diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index 5aea42373..2dddcc980 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -208,7 +208,7 @@ xgb.cv.mknfold <- function(dall, nfold, param) {
   return (ret)
 }
 xgb.cv.aggcv <- function(res, showsd = TRUE) {
-  header = res[[1]]
+  header <- res[[1]]
   ret <- header[1]
   for (i in 2:length(header)) {
     kv <- strsplit(header[i], ":")[[1]]
diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index dd0e2c891..9bd0f0468 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -18,6 +18,9 @@
 #'   further details. See also inst/examples/demo.R for walkthrough example in R.
 #' @param data takes an \code{xgb.DMatrix} as the input.
 #' @param nrounds the max number of iterations
+#' @param nfold number of folds used
+#' @param label option field, when data is Matrix
+#' @param showd boolean, whether show standard deviation of cross validation
 #' @param metrics, list of evaluation metrics to be used in corss validation,
 #'   when it is not specified, the evaluation metric is chosen according to objective function.
 #'   Possible options are:
@@ -28,7 +31,6 @@
 #'   \item \code{auc} Area under curve
 #'   \item \code{merror} Exact matching error, used to evaluate multi-class classification
 #' }
-#'
 #' @param obj customized objective function. Returns gradient and second order 
 #'   gradient with given prediction and dtrain, 
 #' @param feval custimized evaluation function. Returns 
@@ -47,13 +49,20 @@
 #' @export
 #'
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
-                   showsd = TRUE, obj = NULL, feval = NULL, ...) {
+                   showsd = TRUE, metrics=list(), obj = NULL, feval = NULL, ...) {
   if (typeof(params) != "list") {
     stop("xgb.cv: first argument params must be list")
   }
+  if (nfold <= 1) {
+    stop("nfold must be bigger than 1")
+  }
   dtrain <- xgb.get.DMatrix(data, label)
   params <- append(params, list(...))
   params <- append(params, list(silent=1))
+  for (mc in metrics) {
+    params <- append(params, list("eval_metric"=mc))
+  }    
+
   folds <- xgb.cv.mknfold(dtrain, nfold, params)
   history <- list()
   for (i in 1:nrounds) {
diff --git a/R-package/inst/examples/cross_validation.R b/R-package/inst/examples/cross_validation.R
index abe45354d..b46daa19f 100644
--- a/R-package/inst/examples/cross_validation.R
+++ b/R-package/inst/examples/cross_validation.R
@@ -3,7 +3,8 @@ require(methods)
 # Directly read in local file
 dtrain <- xgb.DMatrix("agaricus.txt.train")
 
-history <- xgb.cv(list("max_depth"=3, "eta"=1,
-                       "objective"="binary:logistic"),
-                  dtrain, nround=3, nfold = 5, "eval_metric"="error")
+history <- xgb.cv( data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
+                  "max_depth"=3, "eta"=1,
+                  "objective"="binary:logistic")
+