diff --git a/R-package/R/xgb.importance.R b/R-package/R/xgb.importance.R
index b2e60bed7..2071680d3 100644
--- a/R-package/R/xgb.importance.R
+++ b/R-package/R/xgb.importance.R
@@ -2,7 +2,6 @@
 #' 
 #' Read a xgboost model text dump. 
 #' Can be tree or linear model (text dump of linear model are only supported in dev version of \code{Xgboost} for now).
-#' Return a data.table of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
 #' 
 #' @importFrom data.table data.table
 #' @importFrom magrittr %>%
@@ -11,6 +10,8 @@
 #' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
 #' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).
 #'
+#' @return A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
+#'
 #' @details 
 #' This is the function to understand the model trained (and through your model, your data).
 #' 
diff --git a/R-package/man/xgb.importance.Rd b/R-package/man/xgb.importance.Rd
index 883819993..a7a71cefc 100644
--- a/R-package/man/xgb.importance.Rd
+++ b/R-package/man/xgb.importance.Rd
@@ -11,10 +11,12 @@ xgb.importance(feature_names = NULL, filename_dump = NULL)
 
 \item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).}
 }
+\value{
+A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
+}
 \description{
 Read a xgboost model text dump.
 Can be tree or linear model (text dump of linear model are only supported in dev version of \code{Xgboost} for now).
-Return a data.table of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
 }
 \details{
 This is the function to understand the model trained (and through your model, your data).