documentation update

R-package/R/predict.xgb.Booster.R

@@ -7,6 +7,8 @@ setClass("xgb.Booster")
 #' @param object Object of class "xgb.Boost"
 #' @param newdata takes \code{matrix}, \code{dgCMatrix}, local data file or 
 #'   \code{xgb.DMatrix}. 
+#' @param missing Missing is only used when input is dense matrix, pick a float
+#     value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
 #' @param outputmargin whether the prediction should be shown in the original
 #'   value of sum of functions, when outputmargin=TRUE, the prediction is 
 #'   untransformed margin value. In logistic regression, outputmargin=T will

R-package/R/xgb.cv.R

@@ -32,7 +32,7 @@
 #' @param nfold number of folds used
 #' @param label option field, when data is Matrix
 #' @param missing Missing is only used when input is dense matrix, pick a float
-#'    value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
+#     value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
 #' @param prediction A logical value indicating whether to return the prediction vector.
 #' @param showsd \code{boolean}, whether show standard deviation of cross validation
 #' @param metrics, list of evaluation metrics to be used in corss validation,
@@ -50,8 +50,6 @@
 #' @param feval custimized evaluation function. Returns 
 #'   \code{list(metric='metric-name', value='metric-value')} with given 
 #'   prediction and dtrain,
-#' @param missing Missing is only used when input is dense matrix, pick a float
-#     value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
 #' @param verbose \code{boolean}, print the statistics during the process.
 #' @param ... other parameters to pass to \code{params}.
 #' 

R-package/R/xgb.importance.R

@@ -33,7 +33,7 @@
 #' data(agaricus.test, package='xgboost')
 #' 
 #' #Both dataset are list with two items, a sparse matrix and labels 
-#'   (labels = outcome column which will be learned). 
+#' #(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

R-package/R/xgb.model.dt.tree.R

@@ -43,7 +43,7 @@
 #' data(agaricus.train, package='xgboost')
 #' 
 #' #Both dataset are list with two items, a sparse matrix and labels 
-#'   (labels = outcome column which will be learned). 
+#' #(labels = outcome column which will be learned). 
 #' #Each column of the sparse Matrix is a feature in one hot encoding format.
 #' train <- agaricus.train
 #' 

R-package/R/xgb.plot.tree.R

@@ -43,7 +43,7 @@
 #' data(agaricus.train, package='xgboost')
 #' 
 #' #Both dataset are list with two items, a sparse matrix and labels 
-#'   (labels = outcome column which will be learned). 
+#' #(labels = outcome column which will be learned). 
 #' #Each column of the sparse Matrix is a feature in one hot encoding format.
 #' train <- agaricus.train
 #'