Check for influential observations of GLM w/o numeric variables

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.12.4.7
+Version: 0.12.4.8
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -13,6 +13,11 @@
 
 * Increased accuracy for `check_convergence()` for *glmmTMB* models.
 
+## Bug fixes
+
+* `check_outliers()` did not warn that no numeric variables were found when only
+  the response variable was numeric, but all relevant predictors were not.
+
 # performance 0.12.4
 
 ## Changes

R/check_outliers.R

@@ -378,42 +378,23 @@ check_outliers.default <- function(x,
   # Check args
   if (all(method == "all")) {
     method <- c(
-      "zscore_robust",
-      "iqr",
-      "ci",
-      "cook",
-      "pareto",
-      "mahalanobis",
-      "mahalanobis_robust",
-      "mcd",
-      "ics",
-      "optics",
-      "lof"
+      "zscore_robust", "iqr", "ci", "cook", "pareto", "mahalanobis",
+      "mahalanobis_robust", "mcd", "ics", "optics", "lof"
     )
   }
 
   method <- match.arg(
     method,
     c(
-      "zscore",
-      "zscore_robust",
-      "iqr",
-      "ci",
-      "hdi",
-      "eti",
-      "bci",
-      "cook",
-      "pareto",
-      "mahalanobis",
-      "mahalanobis_robust",
-      "mcd",
-      "ics",
-      "optics",
-      "lof"
+      "zscore", "zscore_robust", "iqr", "ci", "hdi", "eti", "bci", "cook",
+      "pareto", "mahalanobis", "mahalanobis_robust", "mcd", "ics", "optics", "lof"
     ),
     several.ok = TRUE
   )
 
+  # Get model information
+  m_info <- insight::model_info(x)
+
   # Get data
   my_data <- insight::get_data(x, verbose = FALSE)
 
@@ -427,8 +408,17 @@ check_outliers.default <- function(x,
     )
   }
 
-  # Remove non-numerics
-  my_data <- datawizard::data_select(my_data, select = is.numeric, verbose = FALSE)
+  # Remove non-numerics, but in case of binomial, only check predictors
+  if (m_info$is_binomial) {
+    model_predictors <- unique(insight::find_predictors(x, flatten = TRUE))
+  } else {
+    model_predictors <- colnames(my_data)
+  }
+  my_data <- datawizard::data_select(
+    my_data[model_predictors],
+    select = is.numeric,
+    verbose = FALSE
+  )
 
   # check if any data left
   if (is.null(my_data) || ncol(my_data) == 0) {
@@ -468,7 +458,7 @@ check_outliers.default <- function(x,
   }
 
   # Cook
-  if ("cook" %in% method && !insight::model_info(x)$is_bayesian && !inherits(x, "bife")) {
+  if ("cook" %in% method && !m_info$is_bayesian && !inherits(x, "bife")) {
     data_cook <- .check_outliers_cook(
       x,
       threshold = thresholds$cook
@@ -508,7 +498,7 @@ check_outliers.default <- function(x,
   }
 
   # Pareto
-  if ("pareto" %in% method && insight::model_info(x)$is_bayesian) {
+  if ("pareto" %in% method && m_info$is_bayesian) {
     data_pareto <- .check_outliers_pareto(
       x,
       threshold = thresholds$pareto

tests/testthat/test-check_outliers.R

@@ -361,6 +361,16 @@ test_that("check_outliers with invald data", {
 })
 
 
+test_that("check_outliers on numeric data only", {
+  data(mtcars)
+  # all predictors categorical
+  mtcars$wt <- as.factor(mtcars$wt)
+  mtcars$mpg <- as.factor(mtcars$mpg)
+  model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
+  expect_error(check_outliers(model), regex = "No numeric")
+})
+
+
 test_that("check_outliers with DHARMa", {
   skip_if_not_installed("DHARMa")
   mt1 <- mtcars[, c(1, 3, 4)]