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nicholasjclark · Jun 30, 2024 · bea6bc7 · bea6bc7
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diff --git a/R/forecast.mvgam.R b/R/forecast.mvgam.R
@@ -28,13 +28,12 @@ forecast <- function(object, ...){
 #'@rdname forecast.mvgam
 #'@method forecast mvgam
 #' @examples
-#' \dontrun{
-#' simdat <- sim_mvgam(n_series = 3, trend_model = 'AR1')
+#' \donttest{
+#' simdat <- sim_mvgam(n_series = 3, trend_model = AR())
 #' mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
 #'             trend_model = AR(),
+#'             noncentred = TRUE,
 #'             data = simdat$data_train,
-#'             burnin = 300,
-#'             samples = 300,
 #'             chains = 2)
 #'
 #' # Hindcasts on response scale

diff --git a/R/hindcast.mvgam.R b/R/hindcast.mvgam.R
@@ -7,7 +7,8 @@
 #'organized into a convenient format
 #'@return An object of class \code{mvgam_forecast} containing hindcast distributions.
 #'See \code{\link{mvgam_forecast-class}} for details.
-#'#'@seealso \code{\link{forecast.mvgam}}
+#'
+#'@seealso \code{\link{forecast.mvgam}}
 #'@export
 hindcast <- function(object, ...){
   UseMethod("hindcast", object)
@@ -16,13 +17,12 @@ hindcast <- function(object, ...){
 #'@rdname hindcast.mvgam
 #'@method hindcast mvgam
 #' @examples
-#' \dontrun{
-#' simdat <- sim_mvgam(n_series = 3, trend_model = 'AR1')
+#' \donttest{
+#' simdat <- sim_mvgam(n_series = 3, trend_model = AR())
 #' mod <- mvgam(y ~ s(season, bs = 'cc'),
 #'             trend_model = AR(),
+#'             noncentred = TRUE,
 #'             data = simdat$data_train,
-#'             burnin = 300,
-#'             samples = 300,
 #'             chains = 2)
 #'
 #' # Hindcasts on response scale

diff --git a/R/loo.mvgam.R b/R/loo.mvgam.R
@@ -39,7 +39,7 @@
 #' forecast evaluations for further scrutiny of models (see for example \code{\link{forecast.mvgam}},
 #' \code{\link{score.mvgam_forecast}} and \code{\link{lfo_cv}})
 #'@examples
-#'\dontrun{
+#'\donttest{
 #'# Simulate 4 time series with hierarchical seasonality
 #'# and independent AR1 dynamic processes
 #'set.seed(111)

diff --git a/R/mcmc_plot.mvgam.R b/R/mcmc_plot.mvgam.R
@@ -23,12 +23,11 @@
 #' that can be used for argument `variable`
 #' @examples
 #' \dontrun{
-#' simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
+#' simdat <- sim_mvgam(n_series = 1, trend_model = AR())
 #' mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
 #'              trend_model = AR(),
+#'              noncentred = TRUE,
 #'              data = simdat$data_train,
-#'              burnin = 300,
-#'              samples = 300,
 #'              chains = 2)
 #' mcmc_plot(mod)
 #' mcmc_plot(mod, type = 'neff_hist')

diff --git a/R/monotonic.R b/R/monotonic.R
@@ -44,7 +44,7 @@
 #' @author Nicholas J Clark
 #' @name monotonic
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' # Simulate data from a monotonically increasing function
 #' set.seed(123123)
 #' x <- runif(80) * 4 - 1
@@ -72,8 +72,6 @@
 #' mod2 <- mvgam(y ~ s(x, bs = 'moi', k = 18),
 #'              data = mod_data,
 #'              family = gaussian(),
-#'              burnin = 300,
-#'              samples = 300,
 #'              chains = 2)
 #'
 #' plot_predictions(mod2,
@@ -108,8 +106,6 @@
 #' mod <- mvgam(y ~ s(x, bs = 'moi', by = fac, k = 8),
 #'              data = mod_data,
 #'              family = gaussian(),
-#'              burnin = 300,
-#'              samples = 300,
 #'              chains = 2)
 #'
 #' # Visualise the different monotonic functions

diff --git a/R/ti.R b/R/ti.R
@@ -18,7 +18,7 @@
 #'
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' # Simulate some data
 #' dat <- mgcv::gamSim(1, n = 200, scale = 2)
 #'

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