Remove pp_check() alias (#706)

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.11.0.2
+Version: 0.11.0.3
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",

NAMESPACE

@@ -113,8 +113,10 @@ S3method(check_overdispersion,performance_simres)
 S3method(check_overdispersion,poissonirr)
 S3method(check_overdispersion,poissonmfx)
 S3method(check_predictions,BFBayesFactor)
+S3method(check_predictions,brmsfit)
 S3method(check_predictions,default)
 S3method(check_predictions,lme)
+S3method(check_predictions,stanreg)
 S3method(check_residuals,DHARMa)
 S3method(check_residuals,default)
 S3method(check_residuals,performance_simres)

NEWS.md

@@ -1,9 +1,20 @@
 # performance 0.11.1
 
+## Breaking
+
+* Aliases `posterior_predictive_check()` and `check_posterior_predictions()` for
+  `check_predictions()` are deprecated.
+
+## General
+
 * Improved documentation and new vignettes added.
 
 * `check_model()` gets a `base_size` argument, to set the base font size for plots. 
 
+* `check_predictions()` for `stanreg` and `brmsfit` models now returns plots in
+  the usual style as for other models and no longer returns plots from
+  `bayesplot::pp_check()`.
+
 # performance 0.11.0
 
 ## New supported models

R/check_predictions.R

@@ -115,27 +115,99 @@ check_predictions.default <- function(object,
   # args
   type <- match.arg(type, choices = c("density", "discrete_dots", "discrete_interval", "discrete_both"))
 
-  if (isTRUE(minfo$is_bayesian) && isFALSE(inherits(object, "BFBayesFactor"))) {
-    insight::check_if_installed(
-      "bayesplot",
-      "to create posterior prediction plots for Stan models"
+  pp_check.lm(
+    object,
+    iterations = iterations,
+    check_range = check_range,
+    re_formula = re_formula,
+    bandwidth = bandwidth,
+    type = type,
+    verbose = verbose,
+    model_info = minfo,
+    ...
+  )
+}
+
+
+#' @export
+check_predictions.stanreg <- function(object,
+                                      iterations = 50,
+                                      check_range = FALSE,
+                                      re_formula = NULL,
+                                      bandwidth = "nrd",
+                                      type = "density",
+                                      verbose = TRUE,
+                                      ...) {
+  # retrieve model information
+  minfo <- insight::model_info(object, verbose = FALSE)
+
+  # try to find sensible default for "type" argument
+  suggest_dots <- (minfo$is_bernoulli || minfo$is_count || minfo$is_ordinal || minfo$is_categorical || minfo$is_multinomial) # nolint
+  if (missing(type) && suggest_dots) {
+    type <- "discrete_interval"
+  }
+
+  # args
+  type <- match.arg(type, choices = c("density", "discrete_dots", "discrete_interval", "discrete_both"))
+
+  # convert to type-argument for pp_check
+  pp_type <- switch(type,
+    density = "dens",
+    "bars"
+  )
+
+  insight::check_if_installed(
+    "bayesplot",
+    "to create posterior prediction plots for Stan models"
+  )
+
+  # for plotting
+  resp_string <- insight::find_terms(object)$response
+
+  if (inherits(object, "brmsfit")) {
+    out <- as.data.frame(bayesplot::pp_check(object, type = pp_type, ndraws = iterations, ...)$data)
+  } else {
+    out <- as.data.frame(bayesplot::pp_check(object, type = pp_type, nreps = iterations, ...)$data)
+  }
+
+  # bring data into shape, like we have for other models with `check_predictions()`
+  if (pp_type == "dens") {
+    d_filter <- out[!out$is_y, ]
+    d_filter <- datawizard::data_to_wide(
+      d_filter,
+      id_cols = "y_id",
+      values_from = "value",
+      names_from = "rep_id"
     )
-    bayesplot::pp_check(object)
+    d_filter$y_id <- NULL
+    colnames(d_filter) <- paste0("sim_", colnames(d_filter))
+    d_filter$y <- out$value[out$is_y]
+    out <- d_filter
   } else {
-    pp_check.lm(
-      object,
-      iterations = iterations,
-      check_range = check_range,
-      re_formula = re_formula,
-      bandwidth = bandwidth,
-      type = type,
-      verbose = verbose,
-      model_info = minfo,
-      ...
+    colnames(out) <- c("x", "y", "CI_low", "Mean", "CI_high")
+    # to long, for plotting
+    out <- datawizard::data_to_long(
+      out,
+      select = c("y", "Mean"),
+      names_to = "Group",
+      values_to = "Count"
     )
   }
+
+  attr(out, "is_stan") <- TRUE
+  attr(out, "check_range") <- check_range
+  attr(out, "response_name") <- resp_string
+  attr(out, "bandwidth") <- bandwidth
+  attr(out, "model_info") <- minfo
+  attr(out, "type") <- type
+  class(out) <- c("performance_pp_check", "see_performance_pp_check", class(out))
+  out
 }
 
+#' @export
+check_predictions.brmsfit <- check_predictions.stanreg
+
+
 #' @export
 check_predictions.BFBayesFactor <- function(object,
                                             iterations = 50,
@@ -347,11 +419,17 @@ pp_check.glmmTMB   <-
 
 #' @rdname check_predictions
 #' @export
-posterior_predictive_check <- check_predictions
+posterior_predictive_check <- function(object, ...) {
+  .Deprecated("check_predictions()")
+  check_predictions(object, ...)
+}
 
 #' @rdname check_predictions
 #' @export
-check_posterior_predictions <- check_predictions
+check_posterior_predictions <- function(object, ...) {
+  .Deprecated("check_predictions()")
+  check_predictions(object, ...)
+}