parameters() fails on binomial mgcv::gam

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.24.0.2
+Version: 0.24.0.3
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -224,4 +224,4 @@ Config/testthat/edition: 3
 Config/testthat/parallel: true
 Config/Needs/website: easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/datawizard
+Remotes: easystats/datawizard, easystats/performance

NEWS.md

@@ -1,3 +1,12 @@
+# parameters 0.24.1
+
+## Bug fixes
+
+* Fixed issue when printing `model_parameters()` with models from `mgcv::gam()`.
+
+* Fixed issues due to breaking changes in the latest release of the *datawizard*
+  package.
+
 # parameters 0.24.0
 
 ## Breaking Changes

R/dominance_analysis.R

@@ -132,7 +132,7 @@
 #'
 #' dominance_analysis(model_wt, quote_args = "weights")
 #' @export
 dominance_analysis <- function(model, sets = NULL, all = NULL,
                                conditional = TRUE, complete = TRUE,
                                quote_args = NULL, contrasts = model$contrasts,
                                ...) {
@@ -156,7 +156,7 @@
   }
 
   model_info <- insight::model_info(model)
   if (any(unlist(model_info[c("is_bayesian", "is_mixed", "is_gam", "is_multivariate", "is_zero_inflated", "is_hurdle")]))) {
     insight::format_error(
       paste0("`dominance_analysis()` does not yet support models of class `", class(model)[[1]], "`."),
       "You may be able to dominance analyze this model using the {.pkg domir} package."
@@ -228,7 +228,7 @@
   reg <- as.list(insight::get_call(model))[[1]]
 
   # Process sets ----
   if (!is.null(sets)) {
     # gather predictors from each set
     sets_processed <- lapply(sets, function(x) attr(stats::terms(x), "term.labels"))
 
@@ -250,7 +250,7 @@
     # apply names to sets
     set_names <- names(sets)
 
     missing_set_names <- which(set_names == "")
 
     if (length(missing_set_names) > 0) {
       set_names[missing_set_names] <- paste0("set", missing_set_names)
@@ -277,7 +277,7 @@
   }
 
   # Process all ----
   if (!is.null(all)) {
     # gather predictors in all
     all_processed <- attr(stats::terms(all), "term.labels")
 
@@ -331,9 +331,9 @@
   if (length(ivs) == 0) ivs <- "1"
   fml <- stats::reformulate(ivs, response = dv, intercept = insight::has_intercept(model))
 
   data <- insight::get_data(model, verbose = FALSE)
 
   args <- as.list(insight::get_call(model), collapse = "") # extract all arguments from call
 
   loc <- which(!(names(args) %in% c("formula", "data"))) # find formula and data arguments
 
@@ -342,8 +342,8 @@
     insight::format_error("Model submitted does not have a formula and `data` argument.")
   }
 
   args <- args[loc] # remove formula and data arguments
   args <- args[-1] # remove function name
 
   # quote arguments for domin
   for (arg in quote_args) {
@@ -443,7 +443,7 @@
   # Apply set names
   if (!is.null(sets)) {
     for (set in seq_along(sets)) {
       set_name <- if (!is.null(names(sets)[[set]])) {
         names(sets)[[set]]
       } else {
         paste0("set", set)
@@ -571,7 +571,7 @@
   }
 
   da_df_res <- datawizard::data_rename(
-  da_df_res,
+    da_df_res,
     replacement = c(
       "Parameter", "General_Dominance",
       "Percent", "Ranks", "Subset"

R/format.R

@@ -887,9 +887,14 @@ format.parameters_sem <- function(x,
     logit_model <- isTRUE(.additional_arguments(x, "logit_link", FALSE)) ||
       isTRUE(attributes(x)$coefficient_name %in% c("Log-Odds", "Odds Ratio"))
 
+    # remove NA and infinite values from spurios coefficients
+    if (!is.null(spurious_coefficients)) {
+      spurious_coefficients <- spurious_coefficients[!is.na(spurious_coefficients) & !is.infinite(spurious_coefficients)] # nolint
+    }
+
     # check for complete separation coefficients or possible issues with
     # too few data points
-    if (!is.null(spurious_coefficients) && logit_model) {
+    if (!is.null(spurious_coefficients) && length(spurious_coefficients) && logit_model) {
       if (any(spurious_coefficients > 50)) {
         msg <- c(msg, "Some coefficients are very large, which may indicate issues with complete separation.") # nolint
       } else if (any(spurious_coefficients > 15)) {

tests/testthat/_snaps/printing.md

@@ -361,3 +361,24 @@
       Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
         using a Wald t-distribution approximation.
 
+# no fail for mgcv-binomial
+
+    Code
+      print(out)
+    Output
+      # Fixed Effects
+
+      Parameter   | Log-Odds |   SE |        95% CI |     z |    df |     p
+      ---------------------------------------------------------------------
+      (Intercept) |    -0.20 | 0.50 | [-1.18, 0.79] | -0.39 | 29.98 | 0.695
+
+      # Smooth Terms
+
+      Parameter         |    z |   df |     p
+      ---------------------------------------
+      Smooth term (mpg) | 7.24 | 1.02 | 0.007
+    Message
+
+      The model has a log- or logit-link. Consider using `exponentiate =
+        TRUE` to interpret coefficients as ratios.
+

tests/testthat/test-printing.R

@@ -118,3 +118,13 @@ withr::with_options(
     expect_snapshot(print(out))
   })
 )
+
+withr::with_options(
+  list(parameters_warning_exponentiate = TRUE),
+  test_that("no fail for mgcv-binomial", {
+    skip_if_not_installed("mgcv")
+    m <- mgcv::gam(vs ~ s(mpg), data = mtcars, family = "binomial")
+    out <- model_parameters(m)
+    expect_snapshot(print(out))
+  })
+)