diff --git a/R-package/demo/cross_validation.R b/R-package/demo/cross_validation.R
index fbb38f6d8..c3148ae21 100644
--- a/R-package/demo/cross_validation.R
+++ b/R-package/demo/cross_validation.R
@@ -40,10 +40,10 @@ evalerror <- function(preds, dtrain) {
   return(list(metric = "error", value = err))
 }
 
-param <- list(max.depth=2,eta=1,silent=1)
+param <- list(max.depth=2,eta=1,silent=1,
+              objective = logregobj, eval_metric = evalerror)
 # train with customized objective
-xgb.cv(param, dtrain, nround, nfold = 5,
-       obj = logregobj, feval=evalerror)
+xgb.cv(param, dtrain, nround, nfold = 5)
 
 # do cross validation with prediction values for each fold
 res <- xgb.cv(param, dtrain, nround, nfold=5, prediction = TRUE)
diff --git a/R-package/demo/custom_objective.R b/R-package/demo/custom_objective.R
index cb90a7b5a..201f23d98 100644
--- a/R-package/demo/custom_objective.R
+++ b/R-package/demo/custom_objective.R
@@ -61,4 +61,5 @@ logregobjattr <- function(preds, dtrain) {
 print ('start training with user customized objective, with additional attributes in DMatrix')
 # 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, logregobjattr, evalerror)
+bst <- xgb.train(param, dtrain, num_round, watchlist, 
+                 objective=logregobj, eval_metric=evalerror)
diff --git a/R-package/demo/early_stopping.R b/R-package/demo/early_stopping.R
index 34dfebc0b..aa74aa2ee 100644
--- a/R-package/demo/early_stopping.R
+++ b/R-package/demo/early_stopping.R
@@ -31,9 +31,10 @@ evalerror <- function(preds, dtrain) {
   return(list(metric = "error", value = err))
 }
 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,
+
+bst <- xgb.train(param, dtrain, num_round, watchlist, 
+                 objective = logregobj, eval_metric = evalerror, maximize = FALSE,
                  early.stop.round = 3)
-bst <- xgb.cv(param, dtrain, num_round, nfold=5, obj=logregobj, feval = evalerror,
+bst <- xgb.cv(param, dtrain, num_round, nfold = 5, 
+              objective = logregobj, eval_metric = evalerror,
               maximize = FALSE, early.stop.round = 3)