Use examplesIf in roxygen comments

DESCRIPTION

@@ -86,6 +86,7 @@ Suggests:
     boot,
     brms,
     car,
+    carData,
     CompQuadForm,
     correlation,
     cplm,

R/check_convergence.R

@@ -32,7 +32,7 @@
 #' how to resolve convergence issues. Another clue might be large parameter
 #' values, e.g. estimates (on the scale of the linear predictor) larger than
 #' 10 in (non-identity link) generalized linear model *might* indicate
 #' [complete separation](https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/).
 #' Complete separation can be addressed by regularization, e.g. penalized
 #' regression or Bayesian regression with appropriate priors on the fixed effects.
 #'
@@ -52,7 +52,7 @@
 #' cbpp$x <- rnorm(nrow(cbpp))
 #' cbpp$x2 <- runif(nrow(cbpp))
 #'
-#' model <- lm4::glmer(
+#' model <- lme4::glmer(
 #'   cbind(incidence, size - incidence) ~ period + x + x2 + (1 + x | herd),
 #'   data = cbpp,
 #'   family = binomial()

R/check_distribution.R

@@ -48,15 +48,11 @@
 #' 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")
@@ -78,7 +74,7 @@
   } else {
     x <- stats::residuals(model)
   }
   # x_scaled <- .normalize(x)
   dat <- .extract_features(x)
 
   dist_residuals <- as.data.frame(t(stats::predict(classify_distribution, dat, type = "prob")))
@@ -196,23 +192,23 @@
   x <- x[!is.na(x)]
 
   data.frame(
-    "SD" = stats::sd(x),
-    "MAD" = stats::mad(x, constant = 1),
-    "Mean_Median_Distance" = mean(x) - stats::median(x),
-    "Mean_Mode_Distance" = mean(x) - as.numeric(bayestestR::map_estimate(x, bw = "nrd0")),
-    "SD_MAD_Distance" = stats::sd(x) - stats::mad(x, constant = 1),
-    "Var_Mean_Distance" = stats::var(x) - mean(x),
-    "Range_SD" = diff(range(x)) / stats::sd(x),
-    "Range" = diff(range(x)),
-    "IQR" = stats::IQR(x),
-    "Skewness" = .skewness(x),
-    "Kurtosis" = .kurtosis(x),
-    "Uniques" = length(unique(x)) / length(x),
-    "N_Uniques" = length(unique(x)),
-    "Min" = min(x),
-    "Max" = max(x),
-    "Proportion_Positive" = sum(x >= 0) / length(x),
-    "Integer" = all(.is_integer(x))
+    SD = stats::sd(x),
+    MAD = stats::mad(x, constant = 1),
+    Mean_Median_Distance = mean(x) - stats::median(x),
+    Mean_Mode_Distance = mean(x) - as.numeric(bayestestR::map_estimate(x, bw = "nrd0")),
+    SD_MAD_Distance = stats::sd(x) - stats::mad(x, constant = 1),
+    Var_Mean_Distance = stats::var(x) - mean(x),
+    Range_SD = diff(range(x)) / stats::sd(x),
+    Range = diff(range(x)),
+    IQR = stats::IQR(x),
+    Skewness = .skewness(x),
+    Kurtosis = .kurtosis(x),
+    Uniques = length(unique(x)) / length(x),
+    N_Uniques = length(unique(x)),
+    Min = min(x),
+    Max = max(x),
+    Proportion_Positive = sum(x >= 0) / length(x),
+    Integer = all(.is_integer(x))
   )
 }
 

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")) {
@@ -83,8 +82,8 @@
       Mean = vapply(items, mean, numeric(1), na.rm = TRUE),
       SD = vapply(items, stats::sd, numeric(1), na.rm = TRUE),
       Skewness = vapply(items, function(i) as.numeric(datawizard::skewness(i)), numeric(1)),
       "Difficulty" = item_difficulty(items)$Difficulty,
       "Discrimination" = .item_discr,
       "alpha if deleted" = .item_alpha,
       stringsAsFactors = FALSE,
       check.names = FALSE

R/check_model.R

@@ -48,7 +48,7 @@
 #'
 #' @details For Bayesian models from packages **rstanarm** or **brms**,
 #' models will be "converted" to their frequentist counterpart, using
 #' [`bayestestR::bayesian_as_frequentist`](https://easystats.github.io/bayestestR/reference/convert_bayesian_as_frequentist.html).
 #' A more advanced model-check for Bayesian models will be implemented at a
 #' later stage.
 #'
@@ -76,7 +76,7 @@
 #' plots are helpful to check model assumptions, they do not necessarily indicate
 #' so-called "lack of fit", e.g. missed non-linear relationships or interactions.
 #' Thus, it is always recommended to also look at
 #' [effect plots, including partial residuals](https://strengejacke.github.io/ggeffects/articles/introduction_partial_residuals.html).
 #'
 #' @section Homogeneity of Variance:
 #' This plot checks the assumption of equal variance (homoscedasticity). The
@@ -140,20 +140,14 @@
 #'
 #' @family functions to check model assumptions and and assess model quality
 #'
-#' @examples
+#' @examplesIf require("lme4")
 #' \dontrun{
 #' m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
 #' check_model(m)
 #'
-#' if (require("lme4")) {
-#'   m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
-#'   check_model(m, panel = FALSE)
-#' }
-#'
-#' if (require("rstanarm")) {
-#'   m <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
-#'   check_model(m)
-#' }
+#' data(sleepstudy, package = "lme4")
+#' m <- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
+#' check_model(m, panel = FALSE)
 #' }
 #' @export
 check_model <- function(x, ...) {
@@ -208,7 +202,7 @@
   }
 
   # try to find sensible default for "type" argument
   suggest_dots <- (minfo$is_bernoulli || minfo$is_count || minfo$is_ordinal || minfo$is_categorical || minfo$is_multinomial)
   if (missing(type) && suggest_dots) {
     type <- "discrete_interval"
   }

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_normality.R

@@ -27,15 +27,14 @@
 #' standardized deviance residuals is shown (in line with changes in
 #' `plot.lm()` for R 4.3+).
 #'
-#' @examples
+#' @examplesIf require("see")
 #' m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
 #' check_normality(m)
 #'
 #' # plot results
-#' if (require("see")) {
-#'   x <- check_normality(m)
-#'   plot(x)
-#' }
+#' x <- check_normality(m)
+#' plot(x)
+#'
 #' \dontrun{
 #' # QQ-plot
 #' plot(check_normality(m), type = "qq")

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, ...) {
@@ -109,7 +104,7 @@
   minfo <- insight::model_info(object, verbose = FALSE)
 
   # try to find sensible default for "type" argument
   suggest_dots <- (minfo$is_bernoulli || minfo$is_count || minfo$is_ordinal || minfo$is_categorical || minfo$is_multinomial)
   if (missing(type) && suggest_dots) {
     type <- "discrete_interval"
   }
@@ -221,7 +216,7 @@
 
   if (is.null(out)) {
     insight::format_error(
       sprintf("Could not simulate responses. Maybe there is no `simulate()` for objects of class `%s`?", class(object)[1])
     )
   }
 
@@ -268,7 +263,7 @@
   out <- tryCatch(
     {
       matrix_sim <- stats::simulate(object, nsim = iterations, re.form = re_formula, ...)
       as.data.frame(sapply(matrix_sim, function(i) i[, 1] / i[, 2], simplify = TRUE))
     },
     error = function(e) {
       NULL

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/check_symmetry.R

@@ -9,7 +9,7 @@
 #' @param ... Not used.
 #'
 #' @examples
-#' V <- wilcox.test(mtcars$mpg)
+#' V <- suppressWarnings(wilcox.test(mtcars$mpg))
 #' check_symmetry(V)
 #'
 #' @export

R/check_zeroinflation.R

@@ -21,12 +21,10 @@
 #'
 #' @family functions to check model assumptions and and assess model quality
 #'
-#' @examples
-#' if (require("glmmTMB")) {
-#'   data(Salamanders)
-#'   m <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
-#'   check_zeroinflation(m)
-#' }
+#' @examplesIf require("glmmTMB")
+#' data(Salamanders, package = "glmmTMB")
+#' m <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
+#' check_zeroinflation(m)
 #' @export
 check_zeroinflation <- function(x, tolerance = 0.05) {
   # check if we have poisson

R/compare_performance.R

@@ -70,20 +70,18 @@
 #' _Model selection and multimodel inference: A practical information-theoretic approach_ (2nd ed.).
 #' Springer-Verlag. \doi{10.1007/b97636}
 #'
-#' @examples
+#' @examplesIf require("lme4")
 #' data(iris)
 #' lm1 <- lm(Sepal.Length ~ Species, data = iris)
 #' lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
 #' lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
 #' compare_performance(lm1, lm2, lm3)
 #' compare_performance(lm1, lm2, lm3, rank = TRUE)
 #'
-#' if (require("lme4")) {
-#'   m1 <- lm(mpg ~ wt + cyl, data = mtcars)
-#'   m2 <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
-#'   m3 <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
-#'   compare_performance(m1, m2, m3)
-#' }
+#' m1 <- lm(mpg ~ wt + cyl, data = mtcars)
+#' m2 <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
+#' m3 <- lme4::lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
+#' compare_performance(m1, m2, m3)
 #' @inheritParams model_performance.lm
 #' @export
 compare_performance <- function(..., metrics = "all", rank = FALSE, estimator = "ML", verbose = TRUE) {

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,
@@ -357,8 +353,8 @@ variance_decomposition <- function(model,
   result <- structure(
     class = "icc_decomposed",
     list(
-      "ICC_decomposed" = 1 - fun(var_icc),
-      "ICC_CI" = ci_icc
+      ICC_decomposed = 1 - fun(var_icc),
+      ICC_CI = ci_icc
     )
   )
 

R/looic.R

@@ -11,11 +11,15 @@
 #'
 #' @return A list with four elements, the ELPD, LOOIC and their standard errors.
 #'
-#' @examples
-#' if (require("rstanarm")) {
-#'   model <- stan_glm(mpg ~ wt + cyl, data = mtcars, chains = 1, iter = 500, refresh = 0)
-#'   looic(model)
-#' }
+#' @examplesIf require("rstanarm")
+#' model <- rstanarm::stan_glm(
+#'   mpg ~ wt + cyl,
+#'   data = mtcars,
+#'   chains = 1,
+#'   iter = 500,
+#'   refresh = 0
+#' )
+#' looic(model)
 #' @export
 looic <- function(model, verbose = TRUE) {
   insight::check_if_installed("loo")