examples

R/sexit_thresholds.R

@@ -12,16 +12,18 @@
 #' sexit_thresholds(rnorm(1000))
 #' \dontrun{
 #' if (require("rstanarm")) {
-#'   model <- stan_glm(
+#'   model <- suppressWarnings(stan_glm(
 #'     mpg ~ wt + gear,
 #'     data = mtcars,
 #'     chains = 2,
 #'     iter = 200,
 #'     refresh = 0
-#'   )
+#'   ))
 #'   sexit_thresholds(model)
 #'
-#'   model <- stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
+#'   model <- suppressWarnings(
+#'     stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
+#'   )
 #'   sexit_thresholds(model)
 #' }
 #'

R/simulate_priors.R

@@ -12,7 +12,9 @@
 #' \dontrun{
 #' library(bayestestR)
 #' if (require("rstanarm")) {
-#'   model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
+#'   model <- suppressWarnings(
+#'     stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
+#'   )
 #'   simulate_prior(model)
 #' }
 #' }

R/spi.R

@@ -35,7 +35,9 @@
 #' spi(df, ci = c(0.80, 0.89, 0.95))
 #' \dontrun{
 #' library(rstanarm)
-#' model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
+#' model <- suppressWarnings(
+#'   stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
+#' )
 #' spi(model)
 #' }
 #'

R/weighted_posteriors.R

@@ -41,19 +41,19 @@
 #' @examples
 #' \donttest{
 #' if (require("rstanarm") && require("see")) {
-#'   stan_m0 <- stan_glm(extra ~ 1,
+#'   stan_m0 <- suppressWarnings(stan_glm(extra ~ 1,
 #'     data = sleep,
 #'     family = gaussian(),
 #'     refresh = 0,
 #'     diagnostic_file = file.path(tempdir(), "df0.csv")
-#'   )
+#'   ))
 #'
-#'   stan_m1 <- stan_glm(extra ~ group,
+#'   stan_m1 <- suppressWarnings(stan_glm(extra ~ group,
 #'     data = sleep,
 #'     family = gaussian(),
 #'     refresh = 0,
 #'     diagnostic_file = file.path(tempdir(), "df1.csv")
-#'   )
+#'   ))
 #'
 #'   res <- weighted_posteriors(stan_m0, stan_m1)
 #'
@@ -73,21 +73,21 @@
 #'
 #' ## weighted prediction distributions via data.frames
 #' if (require("rstanarm")) {
-#'   m0 <- stan_glm(
+#'   m0 <- suppressWarnings(stan_glm(
 #'     mpg ~ 1,
 #'     data = mtcars,
 #'     family = gaussian(),
 #'     diagnostic_file = file.path(tempdir(), "df0.csv"),
 #'     refresh = 0
-#'   )
+#'   ))
 #'
-#'   m1 <- stan_glm(
+#'   m1 <- suppressWarnings(stan_glm(
 #'     mpg ~ carb,
 #'     data = mtcars,
 #'     family = gaussian(),
 #'     diagnostic_file = file.path(tempdir(), "df1.csv"),
 #'     refresh = 0
-#'   )
+#'   ))
 #'
 #'   # Predictions:
 #'   pred_m0 <- data.frame(posterior_predict(m0))