use refresh=0 for stan_glm in examples

Author: jgabry

R/bayesplot-extractors.R

@@ -36,7 +36,7 @@
 #' @examples
 #' \dontrun{
 #' library(rstanarm)
-#' fit <- stan_glm(mpg ~ wt, data = mtcars)
+#' fit <- stan_glm(mpg ~ wt, data = mtcars, refresh = 0)
 #'
 #' np <- nuts_params(fit)
 #' head(np)

R/bayesplot_grid.R

@@ -42,8 +42,8 @@
 #' \dontrun{
 #' library(rstanarm)
 #' mtcars$log_mpg <- log(mtcars$mpg)
-#' fit1 <- stan_glm(mpg ~ wt, data = mtcars)
-#' fit2 <- stan_glm(log_mpg ~ wt, data = mtcars)
+#' fit1 <- stan_glm(mpg ~ wt, data = mtcars, refresh = 0)
+#' fit2 <- stan_glm(log_mpg ~ wt, data = mtcars, refresh = 0)
 #'
 #' y <- mtcars$mpg
 #' yrep1 <- posterior_predict(fit1, draws = 50)

R/mcmc-diagnostics-nuts.R

@@ -113,7 +113,7 @@
 #' \dontrun{
 #' library(ggplot2)
 #' library(rstanarm)
-#' fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000)
+#' fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000, refresh = 0)
 #' np <- nuts_params(fit)
 #' lp <- log_posterior(fit)
 #'

R/mcmc-diagnostics.R

@@ -100,7 +100,7 @@
 #'
 #' # intentionally use small 'iter' so there are some
 #' # problems with rhat and neff for demonstration
-#' fit <- stan_glm(mpg ~ ., data = mtcars, iter = 50)
+#' fit <- stan_glm(mpg ~ ., data = mtcars, iter = 50, refresh = 0)
 #' rhats <- rhat(fit)
 #' ratios <- neff_ratio(fit)
 #' mcmc_rhat(rhats)

R/mcmc-intervals.R

@@ -131,7 +131,8 @@
 #' fit <- stan_glm(
 #'  mpg ~ 0 + wt + factor(cyl),
 #'  data = mtcars,
-#'  iter = 500
+#'  iter = 500,
+#'  refresh = 0
 #' )
 #' x <- as.matrix(fit)
 #'

R/mcmc-recover.R

@@ -58,7 +58,7 @@
 #' alpha <- 1; beta <- rnorm(10, 0, 3); sigma <- 2
 #' X <- matrix(rnorm(1000), 100, 10)
 #' y <- rnorm(100, mean = c(alpha + X %*% beta), sd = sigma)
-#' fit <- stan_glm(y ~ ., data = data.frame(y, X))
+#' fit <- stan_glm(y ~ ., data = data.frame(y, X), refresh = 0)
 #' draws <- as.matrix(fit)
 #' print(colnames(draws))
 #' true <- c(alpha, beta, sigma)

R/mcmc-scatterplots.R

@@ -241,7 +241,7 @@ mcmc_hex <- function(x,
 #' # will (almost certainly) have some divergences
 #' fit <- stan_glm(
 #'   mpg ~ ., data = mtcars,
-#'   iter = 1000,
+#'   iter = 1000, refresh = 0,
 #'   # this combo of prior and adapt_delta should lead to some divergences
 #'   prior = hs(),
 #'   adapt_delta = 0.9

R/mcmc-traces.R

@@ -106,7 +106,7 @@
 #'
 #' # for models fit using HMC/NUTS divergences can be displayed in the trace plot
 #' library("rstanarm")
-#' fit <- stan_glm(mpg ~ ., data = mtcars,
+#' fit <- stan_glm(mpg ~ ., data = mtcars, refresh = 0,
 #'   # next line to keep example fast and also ensure we get some divergences
 #'                 prior = hs(), iter = 400, adapt_delta = 0.8)
 #'

R/ppc-intervals.R

@@ -81,7 +81,7 @@
 #'
 #' \dontrun{
 #' library("rstanarm")
-#' fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars)
+#' fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars, refresh = 0)
 #' yrep <- posterior_predict(fit)
 #'
 #' color_scheme_set("purple")