use examplesIf

R/check_distribution.R

@@ -48,15 +48,11 @@ NULL
 #' There is a `plot()` method, which shows the probabilities of all predicted
 #' distributions, however, only if the probability is greater than zero.
 #'
-#' @examples
-#' if (require("lme4") && require("parameters") &&
-#'   require("see") && require("patchwork") && require("randomForest")) {
-#'   data(sleepstudy)
-#'
-#'   model <<- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
-#'   check_distribution(model)
-#'   plot(check_distribution(model))
-#' }
+#' @examplesIf require("lme4") && require("parameters") && require("see") && require("patchwork") && require("randomForest")
+#' data(sleepstudy, package = "lme4")
+#' model <<- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
+#' check_distribution(model)
+#' plot(check_distribution(model))
 #' @export
 check_distribution <- function(model) {
   UseMethod("check_distribution")

R/check_itemscale.R

@@ -43,17 +43,16 @@
 #' - Trochim WMK (2008) Types of Reliability.
 #'   ([web](https://conjointly.com/kb/types-of-reliability/))
 #'
-#' @examples
+#' @examplesIf require("parameters") && require("psych")
 #' # data generation from '?prcomp', slightly modified
 #' C <- chol(S <- toeplitz(0.9^(0:15)))
 #' set.seed(17)
 #' X <- matrix(rnorm(1600), 100, 16)
 #' Z <- X %*% C
-#' if (require("parameters") && require("psych")) {
-#'   pca <- principal_components(as.data.frame(Z), rotation = "varimax", n = 3)
-#'   pca
-#'   check_itemscale(pca)
-#' }
+#'
+#' pca <- principal_components(as.data.frame(Z), rotation = "varimax", n = 3)
+#' pca
+#' check_itemscale(pca)
 #' @export
 check_itemscale <- function(x) {
   if (!inherits(x, "parameters_pca")) {

R/check_multimodal.R

@@ -10,33 +10,29 @@
 #' @param x A numeric vector or a data frame.
 #' @param ... Arguments passed to or from other methods.
 #'
-#' @examples
+#' @examplesIf require("multimode") && require("mclust")
 #' \dontrun{
-#' if (require("multimode")) {
-#'   # Univariate
-#'   x <- rnorm(1000)
-#'   check_multimodal(x)
-#' }
+#' # Univariate
+#' x <- rnorm(1000)
+#' check_multimodal(x)
 #'
-#' if (require("multimode") && require("mclust")) {
-#'   x <- c(rnorm(1000), rnorm(1000, 2))
-#'   check_multimodal(x)
+#' x <- c(rnorm(1000), rnorm(1000, 2))
+#' check_multimodal(x)
 #'
-#'   # Multivariate
-#'   m <- data.frame(
-#'     x = rnorm(200),
-#'     y = rbeta(200, 2, 1)
-#'   )
-#'   plot(m$x, m$y)
-#'   check_multimodal(m)
+#' # Multivariate
+#' m <- data.frame(
+#'   x = rnorm(200),
+#'   y = rbeta(200, 2, 1)
+#' )
+#' plot(m$x, m$y)
+#' check_multimodal(m)
 #'
-#'   m <- data.frame(
-#'     x = c(rnorm(100), rnorm(100, 4)),
-#'     y = c(rbeta(100, 2, 1), rbeta(100, 1, 4))
-#'   )
-#'   plot(m$x, m$y)
-#'   check_multimodal(m)
-#' }
+#' m <- data.frame(
+#'   x = c(rnorm(100), rnorm(100, 4)),
+#'   y = c(rbeta(100, 2, 1), rbeta(100, 1, 4))
+#' )
+#' plot(m$x, m$y)
+#' check_multimodal(m)
 #' }
 #' @references
 #' - Ameijeiras-Alonso, J., Crujeiras, R. M., and Rodríguez-Casal, A. (2019).

R/check_predictions.R

@@ -66,26 +66,21 @@
 #' - Gelman, A., Hill, J., and Vehtari, A. (2020). Regression and Other Stories.
 #'   Cambridge University Press.
 #'
-#' @examples
-#' library(performance)
+#' @examplesIf require("see")
 #' # linear model
-#' if (require("see")) {
-#'   model <- lm(mpg ~ disp, data = mtcars)
-#'   check_predictions(model)
-#' }
+#' model <- lm(mpg ~ disp, data = mtcars)
+#' check_predictions(model)
 #'
 #' # discrete/integer outcome
-#' if (require("see")) {
-#'   set.seed(99)
-#'   d <- iris
-#'   d$skewed <- rpois(150, 1)
-#'   model <- glm(
-#'     skewed ~ Species + Petal.Length + Petal.Width,
-#'     family = poisson(),
-#'     data = d
-#'   )
-#'   check_predictions(model, type = "discrete_both")
-#' }
+#' set.seed(99)
+#' d <- iris
+#' d$skewed <- rpois(150, 1)
+#' model <- glm(
+#'   skewed ~ Species + Petal.Length + Petal.Width,
+#'   family = poisson(),
+#'   data = d
+#' )
+#' check_predictions(model, type = "discrete_both")
 #'
 #' @export
 check_predictions <- function(object, ...) {

R/check_sphericity.R

@@ -10,15 +10,14 @@
 #' @return Invisibly returns the p-values of the test statistics. A p-value <
 #'   0.05 indicates a violation of sphericity.
 #'
-#' @examples
-#' if (require("car")) {
-#'   soils.mod <- lm(
-#'     cbind(pH, N, Dens, P, Ca, Mg, K, Na, Conduc) ~ Block + Contour * Depth,
-#'     data = Soils
-#'   )
+#' @examplesIf require("car") && require("carData")
+#' data(Soils, package = "carData")
+#' soils.mod <- lm(
+#'   cbind(pH, N, Dens, P, Ca, Mg, K, Na, Conduc) ~ Block + Contour * Depth,
+#'   data = Soils
+#' )
 #'
-#'   check_sphericity(Manova(soils.mod))
-#' }
+#' check_sphericity(Manova(soils.mod))
 #' @export
 check_sphericity <- function(x, ...) {
   UseMethod("check_sphericity")

R/icc.R

@@ -143,29 +143,25 @@
 #' very large, the variance ratio in the output makes no sense, e.g. because
 #' it is negative. In such cases, it might help to use `robust = TRUE`.
 #'
-#' @examples
-#' if (require("lme4")) {
-#'   model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
-#'   icc(model)
-#' }
+#' @examplesIf require("lme4")
+#' model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
+#' icc(model)
 #'
 #' # ICC for specific group-levels
-#' if (require("lme4")) {
-#'   data(sleepstudy)
-#'   set.seed(12345)
-#'   sleepstudy$grp <- sample(1:5, size = 180, replace = TRUE)
-#'   sleepstudy$subgrp <- NA
-#'   for (i in 1:5) {
-#'     filter_group <- sleepstudy$grp == i
-#'     sleepstudy$subgrp[filter_group] <-
-#'       sample(1:30, size = sum(filter_group), replace = TRUE)
-#'   }
-#'   model <- lmer(
-#'     Reaction ~ Days + (1 | grp / subgrp) + (1 | Subject),
-#'     data = sleepstudy
-#'   )
-#'   icc(model, by_group = TRUE)
+#' data(sleepstudy, package = "lme4")
+#' set.seed(12345)
+#' sleepstudy$grp <- sample(1:5, size = 180, replace = TRUE)
+#' sleepstudy$subgrp <- NA
+#' for (i in 1:5) {
+#'   filter_group <- sleepstudy$grp == i
+#'   sleepstudy$subgrp[filter_group] <-
+#'     sample(1:30, size = sum(filter_group), replace = TRUE)
 #' }
+#' model <- lme4::lmer(
+#'   Reaction ~ Days + (1 | grp / subgrp) + (1 | Subject),
+#'   data = sleepstudy
+#' )
+#' icc(model, by_group = TRUE)
 #' @export
 icc <- function(model,
                 by_group = FALSE,

R/r2_nakagawa.R

@@ -46,12 +46,10 @@
 #'    generalized linear mixed-effects models revisited and expanded. Journal of
 #'    The Royal Society Interface, 14(134), 20170213.
 #'
-#' @examples
-#' if (require("lme4")) {
-#'   model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
-#'   r2_nakagawa(model)
-#'   r2_nakagawa(model, by_group = TRUE)
-#' }
+#' @examplesIf require("lme4")
+#' model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
+#' r2_nakagawa(model)
+#' r2_nakagawa(model, by_group = TRUE)
 #' @export
 r2_nakagawa <- function(model,
                         by_group = FALSE,