CRAN submission 0.21.2

.gitignore

@@ -13,6 +13,7 @@
 
 # Output files from R CMD check
 /*.Rcheck/
+revdep/
 
 # RStudio files
 .Rproj.user/

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 0.21.1
-Date: 2023-05-26 09:34:52 UTC
-SHA: 7ad4819088d188babebe5de6880d1230782b32ec
+Version: 0.21.2
+Date: 2023-09-16 12:58:15 UTC
+SHA: a1a80b72020f0287b81563a419eddb36f59fa18d

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.21.1.9
+Version: 0.21.2
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -81,7 +81,7 @@ Depends:
 Imports:
     bayestestR (>= 0.13.0),
     datawizard (>= 0.7.0),
-    insight (>= 0.19.3.2),
+    insight (>= 0.19.4),
     graphics,
     methods,
     stats,
@@ -213,4 +213,3 @@ Config/Needs/website:
     r-lib/pkgdown,
     easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/insight

NEWS.md

@@ -11,7 +11,7 @@
   accepts the `exponentiate` argument.
 
 * The `print()`, `print_html()`, `print_md()` and `format()` methods for
-  `model_parameters()` get an `add_reference` argument, to add the reference
+  `model_parameters()` get an `include_reference` argument, to add the reference
   category of categorical predictors to the parameters table.
 
 ## Bug fixes

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
@@ -418,29 +418,25 @@
 #'
 #' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
 #'
-#' @examples
+#' @examplesIf require("boot", quietly = TRUE) && require("sandwich") && require("clubSandwich") && require("brglm2")
 #' library(parameters)
 #' model <- lm(mpg ~ wt + cyl, data = mtcars)
 #'
 #' model_parameters(model)
 #'
 #' # bootstrapped parameters
-#' if (require("boot", quietly = TRUE)) {
-#'   model_parameters(model, bootstrap = TRUE)
-#' }
+#' model_parameters(model, bootstrap = TRUE)
 #'
 #' # standardized parameters
 #' model_parameters(model, standardize = "refit")
 #'
 #' # robust, heteroskedasticity-consistent standard errors
-#' if (require("sandwich") && require("clubSandwich")) {
-#'   model_parameters(model, vcov = "HC3")
+#' model_parameters(model, vcov = "HC3")
 #'
-#'   model_parameters(model,
-#'     vcov = "vcovCL",
-#'     vcov_args = list(cluster = mtcars$cyl)
-#'   )
-#' }
+#' model_parameters(model,
+#'   vcov = "vcovCL",
+#'   vcov_args = list(cluster = mtcars$cyl)
+#' )
 #'
 #' # different p-value style in output
 #' model_parameters(model, p_digits = 5)
@@ -454,15 +450,13 @@
 #' model_parameters(model, exponentiate = TRUE)
 #'
 #' # bias-corrected logistic regression with penalized maximum likelihood
-#' if (require("brglm2")) {
-#'   model <- glm(
-#'     vs ~ wt + cyl,
-#'     data = mtcars,
-#'     family = "binomial",
-#'     method = "brglmFit"
-#'   )
-#'   model_parameters(model)
-#' }
+#' model <- glm(
+#'   vs ~ wt + cyl,
+#'   data = mtcars,
+#'   family = "binomial",
+#'   method = "brglmFit"
+#' )
+#' model_parameters(model)
 #' }
 #' @return A data frame of indices related to the model's parameters.
 #' @export
@@ -692,7 +686,7 @@
   # 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.",
       "Use `ci_method=\"wald\"` to return confidence intervals based on robust standard errors."
     )
   }

R/2_ci.R

@@ -31,20 +31,19 @@
 #'
 #' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
 #'
-#' @examples
+#' @examplesIf require("glmmTMB")
 #' \donttest{
 #' library(parameters)
-#' if (require("glmmTMB")) {
-#'   model <- glmmTMB(
-#'     count ~ spp + mined + (1 | site),
-#'     ziformula = ~mined,
-#'     family = poisson(),
-#'     data = Salamanders
-#'   )
+#' data(Salamanders, package = "glmmTMB")
+#' model <- glmmTMB::glmmTMB(
+#'   count ~ spp + mined + (1 | site),
+#'   ziformula = ~mined,
+#'   family = poisson(),
+#'   data = Salamanders
+#' )
 #'
-#'   ci(model)
-#'   ci(model, component = "zi")
-#' }
+#' ci(model)
+#' ci(model, component = "zi")
 #' }
 #' @export
 ci.default <- function(x, ci = 0.95, dof = NULL, method = NULL, ...) {

R/bootstrap_model.R

@@ -40,18 +40,14 @@
 #'
 #' @seealso [`bootstrap_parameters()`], [`simulate_model()`], [`simulate_parameters()`]
 #'
-#' @examples
+#' @examplesIf require("boot", quietly = TRUE) && require("emmeans", quietly = TRUE)
 #' \dontrun{
-#' if (require("boot", quietly = TRUE)) {
-#'   model <- lm(mpg ~ wt + factor(cyl), data = mtcars)
-#'   b <- bootstrap_model(model)
-#'   print(head(b))
+#' model <- lm(mpg ~ wt + factor(cyl), data = mtcars)
+#' b <- bootstrap_model(model)
+#' print(head(b))
 #'
-#'   if (require("emmeans", quietly = TRUE)) {
-#'     est <- emmeans(b, consec ~ cyl)
-#'     print(model_parameters(est))
-#'   }
-#' }
+#' est <- emmeans::emmeans(b, consec ~ cyl)
+#' print(model_parameters(est))
 #' }
 #' @export
 bootstrap_model <- function(model,

R/bootstrap_parameters.R

@@ -33,19 +33,15 @@
 #'   p-values can be biased, and it is suggested to use proper permutation tests
 #'   to obtain non-parametric p-values.
 #'
-#' @examples
+#' @examplesIf require("boot", quietly = TRUE) && require("emmeans", quietly = TRUE)
 #' \dontrun{
-#' if (require("boot", quietly = TRUE)) {
-#'   set.seed(2)
-#'   model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
-#'   b <- bootstrap_parameters(model)
-#'   print(b)
+#' set.seed(2)
+#' model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
+#' b <- bootstrap_parameters(model)
+#' print(b)
 #'
-#'   if (require("emmeans")) {
-#'     est <- emmeans(b, trt.vs.ctrl ~ Species)
-#'     print(model_parameters(est))
-#'   }
-#' }
+#' est <- emmeans::emmeans(b, trt.vs.ctrl ~ Species)
+#' print(model_parameters(est))
 #' }
 #' @export
 bootstrap_parameters <- function(model,

R/cluster_discrimination.R

@@ -14,14 +14,12 @@
 #' [`performance::check_clusterstructure()`] to check suitability of data for
 #' clustering.
 #'
-#' @examples
-#' if (requireNamespace("MASS", quietly = TRUE)) {
-#'   # Retrieve group classification from hierarchical cluster analysis
-#'   clustering <- cluster_analysis(iris[, 1:4], n = 3)
+#' @examplesIf requireNamespace("MASS", quietly = TRUE)
+#' # Retrieve group classification from hierarchical cluster analysis
+#' clustering <- cluster_analysis(iris[, 1:4], n = 3)
 #'
-#'   # Goodness of group classification
-#'   cluster_discrimination(clustering)
-#' }
+#' # Goodness of group classification
+#' cluster_discrimination(clustering)
 #' @export
 cluster_discrimination <- function(x, cluster_groups = NULL, ...) {
   UseMethod("cluster_discrimination")

R/cluster_performance.R

@@ -45,12 +45,12 @@
 cluster_performance.hclust <- function(model, data, clusters, ...) {
   if (is.null(data)) {
     insight::format_error(
       "This function requires the data used to compute the clustering to be provided via `data` as it is not accessible from the clustering object itself."
     )
   }
   if (is.null(clusters)) {
     insight::format_error(
       "This function requires a vector of clusters assignments of same length as data to be passed, as it is not contained in the clustering object itself."
     )
   }
 
@@ -61,19 +61,17 @@
 
 
 #' @rdname cluster_performance
-#' @examples
+#' @examplesIf require("dbscan", quietly = TRUE)
 #' # DBSCAN
-#' if (require("dbscan", quietly = TRUE)) {
-#'   model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
+#' model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
 #'
-#'   rez <- cluster_performance(model, iris[1:4])
-#'   rez
-#' }
+#' rez <- cluster_performance(model, iris[1:4])
+#' rez
 #' @export
 cluster_performance.dbscan <- function(model, data, ...) {
   if (is.null(data)) {
     insight::format_error(
       "This function requires the data used to compute the clustering to be provided via `data` as it is not accessible from the clustering object itself."
     )
   }
 

R/convert_efa_to_cfa.R

@@ -8,22 +8,21 @@
 #' @param max_per_dimension Maximum number of variables to keep per dimension.
 #' @inheritParams principal_components
 #'
-#' @examples
+#' @examplesIf require("psych") && require("lavaan")
 #' \donttest{
 #' library(parameters)
-#' if (require("psych") && require("lavaan")) {
-#'   efa <- psych::fa(attitude, nfactors = 3)
+#' data(attitude)
+#' efa <- psych::fa(attitude, nfactors = 3)
 #'
-#'   model1 <- efa_to_cfa(efa)
-#'   model2 <- efa_to_cfa(efa, threshold = 0.3)
-#'   model3 <- efa_to_cfa(efa, max_per_dimension = 2)
+#' model1 <- efa_to_cfa(efa)
+#' model2 <- efa_to_cfa(efa, threshold = 0.3)
+#' model3 <- efa_to_cfa(efa, max_per_dimension = 2)
 #'
-#'   suppressWarnings(anova(
-#'     lavaan::cfa(model1, data = attitude),
-#'     lavaan::cfa(model2, data = attitude),
-#'     lavaan::cfa(model3, data = attitude)
-#'   ))
-#' }
+#' suppressWarnings(anova(
+#'   lavaan::cfa(model1, data = attitude),
+#'   lavaan::cfa(model2, data = attitude),
+#'   lavaan::cfa(model3, data = attitude)
+#' ))
 #' }
 #' @return Converted index.
 #' @export
@@ -42,9 +41,10 @@ convert_efa_to_cfa.fa <- function(model,
                                   max_per_dimension = NULL,
                                   ...) {
   .efa_to_cfa(model_parameters(model, threshold = threshold, ...),
-              names = names,
-              max_per_dimension = max_per_dimension,
-              ...)
+    names = names,
+    max_per_dimension = max_per_dimension,
+    ...
+  )
 }
 
 #' @export
@@ -78,7 +78,7 @@ efa_to_cfa <- convert_efa_to_cfa
 
 
 #' @keywords internal
-.efa_to_cfa <- function(loadings, names = NULL, max_per_dimension=NULL, ...) {
+.efa_to_cfa <- function(loadings, names = NULL, max_per_dimension = NULL, ...) {
   loadings <- attributes(loadings)$loadings_long
 
   # Get dimension names
@@ -100,7 +100,6 @@ efa_to_cfa <- convert_efa_to_cfa
   cfa <- NULL
   # Iterate over dimensions
   for (i in seq_along(names)) {
-
     # Find correct subset
     items <- loadings[loadings$Component == unique(loadings$Component)[i], ]
 

R/display.R

@@ -91,7 +91,7 @@
                                      font_size = "100%",
                                      line_padding = 4,
                                      column_labels = NULL,
-                                     add_reference = FALSE,
+                                     include_reference = FALSE,
                                      verbose = TRUE,
                                      ...) {
   if (identical(format, "html")) {
@@ -193,7 +193,7 @@
 #' @inheritParams model_parameters.principal
 #' @rdname display.parameters_model
 #' @export
 display.parameters_efa <- function(object, format = "markdown", digits = 2, sort = FALSE, threshold = NULL, labels = NULL, ...) {
   print_md(x = object, digits = digits, sort = sort, threshold = threshold, labels = labels, ...)
 }
 

R/dominance_analysis.R

@@ -31,9 +31,9 @@
 #' @param contrasts A named list of [`contrasts`] used by the model object.
 #' This list is required in order for the correct mapping of parameters to
 #' predictors in the output when the model creates indicator codes for factor
-#' variables using [`model_matrix`]. By default, the `contrast` element from
-#' the model object submitted is used. If the model object does not have a
-#' `contrast` element the user can supply this named list.
+#' variables using [`insight::get_modelmatrix()`]. By default, the `contrast`
+#' element from the model object submitted is used. If the model object does
+#' not have a `contrast` element the user can supply this named list.
 #'
 #' @param ...  Not used at current.
 #'
@@ -115,24 +115,22 @@
 #'
 #' @author Joseph Luchman
 #'
-#' @examples
-#' if (require("domir") && require("performance")) {
-#'   data(mtcars)
+#' @examplesIf require("domir") && require("performance")
+#' data(mtcars)
 #'
-#'   # Dominance Analysis with Logit Regression
-#'   model <- glm(vs ~ cyl + carb + mpg, data = mtcars, family = binomial())
+#' # Dominance Analysis with Logit Regression
+#' model <- glm(vs ~ cyl + carb + mpg, data = mtcars, family = binomial())
 #'
-#'   performance::r2(model)
-#'   dominance_analysis(model)
+#' performance::r2(model)
+#' dominance_analysis(model)
 #'
-#'   # Dominance Analysis with Weighted Logit Regression
-#'   model_wt <- glm(vs ~ cyl + carb + mpg,
-#'     data = mtcars,
-#'     weights = wt, family = quasibinomial()
-#'   )
+#' # Dominance Analysis with Weighted Logit Regression
+#' model_wt <- glm(vs ~ cyl + carb + mpg,
+#'   data = mtcars,
+#'   weights = wt, family = quasibinomial()
+#' )
 #'
-#'   dominance_analysis(model_wt, quote_args = "weights")
-#' }
+#' dominance_analysis(model_wt, quote_args = "weights")
 #' @export
 dominance_analysis <- function(model, sets = NULL, all = NULL,
                                conditional = TRUE, complete = TRUE,
@@ -158,7 +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."
@@ -198,11 +196,11 @@
       insight::format_error("Nested lists are not allowed in `sets`.")
     }
 
     if (!all(sapply(sets, inherits, "formula"))) {
       insight::format_error("Each element of list in `sets` must be a formula.")
     }
 
     if (any(sapply(sets, function(x) attr(stats::terms(x), "response") == 1))) {
       insight::format_error("Formulas in `sets` argument must not have responses/left hand sides.")
     }
   }
@@ -349,7 +347,7 @@
 
   # 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.")
     } else {
       args[[arg]] <-

R/format.R

@@ -15,7 +15,7 @@ format.parameters_model <- function(x,
                                     zap_small = FALSE,
                                     format = NULL,
                                     groups = NULL,
-                                    add_reference = FALSE,
+                                    include_reference = FALSE,
                                     ...) {
   # save attributes
   coef_name <- attributes(x)$coefficient_name
@@ -168,7 +168,7 @@ format.parameters_model <- function(x,
       ci_width = ci_width,
       ci_brackets = ci_brackets,
       zap_small = zap_small,
-      add_reference = add_reference,
+      include_reference = include_reference,
       ...
     )
   } else {
@@ -185,7 +185,7 @@ format.parameters_model <- function(x,
       format = format,
       coef_name = coef_name,
       zap_small = zap_small,
-      add_reference = add_reference,
+      include_reference = include_reference,
       ...
     )
   }