From 8a19dce36f5b907d2334f204e00e3924e061cee1 Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Sun, 29 Oct 2023 12:31:58 +0100
Subject: [PATCH] examples

---
 R/looic.R       |   2 +
 R/r2_bayes.R    |  98 ++++++++++++++++++++++------------------------
 man/looic.Rd    |   2 +
 man/r2_bayes.Rd | 102 ++++++++++++++++++++++++------------------------
 4 files changed, 101 insertions(+), 103 deletions(-)

diff --git a/R/looic.R b/R/looic.R
index 8f0a0c66e..4ded6ccd7 100644
--- a/R/looic.R
+++ b/R/looic.R
@@ -12,6 +12,7 @@
 #' @return A list with four elements, the ELPD, LOOIC and their standard errors.
 #'
 #' @examplesIf require("rstanarm")
+#' \donttest{
 #' model <- suppressWarnings(rstanarm::stan_glm(
 #'   mpg ~ wt + cyl,
 #'   data = mtcars,
@@ -20,6 +21,7 @@
 #'   refresh = 0
 #' ))
 #' looic(model)
+#' }
 #' @export
 looic <- function(model, verbose = TRUE) {
   insight::check_if_installed("loo")
diff --git a/R/r2_bayes.R b/R/r2_bayes.R
index fc007489c..4adf80da7 100644
--- a/R/r2_bayes.R
+++ b/R/r2_bayes.R
@@ -30,67 +30,63 @@
 #'   `r2_posterior()` is the actual workhorse for `r2_bayes()` and
 #'   returns a posterior sample of Bayesian R2 values.
 #'
-#' @examples
+#' @examplesIf require("rstanarm") && require("rstantools") && require("BayesFactor") && require("brms")
 #' library(performance)
-#' if (require("rstanarm") && require("rstantools")) {
-#'   model <- suppressWarnings(stan_glm(
-#'     mpg ~ wt + cyl,
-#'     data = mtcars,
-#'     chains = 1,
-#'     iter = 500,
-#'     refresh = 0,
-#'     show_messages = FALSE
-#'   ))
-#'   r2_bayes(model)
+#' \donttest{
+#' model <- suppressWarnings(rstanarm::stan_glm(
+#'   mpg ~ wt + cyl,
+#'   data = mtcars,
+#'   chains = 1,
+#'   iter = 500,
+#'   refresh = 0,
+#'   show_messages = FALSE
+#' ))
+#' r2_bayes(model)
 #'
-#'   model <- suppressWarnings(stan_lmer(
-#'     Petal.Length ~ Petal.Width + (1 | Species),
-#'     data = iris,
-#'     chains = 1,
-#'     iter = 500,
-#'     refresh = 0
-#'   ))
-#'   r2_bayes(model)
+#' model <- suppressWarnings(rstanarm::stan_lmer(
+#'   Petal.Length ~ Petal.Width + (1 | Species),
+#'   data = iris,
+#'   chains = 1,
+#'   iter = 500,
+#'   refresh = 0
+#' ))
+#' r2_bayes(model)
 #' }
 #'
-#' if (require("BayesFactor")) {
-#'   BFM <- generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
-#'   FM <- lmBF(mpg ~ qsec + gear, data = mtcars)
+#' BFM <- BayesFactor::generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
+#' FM <- BayesFactor::lmBF(mpg ~ qsec + gear, data = mtcars)
 #'
-#'   r2_bayes(FM)
-#'   r2_bayes(BFM[3])
-#'   r2_bayes(BFM, average = TRUE) # across all models
+#' r2_bayes(FM)
+#' r2_bayes(BFM[3])
+#' r2_bayes(BFM, average = TRUE) # across all models
 #'
-#'   # with random effects:
-#'   mtcars$gear <- factor(mtcars$gear)
-#'   model <- lmBF(
-#'     mpg ~ hp + cyl + gear + gear:wt,
-#'     mtcars,
-#'     progress = FALSE,
-#'     whichRandom = c("gear", "gear:wt")
-#'   )
+#' # with random effects:
+#' mtcars$gear <- factor(mtcars$gear)
+#' model <- BayesFactor::lmBF(
+#'   mpg ~ hp + cyl + gear + gear:wt,
+#'   mtcars,
+#'   progress = FALSE,
+#'   whichRandom = c("gear", "gear:wt")
+#' )
 #'
-#'   r2_bayes(model)
-#' }
+#' r2_bayes(model)
 #'
 #' \donttest{
-#' if (require("brms")) {
-#'   model <- suppressWarnings(brms::brm(
-#'     mpg ~ wt + cyl,
-#'     data = mtcars,
-#'     silent = 2,
-#'     refresh = 0
-#'   ))
-#'   r2_bayes(model)
+#' model <- suppressWarnings(brms::brm(
+#'   mpg ~ wt + cyl,
+#'   data = mtcars,
+#'   silent = 2,
+#'   refresh = 0
+#' ))
+#' r2_bayes(model)
 #'
-#'   model <- suppressWarnings(brms::brm(
-#'     Petal.Length ~ Petal.Width + (1 | Species),
-#'     data = iris,
-#'     silent = 2,
-#'     refresh = 0
-#'   ))
-#'   r2_bayes(model)
-#' }
+#' model <- suppressWarnings(brms::brm(
+#'   Petal.Length ~ Petal.Width + (1 | Species),
+#'   data = iris,
+#'   silent = 2,
+#'   refresh = 0
+#' ))
+#' r2_bayes(model)
 #' }
 #' @references
 #' Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018).
diff --git a/man/looic.Rd b/man/looic.Rd
index 742ac3482..7f985f4a2 100644
--- a/man/looic.Rd
+++ b/man/looic.Rd
@@ -22,6 +22,7 @@ indicative of a better fit.
 }
 \examples{
 \dontshow{if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+\donttest{
 model <- suppressWarnings(rstanarm::stan_glm(
   mpg ~ wt + cyl,
   data = mtcars,
@@ -30,5 +31,6 @@ model <- suppressWarnings(rstanarm::stan_glm(
   refresh = 0
 ))
 looic(model)
+}
 \dontshow{\}) # examplesIf}
 }
diff --git a/man/r2_bayes.Rd b/man/r2_bayes.Rd
index 63b082e08..edc78150e 100644
--- a/man/r2_bayes.Rd
+++ b/man/r2_bayes.Rd
@@ -62,67 +62,65 @@ R2 takes both the fixed and random effects into account.
 returns a posterior sample of Bayesian R2 values.
 }
 \examples{
+\dontshow{if (require("rstanarm") && require("rstantools") && require("BayesFactor") && require("brms")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 library(performance)
-if (require("rstanarm") && require("rstantools")) {
-  model <- suppressWarnings(stan_glm(
-    mpg ~ wt + cyl,
-    data = mtcars,
-    chains = 1,
-    iter = 500,
-    refresh = 0,
-    show_messages = FALSE
-  ))
-  r2_bayes(model)
-
-  model <- suppressWarnings(stan_lmer(
-    Petal.Length ~ Petal.Width + (1 | Species),
-    data = iris,
-    chains = 1,
-    iter = 500,
-    refresh = 0
-  ))
-  r2_bayes(model)
+\donttest{
+model <- suppressWarnings(rstanarm::stan_glm(
+  mpg ~ wt + cyl,
+  data = mtcars,
+  chains = 1,
+  iter = 500,
+  refresh = 0,
+  show_messages = FALSE
+))
+r2_bayes(model)
+
+model <- suppressWarnings(rstanarm::stan_lmer(
+  Petal.Length ~ Petal.Width + (1 | Species),
+  data = iris,
+  chains = 1,
+  iter = 500,
+  refresh = 0
+))
+r2_bayes(model)
 }
 
-if (require("BayesFactor")) {
-  BFM <- generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
-  FM <- lmBF(mpg ~ qsec + gear, data = mtcars)
+BFM <- BayesFactor::generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
+FM <- BayesFactor::lmBF(mpg ~ qsec + gear, data = mtcars)
 
-  r2_bayes(FM)
-  r2_bayes(BFM[3])
-  r2_bayes(BFM, average = TRUE) # across all models
+r2_bayes(FM)
+r2_bayes(BFM[3])
+r2_bayes(BFM, average = TRUE) # across all models
 
-  # with random effects:
-  mtcars$gear <- factor(mtcars$gear)
-  model <- lmBF(
-    mpg ~ hp + cyl + gear + gear:wt,
-    mtcars,
-    progress = FALSE,
-    whichRandom = c("gear", "gear:wt")
-  )
+# with random effects:
+mtcars$gear <- factor(mtcars$gear)
+model <- BayesFactor::lmBF(
+  mpg ~ hp + cyl + gear + gear:wt,
+  mtcars,
+  progress = FALSE,
+  whichRandom = c("gear", "gear:wt")
+)
 
-  r2_bayes(model)
-}
+r2_bayes(model)
 
 \donttest{
-if (require("brms")) {
-  model <- suppressWarnings(brms::brm(
-    mpg ~ wt + cyl,
-    data = mtcars,
-    silent = 2,
-    refresh = 0
-  ))
-  r2_bayes(model)
-
-  model <- suppressWarnings(brms::brm(
-    Petal.Length ~ Petal.Width + (1 | Species),
-    data = iris,
-    silent = 2,
-    refresh = 0
-  ))
-  r2_bayes(model)
-}
+model <- suppressWarnings(brms::brm(
+  mpg ~ wt + cyl,
+  data = mtcars,
+  silent = 2,
+  refresh = 0
+))
+r2_bayes(model)
+
+model <- suppressWarnings(brms::brm(
+  Petal.Length ~ Petal.Width + (1 | Species),
+  data = iris,
+  silent = 2,
+  refresh = 0
+))
+r2_bayes(model)
 }
+\dontshow{\}) # examplesIf}
 }
 \references{
 Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018).