To submit to CRAN we cannot use more than 2 threads in our examples/vignettes

R-package/R/predict.xgb.Booster.R

@@ -26,7 +26,7 @@ setClass("xgb.Booster",
 #' train <- agaricus.train
 #' test <- agaricus.test
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' pred <- predict(bst, test$data)
 #' @export
 #' 

R-package/R/xgb.cv.R

@@ -66,7 +66,7 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-#' history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
+#' history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
 #'                   "max.depth"=3, "eta"=1, "objective"="binary:logistic")
 #' print(history)
 #' @export

R-package/R/xgb.dump.R

@@ -29,7 +29,7 @@
 #' train <- agaricus.train
 #' test <- agaricus.test
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' # save the model in file 'xgb.model.dump'
 #' xgb.dump(bst, 'xgb.model.dump', with.stats = TRUE)
 #' 
@@ -68,4 +68,4 @@ xgb.dump <- function(model = NULL, fname = NULL, fmap = "", with.stats=FALSE) {
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c("Lines", "."))
+globalVariables(c("Lines", "."))

R-package/R/xgb.importance.R

@@ -57,7 +57,7 @@
 #' train <- agaricus.train
 #' 
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' 
 #' # train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
 #' xgb.importance(train$data@@Dimnames[[2]], model = bst)

R-package/R/xgb.load.R

@@ -10,7 +10,7 @@
 #' train <- agaricus.train
 #' test <- agaricus.test
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
 #' pred <- predict(bst, test$data)

R-package/R/xgb.model.dt.tree.R

@@ -49,7 +49,7 @@
 #' train <- agaricus.train
 #' 
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' 
 #' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
 #' xgb.model.dt.tree(agaricus.train$data@@Dimnames[[2]], model = bst)
@@ -166,4 +166,4 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c("ID", "Tree", "Yes", ".", ".N", "Feature", "Cover", "Quality", "No", "Gain", "Frequence"))
+globalVariables(c("ID", "Tree", "Yes", ".", ".N", "Feature", "Cover", "Quality", "No", "Gain", "Frequence"))

R-package/R/xgb.plot.importance.R

@@ -33,7 +33,7 @@
 #' train <- agaricus.train
 #' 
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' 
 #' #train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
 #' importance_matrix <- xgb.importance(train$data@@Dimnames[[2]], model = bst)
@@ -59,4 +59,4 @@ xgb.plot.importance <- function(importance_matrix = NULL, numberOfClusters = c(1
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c("Feature", "Gain", "Cluster"))
+globalVariables(c("Feature", "Gain", "Cluster"))

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

@@ -48,7 +48,7 @@
 #' train <- agaricus.train
 #' 
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' 
 #' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
 #' xgb.plot.tree(agaricus.train$data@@Dimnames[[2]], model = bst)
@@ -91,4 +91,4 @@ xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NU
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c("Feature", "yesPath", "ID", "Cover", "Quality", "Split", "Yes", "Yes.Feature", "noPath", "No", "No.Feature", "."))
+globalVariables(c("Feature", "yesPath", "ID", "Cover", "Quality", "Split", "Yes", "Yes.Feature", "noPath", "No", "No.Feature", "."))

R-package/R/xgb.save.R

@@ -11,7 +11,7 @@
 #' train <- agaricus.train
 #' test <- agaricus.test
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
 #' pred <- predict(bst, test$data)

R-package/R/xgb.save.raw.R

@@ -11,7 +11,7 @@
 #' train <- agaricus.train
 #' test <- agaricus.test
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' raw <- xgb.save.raw(bst)
 #' bst <- xgb.load(raw)
 #' pred <- predict(bst, test$data)

R-package/R/xgb.train.R

@@ -108,7 +108,7 @@
 #'   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
 #'   return(list(metric = "error", value = err))
 #' }
-#' bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
+#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
 #' @export
 #' 
 xgb.train <- function(params=list(), data, nrounds, watchlist = list(), 

R-package/R/xgboost.R

@@ -45,7 +45,7 @@
 #' train <- agaricus.train
 #' test <- agaricus.test
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nround = 2,objective = "binary:logistic")
+#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' pred <- predict(bst, test$data)
 #' 
 #' @export