fix relative to examples #Rstat

R-package/man/xgb.importance.Rd

@@ -54,9 +54,8 @@ data(agaricus.train, package='xgboost')
 # Both dataset are list with two items, a sparse matrix and labels 
 # (labels = outcome column which will be learned). 
 # Each column of the sparse Matrix is a feature in one hot encoding format.
-train <- agaricus.train
 
-bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 
 # train$data@Dimnames[[2]] represents the column names of the sparse matrix.

R-package/man/xgb.plot.deepness.Rd

@@ -35,7 +35,7 @@ This function is inspired by this blog post \url{http://aysent.github.io/2015/11
 \examples{
 data(agaricus.train, package='xgboost')
 
-bst <- xgboost(data = agaricus.train$data, max.depth = 15,
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
                  eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
                  min_child_weight = 50)
 

R-package/man/xgb.plot.tree.Rd

@@ -43,9 +43,8 @@ data(agaricus.train, package='xgboost')
 #Both dataset are list with two items, a sparse matrix and labels 
 #(labels = outcome column which will be learned). 
 #Each column of the sparse Matrix is a feature in one hot encoding format.
-train <- agaricus.train
 
-bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 
 #agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.