), 128)
+})
+
 test_that("ppc_loo_pit_qq returns ggplot object", {
   skip_if_not_installed("rstanarm")
   skip_if_not_installed("loo")