Logistic models: Be less verbose by default + be less scary for users with less red

R/1_model_parameters.R

@@ -180,7 +180,7 @@
 #'
 #' Compared to fixed effects (or single-level) models, determining appropriate
 #' df for Wald-based inference in mixed models is more difficult.
 #' See [the R GLMM FAQ](https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#what-are-the-p-values-listed-by-summaryglmerfit-etc.-are-they-reliable)
 #' for a discussion.
 #'
 #' Several approximate methods for computing df are available, but you should
@@ -446,14 +446,16 @@
 #'   coefficients (and related confidence intervals). This is typical for
 #'   logistic regression, or more generally speaking, for models with log or
 #'   logit links. It is also recommended to use `exponentiate = TRUE` for models
-#'   with log-transformed response values. **Note:** Delta-method standard
-#'   errors are also computed (by multiplying the standard errors by the
-#'   transformed coefficients). This is to mimic behaviour of other software
-#'   packages, such as Stata, but these standard errors poorly estimate
-#'   uncertainty for the transformed coefficient. The transformed confidence
-#'   interval more clearly captures this uncertainty. For `compare_parameters()`,
-#'   `exponentiate = "nongaussian"` will only exponentiate coefficients from
-#'   non-Gaussian families.
+#'   with log-transformed response values. For models with a log-transformed
+#'   response variable, when `exponentiate = TRUE`, a one-unit increase in the
+#'   predictor is associated with multiplying the outcome by that predictor's
+#'   coefficient. **Note:** Delta-method standard errors are also computed (by
+#'   multiplying the standard errors by the transformed coefficients). This is
+#'   to mimic behaviour of other software packages, such as Stata, but these
+#'   standard errors poorly estimate uncertainty for the transformed
+#'   coefficient. The transformed confidence interval more clearly captures this
+#'   uncertainty. For `compare_parameters()`, `exponentiate = "nongaussian"`
+#'   will only exponentiate coefficients from non-Gaussian families.
 #' @param p_adjust Character vector, if not `NULL`, indicates the method to
 #'   adjust p-values. See [`stats::p.adjust()`] for details. Further
 #'   possible adjustment methods are `"tukey"`, `"scheffe"`,

R/format.R

@@ -3,7 +3,7 @@
 #' @inheritParams print.parameters_model
 #' @rdname print.parameters_model
 #' @export
 format.parameters_model <- function(x,
                                     pretty_names = TRUE,
                                     split_components = TRUE,
                                     select = NULL,
@@ -243,7 +243,7 @@
 #' @rdname print.compare_parameters
 #' @inheritParams print.parameters_model
 #' @export
 format.compare_parameters <- function(x,
                                       split_components = TRUE,
                                       select = NULL,
                                       digits = 2,
@@ -613,11 +613,6 @@
     footer <- .add_footer_text(footer, footer_text, type, is_ggeffects)
   }
 
-  # add color code, if we have a footer
-  if (!is.null(footer) && type == "text") {
-    footer <- c(footer, "blue")
-  }
-
   # if we have two trailing newlines, remove one
   if (identical(type, "text") && !is.null(footer) && endsWith(footer[1], "\n\n")) {
     footer[1] <- substr(footer[1], 0, nchar(x) - 1)
@@ -750,7 +745,7 @@
 
 
 # footer: type of uncertainty interval
 .print_footer_cimethod <- function(x) {
   if (isTRUE(getOption("parameters_cimethod", TRUE))) {
     # get attributes
     ci_method <- .additional_arguments(x, "ci_method", NULL)
@@ -853,7 +848,7 @@
         msg <- paste(msg, "Uncertainty intervals for random effect variances computed using a Wald z-distribution approximation.") # nolint
       }
 
-      insight::format_alert(msg)
+      insight::format_alert(insight::color_text(msg, "yellow"))
     }
   }
 }
@@ -884,10 +879,6 @@
         spurious_coefficients <- NULL
       }
     } else if (.is_valid_exponentiate_argument(exponentiate) && isTRUE(.additional_arguments(x, "log_response", FALSE))) { # nolint
-      msg <- c(
-        "This model has a log-transformed response variable, and exponentiated parameters are reported.",
-        "A one-unit increase in the predictor is associated with multiplying the outcome by that predictor's coefficient." # nolint
-      )
       # don't show warning about complete separation
       spurious_coefficients <- NULL
     }
@@ -899,9 +890,9 @@
     # check for complete separation coefficients or possible issues with
     # too few data points
     if (!is.null(spurious_coefficients) && logit_model) {
-      if (any(spurious_coefficients > 100)) {
+      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 > 25)) {
+      } else if (any(spurious_coefficients > 15)) {
         msg <- c(msg, "Some coefficients seem to be rather large, which may indicate issues with (quasi) complete separation. Consider using bias-corrected or penalized regression models.") # nolint
       }
     }