From 9b6a14a99d685f0c313197f1b65c945d22627d7d Mon Sep 17 00:00:00 2001
From: El Potaeto <pommedeterresautee@msn.com>
Date: Mon, 29 Dec 2014 23:56:31 +0100
Subject: [PATCH] regeneration of documentation

---
 R-package/man/xgb.dump.Rd       | 17 +++++++++++------
 R-package/man/xgb.importance.Rd | 19 ++++++++++++-------
 2 files changed, 23 insertions(+), 13 deletions(-)

diff --git a/R-package/man/xgb.dump.Rd b/R-package/man/xgb.dump.Rd
index 9be2696b9..bcecc6abd 100644
--- a/R-package/man/xgb.dump.Rd
+++ b/R-package/man/xgb.dump.Rd
@@ -4,7 +4,7 @@
 \alias{xgb.dump}
 \title{Save xgboost model to text file}
 \usage{
-xgb.dump(model, fname, fmap = "")
+xgb.dump(model, fname, fmap = "", with.stats = FALSE)
 }
 \arguments{
 \item{model}{the model object.}
@@ -12,11 +12,16 @@ xgb.dump(model, fname, fmap = "")
 \item{fname}{the name of the binary file.}
 
 \item{fmap}{feature map file representing the type of feature.
-       Detailed description could be found at
-       \url{https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}.
-       See demo/ for walkthrough example in R, and
-       \url{https://github.com/tqchen/xgboost/blob/master/demo/data/featmap.txt}
-       for example Format.}
+Detailed description could be found at
+\url{https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}.
+See demo/ for walkthrough example in R, and
+\url{https://github.com/tqchen/xgboost/blob/master/demo/data/featmap.txt}
+for example Format.}
+
+\item{with.stats}{whether dump statistics of splits
+       When this option is on, the model dump comes with two additional statistics:
+       gain is the approximate loss function gain we get in each split;
+       cover is the sum of second order gradient in each node.}
 }
 \description{
 Save a xgboost model to text file. Could be parsed later.
diff --git a/R-package/man/xgb.importance.Rd b/R-package/man/xgb.importance.Rd
index 9609fe82f..7d02315f9 100644
--- a/R-package/man/xgb.importance.Rd
+++ b/R-package/man/xgb.importance.Rd
@@ -4,30 +4,35 @@
 \alias{xgb.importance}
 \title{Show importance of features in a model}
 \usage{
-xgb.importance(feature_names, filename_dump)
+xgb.importance(feature_names = NULL, filename_dump = NULL)
 }
 \arguments{
-\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix.}
+\item{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}.}
 
-\item{filename_dump}{the name of the text file.}
+\item{filename_dump}{the path to the text file storing the model.}
 }
 \description{
-Read a xgboost model in text file format. Return a data.table of the features with their weight.
+Read a xgboost model in text file format.
+Can be tree or linear model (text dump of linear model are only supported in dev version of Xgboost for now).
+}
+\details{
+Return a data.table of the features with their weight.
+#'
 }
 \examples{
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
 
-#Both dataset are list with two items, a sparse matrix and labels (outcome column which will be learned).
+#Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned).
 #Each column of the sparse Matrix is a feature in one hot encoding format.
 train <- agaricus.train
 test <- agaricus.test
 
 bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
                eta = 1, nround = 2,objective = "binary:logistic")
-xgb.dump(bst, 'xgb.model.dump')
+xgb.dump(bst, 'xgb.model.dump', with.stats = T)
 
-#agaricus.test$data@Dimnames[[2]] represents the column name of the sparse matrix.
+#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
 xgb.importance(agaricus.test$data@Dimnames[[2]], 'xgb.model.dump')
 }