Remove deprecated functions and arguments

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.12.4.1
+Version: 0.12.4.2
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -95,7 +95,7 @@ Suggests:
     cplm,
     dagitty,
     dbscan,
-    DHARMa,
+    DHARMa (>= 0.4.7),
     estimatr,
     fixest,
     flextable,
@@ -129,7 +129,7 @@ Suggests:
     nonnest2,
     ordinal,
     parallel,
-    parameters (>= 0.21.6),
+    parameters (>= 0.22.0),
     patchwork,
     pscl,
     psych,

NAMESPACE

@@ -566,7 +566,6 @@ export(check_multimodal)
 export(check_normality)
 export(check_outliers)
 export(check_overdispersion)
-export(check_posterior_predictions)
 export(check_predictions)
 export(check_residuals)
 export(check_singularity)
@@ -602,7 +601,6 @@ export(performance_rmse)
 export(performance_roc)
 export(performance_rse)
 export(performance_score)
-export(posterior_predictive_check)
 export(print_html)
 export(print_md)
 export(r2)

NEWS.md

@@ -1,3 +1,13 @@
+# performance 0.12.5
+
+## Breaking changes
+
+* Deprecated arguments and alias-function-names have been removed.
+
+## Changes
+
+* Increased accuracy for `check_convergence()` for *glmmTMB* models.
+
 # performance 0.12.4
 
 ## Changes

R/check_convergence.R

@@ -32,7 +32,7 @@
 #' how to resolve convergence issues. Another clue might be large parameter
 #' values, e.g. estimates (on the scale of the linear predictor) larger than
 #' 10 in (non-identity link) generalized linear model *might* indicate
 #' [complete separation](https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/).
 #' Complete separation can be addressed by regularization, e.g. penalized
 #' regression or Bayesian regression with appropriate priors on the fixed effects.
 #'
@@ -98,9 +98,10 @@
 
 
 #' @export
-check_convergence.glmmTMB <- function(x, ...) {
+check_convergence.glmmTMB <- function(x, tolerance = 0.001, ...) {
   # https://github.com/glmmTMB/glmmTMB/issues/275
-  isTRUE(x$sdr$pdHess)
+  # https://stackoverflow.com/q/79110546/2094622
+  isTRUE(all.equal(x$fit$convergence, 0, tolerance = tolerance)) && isTRUE(x$sdr$pdHess)
 }
 
 

R/check_heterogeneity_bias.R

@@ -27,7 +27,6 @@
 #' @param nested Logical, if `TRUE`, the data is treated as nested. If `FALSE`,
 #'   the data is treated as cross-classified. Only applies if `by` contains more
 #'   than one variable.
-#' @param group Deprecated. Use `by` instead.
 #'
 #' @seealso
 #' For further details, read the vignette
@@ -44,15 +43,9 @@
 #' iris$ID <- sample(1:4, nrow(iris), replace = TRUE) # fake-ID
 #' check_heterogeneity_bias(iris, select = c("Sepal.Length", "Petal.Length"), by = "ID")
 #' @export
-check_heterogeneity_bias <- function(x, select = NULL, by = NULL, nested = FALSE, group = NULL) {
+check_heterogeneity_bias <- function(x, select = NULL, by = NULL, nested = FALSE) {
   insight::check_if_installed("datawizard", minimum_version = "0.12.0")
 
-  ## TODO: deprecate later
-  if (!is.null(group)) {
-    insight::format_warning("Argument `group` is deprecated and will be removed in a future release. Please use `by` instead.") # nolint
-    by <- group
-  }
-
   if (insight::is_model(x)) {
     by <- insight::find_random(x, split_nested = TRUE, flatten = TRUE)
     if (is.null(by)) {

R/check_predictions.R

@@ -423,26 +423,6 @@ pp_check.glmmTMB   <-
 #' S3method(bayesplot::pp_check, BFBayesFactor)
 
 
-
-# aliases --------------------------
-
-#' @rdname check_predictions
-#' @export
-posterior_predictive_check <- function(object, ...) {
-  .Deprecated("check_predictions()")
-  check_predictions(object, ...)
-}
-
-#' @rdname check_predictions
-#' @export
-check_posterior_predictions <- function(object, ...) {
-  .Deprecated("check_predictions()")
-  check_predictions(object, ...)
-}
-
-
-
-
 # methods -----------------------
 
 

R/performance_aicc.R

@@ -28,7 +28,7 @@
 #' scale. To get back to the original scale, the likelihood of the model is
 #' multiplied by the Jacobian/derivative of the transformation.
 #'
-#' In case it is not possible to return the corrected AIC value, a waring
+#' In case it is not possible to return the corrected AIC value, a warning
 #' is given that the corrected log-likelihood value could not be computed.
 #'
 #' @references

R/simulate_residuals.R

@@ -65,9 +65,10 @@
 #' @export
 simulate_residuals <- function(x, iterations = 250, ...) {
   insight::check_if_installed("DHARMa")
-  # TODO (low priority): Note that DHARMa::simulateResiduals(x, ...) does its own checks for whether
-  # or not the model passed to it is supported, do we want to use this or do our
-  # own checks so we can supply our own error message?
+  # TODO (low priority): Note that DHARMa::simulateResiduals(x, ...) does its
+  # own checks for whether or not the model passed to it is supported, do we
+  # want to use this or do our own checks so we can supply our own error
+  # message?
   if (iterations < 2) {
     insight::format_error("`iterations` must be at least 2.")
   }

tests/testthat/test-check_zeroinflation.R

@@ -111,22 +111,22 @@ test_that("check_zeroinflation, glmmTMB nbinom", {
   skip_if_not_installed("DHARMa")
   skip_on_cran()
 
+  data(Salamanders, package = "glmmTMB")
+  m <- glmmTMB::glmmTMB(
+    count ~ spp + mined + (1 | site),
+    family = glmmTMB::nbinom1(),
+    data = Salamanders
+  )
   set.seed(1234)
-  dat <- DHARMa::createData(sampleSize = 1000)
-  fit <- suppressWarnings(glmmTMB::glmmTMB(
-    observedResponse ~ Environment1 + (1 | group),
-    data = dat,
-    family = glmmTMB::nbinom1()
-  ))
   expect_equal(
-    check_zeroinflation(fit),
+    check_zeroinflation(m),
     structure(
       list(
-        predicted.zeros = 462,
-        observed.zeros = 482L,
-        ratio = 0.95850622406639,
+        predicted.zeros = 389,
+        observed.zeros = 387L,
+        ratio = 1.00635658914729,
         tolerance = 0.1,
-        p.value = 0.776
+        p.value = 0.944
       ),
       class = "check_zi"
     ),