Accept posterior::draws objects in PPC/PPD functions

R/helpers-ppc.R

@@ -58,15 +58,20 @@ validate_y <- function(y) {
 #' Validate predictions (`yrep` or `ypred`)
 #'
 #' Checks that `predictions` is a numeric matrix, doesn't have any NAs, and has
-#' the correct number of columns.
+#' the correct number of columns. If `predictions` is a `posterior::draws`
+#' object it is first coerced to a matrix.
 #'
-#' @param predictions The user's `yrep` or `ypred` object (SxN matrix).
+#' @param predictions The user's `yrep` or `ypred` object (SxN matrix or a
+#'   `posterior::draws` object).
 #' @param `n_obs` The number of observations (columns) that `predictions` should
 #'   have, if applicable.
 #' @return Either throws an error or returns a numeric matrix.
 #' @noRd
 validate_predictions <- function(predictions, n_obs = NULL) {
-  # sanity checks
+  if (posterior::is_draws(predictions)) {
+    predictions <- posterior::as_draws_matrix(predictions)
+    predictions <- unclass(predictions)
+  }
   stopifnot(is.matrix(predictions), is.numeric(predictions))
   if (!is.null(n_obs)) {
     stopifnot(length(n_obs) == 1, n_obs == as.integer(n_obs))

man-roxygen/args-y-yrep.R

@@ -1,8 +1,9 @@
 #' @param y A vector of observations. See **Details**.
 #' @param yrep An `S` by `N` matrix of draws from the posterior (or prior)
-#'   predictive distribution. The number of rows, `S`, is the size of the
-#'   posterior (or prior) sample used to generate `yrep`. The number of columns,
-#'   `N` is the number of predicted observations (`length(y)`). The columns of
-#'   `yrep` should be in the same order as the data points in `y` for the plots
-#'   to make sense. See the **Details** and **Plot Descriptions** sections for
-#'   additional advice specific to particular plots.
+#'   predictive distribution, or a [`posterior::draws`] object. The number of
+#'   rows, `S`, is the size of the posterior (or prior) sample used to generate
+#'   `yrep`. The number of columns, `N` is the number of predicted observations
+#'   (`length(y)`). The columns of `yrep` should be in the same order as the
+#'   data points in `y` for the plots to make sense. See the **Details** and
+#'   **Plot Descriptions** sections for additional advice specific to particular
+#'   plots.

man-roxygen/args-ypred.R

@@ -1,4 +1,5 @@
 #' @param ypred An `S` by `N` matrix of draws from the posterior (or prior)
-#'   predictive distribution. The number of rows, `S`, is the size of the
-#'   posterior (or prior) sample used to generate `ypred`. The number of
-#'   columns, `N`, is the number of predicted observations.
+#'   predictive distribution, or a [`posterior::draws`] object. The number of
+#'   rows, `S`, is the size of the posterior (or prior) sample used to generate
+#'   `ypred`. The number of columns, `N`, is the number of predicted
+#'   observations.

tests/testthat/test-helpers-ppc.R

@@ -113,6 +113,85 @@ test_that("get_interpolation_values catches impossible values", {
   )
 })
 
+# validate_predictions with posterior::draws objects ----------------------
+test_that("validate_predictions accepts draws objects", {
+  result <- validate_predictions(posterior::as_draws_matrix(yrep), ncol(yrep))
+  expect_true(is.matrix(result))
+  expect_equal(dim(result), dim(yrep))
+  expect_true(is.numeric(result))
+
+  result <- validate_predictions(posterior::as_draws_array(yrep))
+  expect_true(is.matrix(result))
+  expect_equal(dim(result), dim(yrep))
+
+  result <- validate_predictions(posterior::as_draws_df(yrep))
+  expect_true(is.matrix(result))
+  expect_equal(dim(result), dim(yrep))
+
+  result <- validate_predictions(posterior::as_draws_list(yrep))
+  expect_true(is.matrix(result))
+  expect_equal(dim(result), dim(yrep))
+
+  result <- validate_predictions(posterior::as_draws_rvars(yrep))
+  expect_true(is.matrix(result))
+  expect_equal(dim(result), dim(yrep))
+})
+
+
+draws_arr <- posterior::bind_draws(
+  posterior::as_draws_array(matrix(rnorm(1000), nrow = 10, ncol = 100, dimnames = list(NULL, paste0("V", 1:100)))),
+  posterior::as_draws_array(matrix(rnorm(1000), nrow = 10, ncol = 100, dimnames = list(NULL, paste0("V", 1:100)))),
+  posterior::as_draws_array(matrix(rnorm(1000), nrow = 10, ncol = 100, dimnames = list(NULL, paste0("V", 1:100)))),
+  along = "chain"
+)
+
+test_that("validate_predictions merges chains from multi-chain draws objects", {
+  # 10 iterations x 3 chains x 100 variables -> 30 x 100 matrix
+  result <- validate_predictions(draws_arr)
+  expect_equal(nrow(result), 30)
+  expect_equal(ncol(result), 100)
+
+  result <- validate_predictions(posterior::as_draws_df(draws_arr))
+  expect_equal(nrow(result), 30)
+  expect_equal(ncol(result), 100)
+
+  result <- validate_predictions(posterior::as_draws_list(draws_arr))
+  expect_equal(nrow(result), 30)
+  expect_equal(ncol(result), 100)
+
+  result <- validate_predictions(posterior::as_draws_rvars(draws_arr))
+  expect_equal(nrow(result), 30)
+  expect_equal(ncol(result), 100)
+})
+
+test_that("posterior::draws input results in identical ggplot data", {
+  # comparing to regular yrep
+  p0 <- ggplot2::ggplot_build(ppc_dens_overlay(y, yrep))
+  p1 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_matrix(yrep)))
+  p2 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_array(yrep)))
+  p3 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_df(yrep)))
+  p4 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_list(yrep)))
+  p5 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_rvars(yrep)))
+  expect_identical(p1@data, p0@data)
+  expect_identical(p2@data, p0@data)
+  expect_identical(p3@data, p0@data)
+  expect_identical(p4@data, p0@data)
+  expect_identical(p5@data, p0@data)
+
+  # comparing to converted draws_arr
+  p1 <- ggplot2::ggplot_build(ppc_dens_overlay(y, draws_arr))
+  p2 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_matrix(draws_arr)))
+  p3 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_df(draws_arr)))
+  p4 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_list(draws_arr)))
+  p5 <- ggplot2::ggplot_build(ppc_dens_overlay(y, posterior::as_draws_rvars(draws_arr)))
+  expect_identical(p2@data, p1@data)
+  expect_identical(p3@data, p1@data)
+  expect_identical(p4@data, p1@data)
+  expect_identical(p5@data, p1@data)
+})
+
+
+
 # ecdf_intervals ---------------------------------------------------------
 test_that("ecdf_intervals returns right dimensions and values", {
   lims <- ecdf_intervals(.0001, N = 100, K = 100, L = 1)