Bootstrap Argument in parameters::parameters() Function Not Working with svyglm Models

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.21.3.2
+Version: 0.21.3.3
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -146,6 +146,7 @@ Suggests:
     lme4,
     lmerTest,
     lmtest,
+    logistf,
     logspline,
     lqmm,
     M3C,

NEWS.md

@@ -6,6 +6,11 @@
   `marginaleffects::predictions()` now defaults to `FALSE`, in line with all
   the other `model_parameters()` methods.
 
+## Changes
+
+* `model_parameters()` for models of package *survey* now gives informative
+  messages when `bootstrap = TRUE` (which is currently not supported).
+
 # parameters 0.21.3
 
 ## Changes

R/1_model_parameters.R

@@ -181,7 +181,7 @@
 #'
 #' 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
@@ -576,14 +576,14 @@
       ci_method <- "quantile"
     }
 
-    args <- list(
+    fun_args <- list(
       model,
       iterations = iterations,
       ci = ci,
       ci_method = ci_method
     )
-    args <- c(args, dots)
-    params <- do.call("bootstrap_parameters", args)
+    fun_args <- c(fun_args, dots)
+    params <- do.call("bootstrap_parameters", fun_args)
 
     # Processing, non-bootstrapped parameters
   } else {
@@ -592,7 +592,7 @@
       ci_method <- "wald"
     }
 
-    args <- list(
+    fun_args <- list(
       model,
       ci = ci,
       component = component,
@@ -607,8 +607,8 @@
       vcov = vcov,
       vcov_args = vcov_args
     )
-    args <- c(args, dots)
-    params <- do.call(".extract_parameters_generic", args)
+    fun_args <- c(fun_args, dots)
+    params <- do.call(".extract_parameters_generic", fun_args)
   }
 
 
@@ -686,12 +686,12 @@
   # tell user that profiled CIs don't respect vcov-args
   if (identical(ci_method, "profile") && (!is.null(vcov) || !is.null(vcov_args)) && isTRUE(verbose)) {
     insight::format_alert(
-      "When `ci_method=\"profile\"`, `vcov` only modifies standard errors, test-statistic and p-values, but not confidence intervals.",
+      "When `ci_method=\"profile\"`, `vcov` only modifies standard errors, test-statistic and p-values, but not confidence intervals.", # nolint
       "Use `ci_method=\"wald\"` to return confidence intervals based on robust standard errors."
     )
   }
 
-  args <- list(
+  fun_args <- list(
     model = model,
     ci = ci,
     ci_method = ci_method,
@@ -708,8 +708,8 @@
     vcov_args = vcov_args,
     verbose = verbose
   )
-  args <- c(args, dots)
-  out <- do.call(".model_parameters_generic", args)
+  fun_args <- c(fun_args, dots)
+  out <- do.call(".model_parameters_generic", fun_args)
 
   attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(model))
   out

R/3_p_value.R

@@ -105,13 +105,13 @@ p_value.default <- function(model,
     if (inherits(vcov, "data.frame") || "SE" %in% colnames(vcov)) {
       se <- vcov
     } else {
-      args <- list(model,
+      fun_args <- list(model,
         vcov_args = vcov_args,
         vcov = vcov,
         verbose = verbose
       )
-      args <- c(args, dots)
-      se <- do.call("standard_error", args)
+      fun_args <- c(fun_args, dots)
+      se <- do.call("standard_error", fun_args)
     }
 
     dof <- degrees_of_freedom(model, method = "wald", verbose = FALSE)

R/4_standard_error.R

@@ -88,8 +88,8 @@
 
   # vcov: function which returns a matrix
   if (is.function(vcov)) {
-    args <- c(list(model), vcov_args, dots)
-    se <- .safe(sqrt(diag(do.call("vcov", args))))
+    fun_args <- c(list(model), vcov_args, dots)
+    se <- .safe(sqrt(diag(do.call("vcov", fun_args))))
   }
 
   # vcov: character (with backward compatibility for `robust = TRUE`)
@@ -185,27 +185,27 @@
 
 
 # .ranef_se <- function(x) {
 # insight::check_if_installed("lme4")
 #
 #   cc <- stats::coef(model)
 #
 #   # get names of intercepts
 #   inames <- names(cc)
 #
 #   # variances of fixed effects
 #   fixed.vars <- diag(as.matrix(stats::vcov(model)))
 #
 #   # extract variances of conditional modes
 #   r1 <- lme4::ranef(model, condVar = TRUE)
 #
 #   # we may have multiple random intercepts, iterate all
 #   se.merMod <- lapply(1:length(cc), function(i) {
 #     cmode.vars <- t(apply(attr(r1[[i]], "postVar"), 3, diag))
 #     seVals <- sqrt(sweep(cmode.vars, 2, fixed.vars[names(r1[[i]])], "+", check.margin = FALSE))
 #
 #     if (length(r1[[i]]) == 1) {
 #       seVals <- as.data.frame(t(seVals))
 #       stats::setNames(seVals, names(r1[[i]]))
 #     } else {
 #       seVals <- seVals[, 1:2]
 #       stats::setNames(as.data.frame(seVals), names(r1[[i]]))

R/ci_profile_boot.R

@@ -84,14 +84,14 @@
   dot_args <- .check_profile_uniroot_args(...)
 
   if (is.null(profiled)) {
-    args <- list(x, method = "profile", level = ci, dot_args)
-    out <- as.data.frame(do.call(stats::confint, args))
+    fun_args <- list(x, method = "profile", level = ci, dot_args)
+    out <- as.data.frame(do.call(stats::confint, fun_args))
   } else {
-    args <- list(profiled, level = ci, dot_args)
-    out <- .safe(as.data.frame(do.call(stats::confint, args)))
+    fun_args <- list(profiled, level = ci, dot_args)
+    out <- .safe(as.data.frame(do.call(stats::confint, fun_args)))
     if (is.null(out)) {
-      args <- list(x, method = "profile", level = ci, dot_args)
-      out <- as.data.frame(do.call(stats::confint, args))
+      fun_args <- list(x, method = "profile", level = ci, dot_args)
+      out <- as.data.frame(do.call(stats::confint, fun_args))
     }
   }
   .process_glmmTMB_CI(x, out, ci, component)
@@ -103,8 +103,8 @@
 .ci_uniroot_glmmTMB <- function(x, ci, component, ...) {
   # make sure "..." doesn't pass invalid arguments to package TMB
   dot_args <- .check_profile_uniroot_args(...)
-  args <- list(x, level = ci, method = "uniroot", dot_args)
-  out <- as.data.frame(do.call(stats::confint, args))
+  fun_args <- list(x, level = ci, method = "uniroot", dot_args)
+  out <- as.data.frame(do.call(stats::confint, fun_args))
   .process_glmmTMB_CI(x, out, ci, component)
 }
 

R/dominance_analysis.R

@@ -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
 
@@ -342,16 +342,15 @@
     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) {
-    if (!(arg %in% names(args))) {
-      insight::format_error(arg, " in `quote_args` not among arguments in model.")
+    if (arg %in% names(args)) {
+      args[[arg]] <- str2lang(paste0("quote(", deparse(args[[arg]]), ")", collapse = ""))
     } else {
-      args[[arg]] <-
-        str2lang(paste0("quote(", deparse(args[[arg]]), ")", collapse = ""))
+      insight::format_error(arg, " in `quote_args` not among arguments in model.")
     }
   }
 
@@ -444,7 +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)