replace iris in docs

R-package/R/getinfo.xgb.DMatrix.R

@@ -5,9 +5,9 @@ setClass('xgb.DMatrix')
 #' Get information of an xgb.DMatrix object
 #' 
 #' @examples
-#' data(iris)
-#' iris[,5] <- as.numeric(iris[,5]=='setosa')
-#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+#' data(agaricus.train, package='xgboost')
+#' train <- agaricus.train
+#' dtrain <- xgb.DMatrix(train$data, label=train$label)
 #' labels <- getinfo(dtrain, 'label')
 #' setinfo(dtrain, 'label', 1-labels)
 #' labels2 <- getinfo(dtrain, 'label')

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

@@ -15,9 +15,13 @@ setClass("xgb.Booster")
 #'  only valid for gbtree, but not for gblinear. set it to be value bigger 
 #'  than 0. It will use all trees by default.
 #' @examples
-#' data(iris)
-#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
-#' pred <- predict(bst, as.matrix(iris[,1:4]))
+#' data(agaricus.train, package='xgboost')
+#' data(agaricus.test, package='xgboost')
+#' train <- agaricus.train
+#' test <- agaricus.test
+#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#'                eta = 1, nround = 2,objective = "binary:logistic")
+#' pred <- predict(bst, test$data)
 #' @export
 #' 
 setMethod("predict", signature = "xgb.Booster", 

R-package/R/setinfo.xgb.DMatrix.R

@@ -3,9 +3,9 @@
 #' Set information of an xgb.DMatrix object
 #' 
 #' @examples
-#' data(iris)
-#' iris[,5] <- as.numeric(iris[,5]=='setosa')
-#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+#' data(agaricus.train, package='xgboost')
+#' train <- agaricus.train
+#' dtrain <- xgb.DMatrix(train$data, label=train$label)
 #' labels <- getinfo(dtrain, 'label')
 #' setinfo(dtrain, 'label', 1-labels)
 #' labels2 <- getinfo(dtrain, 'label')

R-package/R/slice.xgb.DMatrix.R

@@ -7,9 +7,9 @@ setClass('xgb.DMatrix')
 #' orginal xgb.DMatrix object
 #' 
 #' @examples
-#' data(iris)
-#' iris[,5] <- as.numeric(iris[,5]=='setosa')
-#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+#' data(agaricus.train, package='xgboost')
+#' train <- agaricus.train
+#' dtrain <- xgb.DMatrix(train$data, label=train$label)
 #' dsub <- slice(dtrain, 1:3)
 #' @rdname slice
 #' @export

R-package/R/xgb.DMatrix.R

@@ -11,11 +11,11 @@
 #' @param ... other information to pass to \code{info}.
 #' 
 #' @examples
-#' data(iris)
-#' iris[,5] <- as.numeric(iris[,5]=='setosa')
-#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
-#' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix')
-#' dtrain <- xgb.DMatrix('iris.xgb.DMatrix')
+#' data(agaricus.train, package='xgboost')
+#' train <- agaricus.train
+#' dtrain <- xgb.DMatrix(train$data, label=train$label)
+#' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
+#' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
 #' @export
 #' 
 xgb.DMatrix <- function(data, info = list(), missing = 0, ...) {

R-package/R/xgb.DMatrix.save.R

@@ -6,11 +6,11 @@
 #' @param fname the name of the binary file.
 #' 
 #' @examples
-#' data(iris)
-#' iris[,5] <- as.numeric(iris[,5]=='setosa')
-#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
-#' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix')
-#' dtrain <- xgb.DMatrix('iris.xgb.DMatrix')
+#' data(agaricus.train, package='xgboost')
+#' train <- agaricus.train
+#' dtrain <- xgb.DMatrix(train$data, label=train$label)
+#' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
+#' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
 #' @export
 #' 
 xgb.DMatrix.save <- function(DMatrix, fname) {

R-package/R/xgb.cv.R

@@ -46,6 +46,11 @@
 #' 
 #' This function only accepts an \code{xgb.DMatrix} object as the input.
 #'
+#' @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"),
+#'                   "max_depth"=3, "eta"=1, "objective"="binary:logistic")
 #' @export
 #'
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,

R-package/R/xgb.dump.R

@@ -12,9 +12,13 @@
 #'        
 #'
 #' @examples
-#' data(iris)
-#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
-#' xgb.dump(bst, 'iris.xgb.model.dump')
+#' data(agaricus.train, package='xgboost')
+#' data(agaricus.test, package='xgboost')
+#' train <- agaricus.train
+#' test <- agaricus.test
+#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#'                eta = 1, nround = 2,objective = "binary:logistic")
+#' xgb.dump(bst, 'xgb.model.dump')
 #' @export
 #' 
 xgb.dump <- function(model, fname, fmap = "") {

R-package/R/xgb.load.R

@@ -5,11 +5,15 @@
 #' @param modelfile the name of the binary file.
 #' 
 #' @examples
-#' data(iris)
-#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
-#' xgb.save(bst, 'iris.xgb.model')
-#' bst <- xgb.load('iris.xgb.model')
-#' pred <- predict(bst, as.matrix(iris[,1:4]))
+#' data(agaricus.train, package='xgboost')
+#' data(agaricus.test, package='xgboost')
+#' train <- agaricus.train
+#' test <- agaricus.test
+#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#'                eta = 1, nround = 2,objective = "binary:logistic")
+#' xgb.save(bst, 'xgb.model')
+#' bst <- xgb.load('xgb.model')
+#' pred <- predict(bst, test$data)
 #' @export
 #' 
 xgb.load <- function(modelfile) {

R-package/R/xgb.save.R

@@ -6,11 +6,15 @@
 #' @param fname the name of the binary file.
 #' 
 #' @examples
-#' data(iris)
-#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
-#' xgb.save(bst, 'iris.xgb.model')
-#' bst <- xgb.load('iris.xgb.model')
-#' pred <- predict(bst, as.matrix(iris[,1:4]))
+#' data(agaricus.train, package='xgboost')
+#' data(agaricus.test, package='xgboost')
+#' train <- agaricus.train
+#' test <- agaricus.test
+#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#'                eta = 1, nround = 2,objective = "binary:logistic")
+#' xgb.save(bst, 'xgb.model')
+#' bst <- xgb.load('xgb.model')
+#' pred <- predict(bst, test$data)
 #' @export
 #' 
 xgb.save <- function(model, fname) {

R-package/R/xgb.train.R

@@ -46,9 +46,8 @@
 #' 
 #' 
 #' @examples
-#' data(iris)
-#' iris[,5] <- as.numeric(iris[,5]=='setosa')
-#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+#' 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)