parameters() fails on binomial mgcv::gam

Fixes #1051

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

@@ -77,6 +77,27 @@
       Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
         using a Wald z-distribution approximation.
 
+---
+
+    Code
+      print(model_parameters(m))
+    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.
+
 # adding model summaries
 
     Code

tests/testthat/test-printing.R

@@ -118,3 +118,9 @@ withr::with_options(
     expect_snapshot(print(out))
   })
 )
+
+test_that("print model with multiple components", {
+  skip_if_not_installed("mgcv")
+  m <- mgcv::gam(vs ~ s(mpg), data = mtcars, family = "binomial")
+  expect_snapshot(print(model_parameters(m)))
+})