diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index 2dee9d9f8..cc0e42a93 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -196,7 +196,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         bestScore = score
         bestInd = i
       } else {
-        if (i-bestInd>early_stop_round) {
+        if (i-bestInd>=early_stop_round) {
           earlyStopflag = TRUE
           cat('Stopping. Best iteration:',bestInd)
           break
diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index f6e60e23d..8ed0099a2 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -187,7 +187,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
           bestScore = score
           bestInd = i
         } else {
-          if (i-bestInd>early_stop_round) {
+          if (i-bestInd>=early_stop_round) {
             earlyStopflag = TRUE
             cat('Stopping. Best iteration:',bestInd)
             break
diff --git a/R-package/demo/early_Stopping.R b/R-package/demo/early_Stopping.R
index 692b413aa..34dfebc0b 100644
--- a/R-package/demo/early_Stopping.R
+++ b/R-package/demo/early_Stopping.R
@@ -34,6 +34,6 @@ print ('start training with early Stopping setting')
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train
 bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror, maximize = FALSE,
-                 earlyStopRound = 3)
+                 early.stop.round = 3)
 bst <- xgb.cv(param, dtrain, num_round, nfold=5, obj=logregobj, feval = evalerror,
-              maximize = FALSE, earlyStopRound = 3)
+              maximize = FALSE, early.stop.round = 3)