get a pass in function docstring

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

@@ -8,7 +8,9 @@ setClass("xgb.Booster")
 #' @param newdata takes \code{matrix}, \code{dgCMatrix}, local data file or 
 #'   \code{xgb.DMatrix}. 
 #' @param outputmargin whether the prediction should be shown in the original
-#'   value or after the logit mapping
+#'   value of sum of functions, when outputmargin=TRUE, the prediction is 
+#'   untransformed margin value. In logistic regression, outputmargin=T will
+#'   output value before logistic transformation.
 #'
 #' @section Value
 #' return a numerical vector.

R-package/R/xgb.DMatrix.R

@@ -5,8 +5,9 @@
 #' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character 
 #'   indicating the data file.
 #' @param info a list of information of the xgb.DMatrix object
-#' @param missing Sometime a data use 0 or other extreme value to represents
-#'   missing values.
+#' @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 ... other information to pass to \code{info}.
 #' 
 #' @examples

R-package/R/xgb.dump.R

@@ -4,7 +4,9 @@
 #' 
 #' @param model the model object.
 #' @param fname the name of the binary file.
-#' @param fmap TODO(tqchen)
+#' @param fmap feature map file representing the type of feature, to make it
+#'        look nice, run demo/demo.R for result and demo/featmap.txt for example
+#'        Format: https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model
 #'
 #' @examples
 #' data(iris)

R-package/R/xgb.train.R

@@ -8,9 +8,13 @@
 #' @param dtrain takes an \code{xgb.DMatrix} as the input.
 #' @param nrounds the max number of iterations
 #' @param watchlist what information should be printed when \code{verbose=1} or
-#'   \code{verbose=2}.
+#'   \code{verbose=2}. Watchlist is used to specify validation set monitoring
+#'   during training. For example user can specify
+#'    watchlist=list(validation1=mat1, validation2=mat2) to watch
+#'    the performance of each round's model on mat1 and mat2
+#'
 #' @param obj customized objective function. Given prediction and dtrain, 
-#'   return gradient and second order gradient
+#'   return gradient and second order gradient.
 #' @param feval custimized evaluation function. Given prediction and dtrain,
 #'   return a \code{list(metric='metric-name', value='metric-value')}.
 #' @param ... other parameters to pass to \code{params}.
@@ -21,7 +25,8 @@
 #' Parallelization is automatically enabled under Linux/Windows. Mac users can 
 #' also enjoy this feature if compile this package with openmp.
 #' 
-#' This function only accepts an \code{xgb.DMatrix} object as the input, 
+#' This function only accepts an \code{xgb.DMatrix} object as the input.
+#' It supports advanced features such as watchlist, customized objective function,
 #' therefore it is more flexible than \code{\link{xgboost}}.
 #' 
 #' @section Value

R-package/R/xgboost.R

@@ -4,22 +4,24 @@
 #' 
 #' @param data takes \code{matrix}, \code{dgCMatrix}, local data file or 
 #'   \code{xgb.DMatrix}. 
-#' @param label the response variable. Not required if data is local data file 
-#'   or \code{xgb.DMatrix}. 
-#' @param params the list of parameters. See 
+#' @param label the response variable. User should not set this field,
+#    if data is local data file or  \code{xgb.DMatrix}. 
+#' @param params the list of parameters.  See 
 #'   \url{https://github.com/tqchen/xgboost/wiki/Parameters} for 
-#'   further details.
+#'   further details. See also demo/demo.R for walkthrough example in R.
 #' @param nrounds the max number of iterations
 #' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print 
 #'   information of performance. If 2, xgboost will print information of both
-#'   performance and tree.
+#'   performance and construction progress information
 #' @param ... other parameters to pass to \code{params}.
 #' 
 #' @details 
 #' This is the modeling function for xgboost.
 #' 
-#' Parallelization is automatically enabled under Linux/Windows. Mac users can 
-#' also enjoy this feature if compile this package with openmp.
+#' Parallelization is automatically enabled under Linux/Windows.
+#' Number of threads can also be manually specified via nthread parameter
+#' Mac users can also enjoy this feature if R CMD COMPILE support compile
+#' package with openmp.
 #' 
 #' @section Value
 #' return a \code{xgb.DMatrix} class object.