fix issue #956

DESCRIPTION

@@ -78,4 +78,4 @@ Additional_repositories:
 VignetteBuilder: 
     knitr,
     R.rsp
-RoxygenNote: 7.1.0
+RoxygenNote: 7.1.1

R/log_lik.R

@@ -26,6 +26,9 @@
 #'  If \code{pointwise = TRUE}, the output is a function
 #'  with a \code{draws} attribute containing all relevant
 #'  data and posterior samples.
+#'  
+#' @template details-newdata-na
+#' @template details-allow_new_levels
 #' 
 #' @aliases log_lik
 #' @method log_lik brmsfit

R/posterior_epred.R

@@ -25,10 +25,8 @@
 #'   categories. In multivariate models, an additional dimension is added to the
 #'   output which indexes along the different response variables.
 #'   
-#' @details \code{NA} values within factors in \code{newdata}, 
-#'   are interpreted as if all dummy variables of this factor are 
-#'   zero. This allows, for instance, to make predictions of the grand mean 
-#'   when using sum coding.
+#' @template details-newdata-na
+#' @template details-allow_new_levels
 #'
 #' @examples 
 #' \dontrun{

R/posterior_predict.R

@@ -29,27 +29,24 @@
 #' @param ... Further arguments passed to \code{\link{prepare_predictions}}
 #'   that control several aspects of data validation and prediction.
 #' 
-#' @return An \code{array} of predicted response values. If \code{summary =
-#'   FALSE}, the output is as an S x N matrix, where S is the number of
-#'   posterior samples and N is the number of observations. In multivariate
-#'   models, an additional dimension is added to the output which indexes along
-#'   the different response variables.
-#' 
-#' @details \code{NA} values within factors in \code{newdata}, 
-#'   are interpreted as if all dummy variables of this factor are 
-#'   zero. This allows, for instance, to make predictions of the grand mean 
-#'   when using sum coding.  
-#' 
-#'   For truncated discrete models only: In the absence of any general algorithm
-#'   to sample from truncated discrete distributions, rejection sampling is
-#'   applied in this special case. This means that values are sampled until a
-#'   value lies within the defined truncation boundaries. In practice, this
-#'   procedure may be rather slow (especially in \R). Thus, we try to do
-#'   approximate rejection sampling by sampling each value \code{ntrys} times
-#'   and then select a valid value. If all values are invalid, the closest
-#'   boundary is used, instead. If there are more than a few of these
-#'   pathological cases, a warning will occur suggesting to increase argument
-#'   \code{ntrys}.
+#' @return An \code{array} of predicted response values. In univariate models,
+#'   the output is as an S x N matrix, where S is the number of posterior
+#'   samples and N is the number of observations. In multivariate models, an
+#'   additional dimension is added to the output which indexes along the
+#'   different response variables.
+#'   
+#' @template details-newdata-na
+#' @template details-allow_new_levels
+#' @details For truncated discrete models only: In the absence of any general
+#'   algorithm to sample from truncated discrete distributions, rejection
+#'   sampling is applied in this special case. This means that values are
+#'   sampled until a value lies within the defined truncation boundaries. In
+#'   practice, this procedure may be rather slow (especially in \R). Thus, we
+#'   try to do approximate rejection sampling by sampling each value
+#'   \code{ntrys} times and then select a valid value. If all values are
+#'   invalid, the closest boundary is used, instead. If there are more than a
+#'   few of these pathological cases, a warning will occur suggesting to
+#'   increase argument \code{ntrys}.
 #' 
 #' @examples 
 #' \dontrun{

man-roxygen/details-allow_new_levels.R

@@ -0,0 +1,8 @@
+#' @details 
+#' In multilevel models, it is possible to 
+#' allow new levels of grouping factors to be used in the predictions.
+#' This can be controlled via argument \code{allow_new_levels}.
+#' New levels can be sampled in multiple ways, which can be controlled
+#' via argument \code{sample_new_levels}. Both of these arguments are
+#' documented in \code{\link{prepare_predictions}} along with several
+#' other useful arguments to control specific aspects of the predictions.

man-roxygen/details-newdata-na.R

@@ -0,0 +1,4 @@
+#' @details \code{NA} values within factors in \code{newdata}, 
+#'   are interpreted as if all dummy variables of this factor are 
+#'   zero. This allows, for instance, to make predictions of the grand mean 
+#'   when using sum coding.