difficult-to-diagnose errors using "difftime" response in a linear model

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.10.8.11
+Version: 0.10.8.12
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -26,6 +26,9 @@
 * `check_outliers()` now properly accept the `percentage_central` argument when
   using the `"mcd"` method.
 
+* Fixed edge cases in `check_collinearity()` and `check_outliers()` for models
+  with response variables of classes `Date`, `POSIXct`, `POSIXlt` or `difftime`.
+
 # performance 0.10.8
 
 ## Changes

R/check_collinearity.R

@@ -403,8 +403,22 @@
 
 
 
 .check_collinearity <- function(x, component, ci = 0.95, verbose = TRUE) {
   v <- insight::get_varcov(x, component = component, verbose = FALSE)
+
+  # sanity check
+  if (is.null(v)) {
+    if (isTRUE(verbose)) {
+      insight::format_alert(
+        paste(
+          sprintf("Could not extract the variance-covariance matrix for the %s component of the model.", component),
+          "Please try to run `vcov(model)`, which may help identifying the problem."
+        )
+      )
+    }
+    return(NULL)
+  }
+
   term_assign <- .term_assignments(x, component, verbose = verbose)
 
   # any assignment found?
@@ -432,10 +446,8 @@
   if (insight::has_intercept(x)) {
     v <- v[-1, -1]
     term_assign <- term_assign[-1]
-  } else {
-    if (isTRUE(verbose)) {
-      insight::format_alert("Model has no intercept. VIFs may not be sensible.")
-    }
+  } else if (isTRUE(verbose)) {
+    insight::format_alert("Model has no intercept. VIFs may not be sensible.")
   }
 
   f <- insight::find_formula(x)

R/check_model.R

@@ -49,7 +49,7 @@
 #'
 #' @details For Bayesian models from packages **rstanarm** or **brms**,
 #' models will be "converted" to their frequentist counterpart, using
 #' [`bayestestR::bayesian_as_frequentist`](https://easystats.github.io/bayestestR/reference/convert_bayesian_as_frequentist.html).
 #' A more advanced model-check for Bayesian models will be implemented at a
 #' later stage.
 #'
@@ -77,7 +77,7 @@
 #' plots are helpful to check model assumptions, they do not necessarily indicate
 #' so-called "lack of fit", e.g. missed non-linear relationships or interactions.
 #' Thus, it is always recommended to also look at
 #' [effect plots, including partial residuals](https://strengejacke.github.io/ggeffects/articles/introduction_partial_residuals.html).
 #'
 #' @section Homogeneity of Variance:
 #' This plot checks the assumption of equal variance (homoscedasticity). The
@@ -192,12 +192,18 @@
       suppressWarnings(.check_assumptions_glm(x, minfo, verbose, ...))
     },
     error = function(e) {
-      NULL
+      e
     }
   )
 
-  if (is.null(ca)) {
-    insight::format_error(paste0("`check_model()` not implemented for models of class `", class(x)[1], "` yet."))
+  if (inherits(ca, c("error", "simpleError"))) {
+    pattern <- "(\n|\\s{2,})"
+    replacement <- " "
+    cleaned_string <- gsub(pattern, replacement, ca$message)
+    insight::format_error(
+      paste("`check_model()` returned following error:", cleaned_string),
+      paste0("\nIf the error message does not help identifying your problem, another reason why `check_model()` failed might be that models of class `", class(x)[1], "` are not yet supported.") # nolint
+    )
   }
 
   # try to find sensible default for "type" argument

R/check_outliers.R

@@ -23,6 +23,7 @@
 #'   'Details'). If a numeric value is given, it will be used as the threshold
 #'   for any of the method run.
 #' @param ID Optional, to report an ID column along with the row number.
+#' @param verbose Toggle warnings.
 #' @param ... When `method = "ics"`, further arguments in `...` are passed
 #' down to [ICSOutlier::ics.outlier()]. When `method = "mahalanobis"`,
 #' they are  passed down to [stats::mahalanobis()]. `percentage_central` can
@@ -309,7 +310,7 @@
 #' group_iris <- datawizard::data_group(iris, "Species")
 #' check_outliers(group_iris)
 #'
 #' @examplesIf require("see") && require("bigutilsr") && require("loo") && require("MASS") && require("ICSOutlier") && require("ICS") && require("dbscan")
 #' \donttest{
 #' # You can also run all the methods
 #' check_outliers(data, method = "all", verbose = FALSE)
@@ -348,6 +349,7 @@
                                    method = c("cook", "pareto"),
                                    threshold = NULL,
                                    ID = NULL,
+                                   verbose = TRUE,
                                    ...) {
   # Check args
   if (all(method == "all")) {
@@ -391,8 +393,23 @@
   # Get data
   my_data <- insight::get_data(x, verbose = FALSE)
 
+  # sanity check for date, POSIXt and difftime variables
+  if (any(vapply(my_data, inherits, FUN.VALUE = logical(1), what = c("Date", "POSIXt", "difftime"))) && verbose) {
+    insight::format_alert(
+      paste(
+        "Date variables are not supported for outliers detection. These will be ignored.",
+        "Make sure any date variables are converted to numeric or factor {.b before} fitting the model."
+      )
+    )
+  }
+
   # Remove non-numerics
-  my_data <- datawizard::data_select(my_data, select = is.numeric)
+  my_data <- datawizard::data_select(my_data, select = is.numeric, verbose = FALSE)
+
+  # check if any data left
+  if (is.null(my_data) || ncol(my_data) == 0) {
+    insight::format_error("No numeric variables found. No data to check for outliers.")
+  }
 
   # Thresholds
   if (is.null(threshold)) {
@@ -409,7 +426,7 @@
     )
   }
 
-  if (!missing(ID)) {
+  if (!missing(ID) && verbose) {
     insight::format_warning(paste0("ID argument not supported for model objects of class `", class(x)[1], "`."))
   }
 
@@ -1175,7 +1192,7 @@
 
   # Isolation Forest
   # if ("iforest" %in% method) {
   #   out <- c(out, .check_outliers_iforest(x, threshold = thresholds$iforest))
   # }
 
   # Local Outlier Factor
@@ -1559,7 +1576,7 @@
     out <- cbind(out, ID.names)
   }
 
   # out$Distance_IQR <- Distance_IQR
 
   out$Distance_IQR <- vapply(as.data.frame(t(Distance_IQR)), function(x) {
     ifelse(all(is.na(x)), NA_real_, max(x, na.rm = TRUE))
@@ -1741,7 +1758,7 @@
   }
 
   # check whether N to p ratio is not too large, else MCD flags too many outliers
   # See #672: This does seem to be a function of the N/p (N = sample size; p =
   # number of parameters) ratio. When it is larger than 10, the % of outliers
   # flagged is okay (in well behaved data). This makes sense: the MCD looks at
   # the cov matrix of subsamples of the data - with high dimensional data, small
@@ -1882,14 +1899,14 @@
 
 
 # .check_outliers_iforest <- function(x, threshold = 0.025) {
 #   out <- data.frame(Row = seq_len(nrow(x)))
 #
 #   # Install packages
 #  insight::check_if_installed("solitude")
 #
 #   # Compute
 #   if (utils::packageVersion("solitude") < "0.2.0") {
 #     iforest <- solitude::isolationForest(x)
 #     out$Distance_iforest <- stats::predict(iforest, x, type = "anomaly_score")
 #   } else if (utils::packageVersion("solitude") == "0.2.0") {
 #     stop(paste("Must update package `solitude` (above version 0.2.0).",

tests/testthat/test-check_collinearity.R

@@ -211,3 +211,10 @@ test_that("check_collinearity, hurdle/zi models w/o zi-formula", {
   )
   expect_equal(out$VIF, c(1.05772, 1.05772, 1.06587, 1.06587), tolerance = 1e-4)
 })
+
+test_that("check_collinearity, invalid data", {
+  skip_if(packageVersion("insight") < "0.19.8.2")
+  dd <- data.frame(y = as.difftime(0:5, units = "days"))
+  m1 <- lm(y ~ 1, data = dd)
+  expect_error(check_collinearity(m1), "Can't extract variance-covariance matrix")
+})

tests/testthat/test-check_model.R

@@ -36,3 +36,10 @@ test_that("`check_outliers()` works if convergence issues", {
   x <- check_outliers(m, verbose = FALSE)
   expect_s3_class(x, "check_outliers")
 })
+
+test_that("`check_model()` for invalid models", {
+  skip_if(packageVersion("insight") < "0.19.8.2")
+  dd <- data.frame(y = as.difftime(0:5, units = "days"))
+  m1 <- lm(y ~ 1, data = dd)
+  expect_error(check_model(m1))
+})

tests/testthat/test-check_outliers.R

@@ -329,3 +329,16 @@ test_that("cook multiple methods which", {
     c("setosa", "versicolor", "virginica")
   )
 })
+
+
+test_that("check_outliers with invald data", {
+  dd <- data.frame(y = as.difftime(0:5, units = "days"))
+  m1 <- lm(y ~ 1, data = dd)
+  expect_error(
+    expect_message(
+      check_outliers(m1),
+      regex = "Date variables are not supported"
+    ),
+    regex = "No numeric variables found"
+  )
+})