Merge remote-tracking branch 'upstream/master'

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

@@ -7,8 +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 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/slice.xgb.DMatrix.R

@@ -28,6 +28,18 @@ setMethod("slice", signature = "xgb.DMatrix",
               if (class(object) != "xgb.DMatrix") {
                   stop("slice: first argument dtrain must be xgb.DMatrix")
               }
-              ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset, PACKAGE = "xgboost")
+              ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset, 
+                           PACKAGE = "xgboost")
+              
+              attr_list <- attributes(object)
+              nr <- xgb.numrow(object)
+              len <- sapply(attr_list,length)
+              ind <- which(len==nr)
+              if (length(ind)>0) {
+                  nms <- names(attr_list)[ind]
+                  for (i in 1:length(ind)) {
+                    attr(ret,nms[i]) <- attr(object,nms[i])[idxset]
+                  }
+              }
               return(structure(ret, class = "xgb.DMatrix"))
           })

R-package/R/xgb.DMatrix.R

@@ -6,7 +6,7 @@
 #'   indicating the data file.
 #' @param info a list of information of the xgb.DMatrix object
 #' @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 ... other information to pass to \code{info}.
 #' 

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,
@@ -134,4 +134,4 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(".")
+globalVariables(".")

R-package/R/xgboost.R

@@ -5,7 +5,7 @@
 #' @param data takes \code{matrix}, \code{dgCMatrix}, local data file or 
 #'   \code{xgb.DMatrix}. 
 #' @param label the response variable. User should not set this field,
-#    if data is local data file or  \code{xgb.DMatrix}. 
+#'    if data is local data file or  \code{xgb.DMatrix}. 
 #' @param params the list of parameters. Commonly used ones are:
 #' \itemize{
 #'   \item \code{objective} objective function, common ones are
@@ -24,8 +24,8 @@
 #' @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 construction progress information
-#' @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 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 parameters to pass to \code{params}.
 #' 
 #' @details