Merge branch 'main' into strengejacke/issue697

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.12.4.7
+Version: 0.12.4.16
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -74,7 +74,7 @@ Depends:
     R (>= 3.6)
 Imports:
     bayestestR (>= 0.15.0),
-    insight (>= 0.20.5),
+    insight (>= 1.0.0),
     datawizard (>= 0.13.0),
     stats,
     utils
@@ -160,4 +160,4 @@ Config/Needs/website:
     r-lib/pkgdown,
     easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/insight
+Remotes: easystats/datawizard, easystats/see, easystats/insight

NAMESPACE

@@ -25,7 +25,8 @@ S3method(as.data.frame,performance_score)
 S3method(as.data.frame,r2_bayes)
 S3method(as.data.frame,r2_loo)
 S3method(as.data.frame,r2_nakagawa)
-S3method(as.numeric,check_outliers)
+S3method(as.double,check_outliers)
+S3method(as.double,performance_roc)
 S3method(check_autocorrelation,default)
 S3method(check_collinearity,BFBayesFactor)
 S3method(check_collinearity,MixMod)
@@ -473,6 +474,7 @@ S3method(r2_mcfadden,clm)
 S3method(r2_mcfadden,clm2)
 S3method(r2_mcfadden,cpglm)
 S3method(r2_mcfadden,glm)
+S3method(r2_mcfadden,glmmTMB)
 S3method(r2_mcfadden,glmx)
 S3method(r2_mcfadden,logitmfx)
 S3method(r2_mcfadden,logitor)

NEWS.md

@@ -2,6 +2,9 @@
 
 ## Breaking changes
 
+* `check_outliers()` with `method = "optics"` now returns a further refined
+  cluster selection, by passing the `optics_xi` argument to `dbscan::extractXi()`.
+
 * Deprecated arguments and alias-function-names have been removed.
 
 * Argument names in `check_model()` that refer to plot-aesthetics (like
@@ -13,6 +16,22 @@
 
 * Increased accuracy for `check_convergence()` for *glmmTMB* models.
 
+* `r2()` and `r2_mcfadden()` now support beta-binomial (non-mixed) models from
+  package *glmmTMB*.
+
+* An `as.numeric()` resp. `as.double()` method for objects of class
+  `performance_roc` was added.
+
+* Improved documentation for `performance_roc()`.
+
+## 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.
+
+* `check_collinearity()` did not work for glmmTMB models when zero-inflation
+  component was set to `~0`.
+
 # performance 0.12.4
 
 ## Changes

R/check_autocorrelation.R

@@ -49,7 +49,6 @@ check_autocorrelation.default <- function(x, nsim = 1000, ...) {
 }
 
 
-
 # methods ------------------------------
 
 #' @export
@@ -81,7 +80,6 @@ print.check_autocorrelation <- function(x, ...) {
 }
 
 
-
 # utilities -------------------------------
 
 .durbWats <- function(.residuals) {

R/check_clusterstructure.R

@@ -75,7 +75,6 @@ check_clusterstructure <- function(x,
 }
 
 
-
 #' @export
 plot.check_clusterstructure <- function(x, ...) {
   # Can be reimplemented with ggplot in see
@@ -88,7 +87,6 @@ plot.check_clusterstructure <- function(x, ...) {
 }
 
 
-
 #' @keywords internal
 .clusterstructure_dm <- function(x, distance = "euclidean", method = "ward.D2") {
   d <- stats::dist(x, method = distance)
@@ -97,7 +95,6 @@ plot.check_clusterstructure <- function(x, ...) {
 }
 
 
-
 #' @keywords internal
 .clusterstructure_hopkins <- function(x, distance = "euclidean") {
   # This is based on the hopkins() function from the clustertend package

R/check_collinearity.R

@@ -73,7 +73,12 @@
 #' This portion of multicollinearity among the component terms of an
 #' interaction is also called "inessential ill-conditioning", which leads to
 #' inflated VIF values that are typically seen for models with interaction
-#' terms _(Francoeur 2013)_.
+#' terms _(Francoeur 2013)_. Centering interaction terms can resolve this
+#' issue _(Kim and Jung 2024)_.
+#'
+#' @section Multicollinearity and Polynomial Terms:
+#' Polynomial transformations are considered a single term and thus VIFs are
+#' not calculated between them.
 #'
 #' @section Concurvity for Smooth Terms in Generalized Additive Models:
 #' `check_concurvity()` is a wrapper around `mgcv::concurvity()`, and can be
@@ -91,26 +96,30 @@
 #' @references
 #'
 #' - Francoeur, R. B. (2013). Could Sequential Residual Centering Resolve
-#' Low Sensitivity in Moderated Regression? Simulations and Cancer Symptom
-#' Clusters. Open Journal of Statistics, 03(06), 24-44.
+#'   Low Sensitivity in Moderated Regression? Simulations and Cancer Symptom
+#'   Clusters. Open Journal of Statistics, 03(06), 24-44.
+#'
+#' - James, G., Witten, D., Hastie, T., and Tibshirani, R. (eds.). (2013). An
+#'   introduction to statistical learning: with applications in R. New York:
+#'   Springer.
 #'
-#' - James, G., Witten, D., Hastie, T., and Tibshirani, R. (eds.). (2013).
-#' An introduction to statistical learning: with applications in R. New York:
-#' Springer.
+#' - Kim, Y., & Jung, G. (2024). Understanding linear interaction analysis with
+#'   causal graphs. British Journal of Mathematical and Statistical Psychology,
+#'   00, 1–14.
 #'
 #' - Marcoulides, K. M., and Raykov, T. (2019). Evaluation of Variance
-#' Inflation Factors in Regression Models Using Latent Variable Modeling
-#' Methods. Educational and Psychological Measurement, 79(5), 874–882.
+#'   Inflation Factors in Regression Models Using Latent Variable Modeling
+#'   Methods. Educational and Psychological Measurement, 79(5), 874–882.
 #'
 #' - McElreath, R. (2020). Statistical rethinking: A Bayesian course with
-#' examples in R and Stan. 2nd edition. Chapman and Hall/CRC.
+#'   examples in R and Stan. 2nd edition. Chapman and Hall/CRC.
 #'
 #' - Vanhove, J. (2019). Collinearity isn't a disease that needs curing.
-#' [webpage](https://janhove.github.io/posts/2019-09-11-collinearity/)
+#'   [webpage](https://janhove.github.io/posts/2019-09-11-collinearity/)
 #'
 #' - Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid
-#' common statistical problems: Data exploration. Methods in Ecology and
-#' Evolution (2010) 1:3–14.
+#'   common statistical problems: Data exploration. Methods in Ecology and
+#'   Evolution (2010) 1:3–14.
 #'
 #' @family functions to check model assumptions and and assess model quality
 #'
@@ -139,7 +148,6 @@ check_collinearity <- function(x, ...) {
 multicollinearity <- check_collinearity
 
 
-
 # default ------------------------------
 
 #' @rdname check_collinearity
@@ -150,7 +158,6 @@ check_collinearity.default <- function(x, ci = 0.95, verbose = TRUE, ...) {
 }
 
 
-
 # methods -------------------------------------------
 
 #' @export
@@ -193,7 +200,7 @@ plot.check_collinearity <- function(x, ...) {
   x <- insight::format_table(x)
   x <- datawizard::data_rename(
     x,
-    pattern = "SE_factor",
+    select = "SE_factor",
     replacement = "Increased SE",
     verbose = FALSE
   )
@@ -218,7 +225,6 @@ plot.check_collinearity <- function(x, ...) {
 }
 
 
-
 # other classes ----------------------------------
 
 #' @export
@@ -294,7 +300,6 @@ check_collinearity.betaor <- check_collinearity.logitor
 check_collinearity.betamfx <- check_collinearity.logitor
 
 
-
 # zi-models -------------------------------------
 
 #' @rdname check_collinearity
@@ -353,7 +358,6 @@ check_collinearity.zerocount <- function(x,
 }
 
 
-
 # utilities ---------------------------------
 
 .check_collinearity_zi_model <- function(x, component, ci = 0.95, verbose = TRUE) {
@@ -403,9 +407,8 @@ check_collinearity.zerocount <- function(x,
 }
 
 
-
 .check_collinearity <- function(x, component, ci = 0.95, verbose = TRUE) {
-  v <- insight::get_varcov(x, component = component, verbose = FALSE)
+  v <- .safe(insight::get_varcov(x, component = component, verbose = FALSE))
 
   # sanity check
   if (is.null(v)) {
@@ -514,7 +517,7 @@ check_collinearity.zerocount <- function(x,
   if (!is.null(insight::find_interactions(x)) && any(result > 10) && isTRUE(verbose)) {
     insight::format_alert(
       "Model has interaction terms. VIFs might be inflated.",
-      "You may check multicollinearity among predictors of a model without interaction terms."
+      "Try to center the variables used for the interaction, or check multicollinearity among predictors of a model without interaction terms." # nolint
     )
   }
 
@@ -577,7 +580,6 @@ check_collinearity.zerocount <- function(x,
 }
 
 
-
 .term_assignments <- function(x, component, verbose = TRUE) {
   tryCatch(
     {
@@ -613,7 +615,6 @@ check_collinearity.zerocount <- function(x,
 }
 
 
-
 .find_term_assignment <- function(x, component, verbose = TRUE) {
   pred <- insight::find_predictors(x)[[component]]
 
@@ -648,7 +649,6 @@ check_collinearity.zerocount <- function(x,
 }
 
 
-
 .zi_term_assignment <- function(x, component = "zero_inflated", verbose = TRUE) {
   tryCatch(
     {

R/check_concurvity.R

@@ -25,7 +25,6 @@ check_concurvity.gam <- function(x, ...) {
 }
 
 
-
 # methods ---------------------------------
 
 #' @export

R/check_distribution.R

@@ -61,7 +61,6 @@ check_distribution <- function(model) {
 }
 
 
-
 # default -----------------------------
 
 #' @export
@@ -106,7 +105,6 @@ check_distribution.default <- function(model) {
 }
 
 
-
 # methods --------------------------
 
 #' @export
@@ -160,7 +158,6 @@ plot.check_distribution_numeric <- function(x, ...) {
 }
 
 
-
 # other classes -------------------
 
 #' @export
@@ -184,7 +181,6 @@ check_distribution.numeric <- function(model) {
 }
 
 
-
 # utilities -----------------------------
 
 .extract_features <- function(x, type = NULL) {
@@ -243,7 +239,6 @@ check_distribution.numeric <- function(model) {
 }
 
 
-
 .is_integer <- function(x) {
   tryCatch(
     ifelse(is.infinite(x), FALSE, x %% 1 == 0),

R/check_factorstructure.R

@@ -114,9 +114,6 @@ check_factorstructure <- function(x, n = NULL, ...) {
 }
 
 
-
-
-
 #' @rdname check_factorstructure
 #' @export
 check_kmo <- function(x, n = NULL, ...) {
@@ -169,9 +166,6 @@ check_kmo <- function(x, n = NULL, ...) {
 }
 
 
-
-
-
 #' @rdname check_factorstructure
 #' @export
 check_sphericity_bartlett <- function(x, n = NULL, ...) {
@@ -213,7 +207,6 @@ check_sphericity_bartlett <- function(x, n = NULL, ...) {
 }
 
 
-
 # Helpers -----------------------------------------------------------------
 
 #' @keywords internal

R/check_heterogeneity_bias.R

@@ -102,8 +102,6 @@ check_heterogeneity_bias <- function(x, select = NULL, by = NULL, nested = FALSE
 }
 
 
-
-
 #' @export
 print.check_heterogeneity_bias <- function(x, ...) {
   cat("Possible heterogeneity bias due to following predictors: ")

R/check_heteroscedasticity.R

@@ -79,7 +79,6 @@ check_heteroscedasticity.default <- function(x, ...) {
 }
 
 
-
 # methods -----------------------
 
 #' @export
@@ -112,7 +111,6 @@ plot.check_heteroscedasticity <- function(x, ...) {
 }
 
 
-
 # utilities -----------------------
 
 .sigma <- function(x) {

R/check_homogeneity.R

@@ -36,7 +36,6 @@ check_homogeneity <- function(x, method = c("bartlett", "fligner", "levene", "au
 }
 
 
-
 # default -------------------------
 
 #' @export
@@ -105,7 +104,6 @@ check_homogeneity.default <- function(x, method = c("bartlett", "fligner", "leve
 }
 
 
-
 # methods -----------------------
 
 #' @export
@@ -130,7 +128,6 @@ plot.check_homogeneity <- function(x, ...) {
 }
 
 
-
 # other classes -----------------------------
 
 #' @rdname check_homogeneity

R/check_htest.R

@@ -153,7 +153,6 @@ check_symmetry.htest <- function(x, ...) {
 # }
 
 
-
 # Print -------------------------------------------------------------------
 
 #' @export