diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 795a62eb1..099d8b4d0 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -36,7 +36,7 @@
 #' 3. Task Parameters 
 #' 
 #' \itemize{
-#' \item \code{objective} specify the learning task and the corresponding learning objective, and the objective options are below:
+#' \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
 #'   \itemize{
 #'     \item \code{reg:linear} linear regression (Default).
 #'     \item \code{reg:logistic} logistic regression.
@@ -48,7 +48,7 @@
 #'     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
 #'   }
 #'   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
-#'   \item \code{eval_metric} evaluation metrics for validation data. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
+#'   \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
 #' }
 #' 
 #' @param data takes an \code{xgb.DMatrix} as the input.
@@ -103,7 +103,6 @@
 #' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 #' dtest <- dtrain
 #' watchlist <- list(eval = dtest, train = dtrain)
-#' param <- list(max.depth = 2, eta = 1, silent = 1)
 #' logregobj <- function(preds, dtrain) {
 #'    labels <- getinfo(dtrain, "label")
 #'    preds <- 1/(1 + exp(-preds))
@@ -116,7 +115,8 @@
 #'   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
 #'   return(list(metric = "error", value = err))
 #' }
-#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
+#' param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
+#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
 #' @export
 #' 
 xgb.train <- function(params=list(), data, nrounds, watchlist = list(), 
diff --git a/R-package/demo/custom_objective.R b/R-package/demo/custom_objective.R
index b0a0a02ca..cb90a7b5a 100644
--- a/R-package/demo/custom_objective.R
+++ b/R-package/demo/custom_objective.R
@@ -8,7 +8,6 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 # note: for customized objective function, we leave objective as default
 # note: what we are getting is margin value in prediction
 # you must know what you are doing
-param <- list(max.depth=2,eta=1,nthread = 2, silent=1)
 watchlist <- list(eval = dtest, train = dtrain)
 num_round <- 2
 
@@ -33,10 +32,13 @@ evalerror <- function(preds, dtrain) {
   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
   return(list(metric = "error", value = err))
 }
+
+param <- list(max.depth=2,eta=1,nthread = 2, silent=1, 
+              objective=logregobj, eval_metric=evalerror)
 print ('start training with user customized objective')
 # 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)
+bst <- xgb.train(param, dtrain, num_round, watchlist)
 
 #
 # there can be cases where you want additional information 
diff --git a/R-package/man/xgb.cv.Rd b/R-package/man/xgb.cv.Rd
index 389cadef0..f1fe97563 100644
--- a/R-package/man/xgb.cv.Rd
+++ b/R-package/man/xgb.cv.Rd
@@ -7,8 +7,8 @@
 xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
   missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
-  verbose = T, printEveryN=1L, early_stop_round = NULL, early.stop.round = NULL,
-  maximize = NULL, ...)
+  verbose = T, printEveryN = 1L, early_stop_round = NULL,
+  early.stop.round = NULL, maximize = NULL, ...)
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:
diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd
index 74dca1c49..4d5d8d3e6 100644
--- a/R-package/man/xgb.train.Rd
+++ b/R-package/man/xgb.train.Rd
@@ -5,7 +5,7 @@
 \title{eXtreme Gradient Boosting Training}
 \usage{
 xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
-  feval = NULL, verbose = 1, printEveryN=1L, early_stop_round = NULL,
+  feval = NULL, verbose = 1, printEveryN = 1L, early_stop_round = NULL,
   early.stop.round = NULL, maximize = NULL, ...)
 }
 \arguments{
@@ -43,7 +43,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
 3. Task Parameters
 
 \itemize{
-\item \code{objective} specify the learning task and the corresponding learning objective, and the objective options are below:
+\item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
   \itemize{
     \item \code{reg:linear} linear regression (Default).
     \item \code{reg:logistic} logistic regression.
@@ -55,7 +55,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
   }
   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
-  \item \code{eval_metric} evaluation metrics for validation data. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
+  \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
 }}
 
 \item{data}{takes an \code{xgb.DMatrix} as the input.}
@@ -122,7 +122,6 @@ data(agaricus.train, package='xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- dtrain
 watchlist <- list(eval = dtest, train = dtrain)
-param <- list(max.depth = 2, eta = 1, silent = 1)
 logregobj <- function(preds, dtrain) {
    labels <- getinfo(dtrain, "label")
    preds <- 1/(1 + exp(-preds))
@@ -135,6 +134,7 @@ evalerror <- function(preds, dtrain) {
   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
   return(list(metric = "error", value = err))
 }
-bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
+param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
+bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
 }
 
diff --git a/R-package/man/xgboost.Rd b/R-package/man/xgboost.Rd
index bf9cb5c91..7371522fd 100644
--- a/R-package/man/xgboost.Rd
+++ b/R-package/man/xgboost.Rd
@@ -5,8 +5,8 @@
 \title{eXtreme Gradient Boosting (Tree) library}
 \usage{
 xgboost(data = NULL, label = NULL, missing = NULL, params = list(),
-  nrounds, verbose = 1, printEveryN=1L, early_stop_round = NULL, early.stop.round = NULL,
-  maximize = NULL, ...)
+  nrounds, verbose = 1, printEveryN = 1L, early_stop_round = NULL,
+  early.stop.round = NULL, maximize = NULL, ...)
 }
 \arguments{
 \item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or