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)

R-package/man/getinfo.Rd

@@ -20,9 +20,9 @@ getinfo(object, ...)
 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/man/predict-xgb.Booster-method.Rd

@@ -26,8 +26,12 @@ than 0. It will use all trees by default.}
 Predicted values based on xgboost model object.
 }
 \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)
 }
 

R-package/man/setinfo.Rd

@@ -22,9 +22,9 @@ setinfo(object, ...)
 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/man/slice.Rd

@@ -22,9 +22,9 @@ Get a new DMatrix containing the specified rows of
 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)
 }
 

R-package/man/xgb.DMatrix.Rd

@@ -19,10 +19,10 @@ indicating the data file.}
 Contruct xgb.DMatrix object from dense matrix, sparse matrix or local 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')
 }
 

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

@@ -14,10 +14,10 @@ xgb.DMatrix.save(DMatrix, fname)
 Save xgb.DMatrix object to 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')
 }
 

R-package/man/xgb.cv.Rd

@@ -63,4 +63,10 @@ Number of threads can also be manually specified via "nthread" parameter.
 
 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")
+}
 

R-package/man/xgb.dump.Rd

@@ -20,8 +20,12 @@ xgb.dump(model, fname, fmap = "")
 Save a xgboost model to text file. Could be parsed later.
 }
 \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')
 }
 

R-package/man/xgb.load.Rd

@@ -12,10 +12,14 @@ xgb.load(modelfile)
 Load xgboost model from the binary model 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)
 }
 

R-package/man/xgb.save.Rd

@@ -14,10 +14,14 @@ xgb.save(model, fname)
 Save xgboost model from xgboost or xgb.train
 }
 \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)
 }
 

R-package/man/xgb.train.Rd

@@ -58,9 +58,8 @@ It supports advanced features such as watchlist, customized objective function,
 therefore it is more flexible than \code{\link{xgboost}}.
 }
 \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)