fix iris to Rd files

R-package/man/getinfo.Rd

@@ -21,7 +21,7 @@ Get information of an xgb.DMatrix object
 }
 \examples{
 data(iris)
-iris[,5] <- as.numeric(iris[,5])
+iris[,5] <- as.numeric(iris[,5]=='setosa')
 dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
 labels <- getinfo(dtrain, "label")
 }

R-package/man/predict-xgb.Booster-method.Rd

@@ -18,15 +18,16 @@ value of sum of functions, when outputmargin=TRUE, the prediction is
 untransformed margin value. In logistic regression, outputmargin=T will
 output value before logistic transformation.}
 
-\item{ntreelimit}{limit number of trees used in prediction, this parameter is only valid for gbtree, but not for gblinear.
-set it to be value bigger than 0. It will use all trees by default.}
+\item{ntreelimit}{limit number of trees used in prediction, this parameter is
+only valid for gbtree, but not for gblinear. set it to be value bigger
+than 0. It will use all trees by default.}
 }
 \description{
 Predicted values based on xgboost model object.
 }
 \examples{
 data(iris)
-bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
 pred <- predict(bst, as.matrix(iris[,1:4]))
 }
 

R-package/man/slice.Rd

@@ -23,7 +23,7 @@ orginal xgb.DMatrix object
 }
 \examples{
 data(iris)
-iris[,5] <- as.numeric(iris[,5])
+iris[,5] <- as.numeric(iris[,5]=='setosa')
 dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
 dsub <- slice(dtrain, 1:3)
 }

R-package/man/xgb.DMatrix.Rd

@@ -20,7 +20,7 @@ Contruct xgb.DMatrix object from dense matrix, sparse matrix or local file.
 }
 \examples{
 data(iris)
-iris[,5] <- as.numeric(iris[,5])
+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')

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

@@ -15,7 +15,7 @@ Save xgb.DMatrix object to binary file
 }
 \examples{
 data(iris)
-iris[,5] <- as.numeric(iris[,5])
+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')

R-package/man/xgb.dump.Rd

@@ -21,7 +21,7 @@ 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]), nrounds = 2)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
 xgb.dump(bst, 'iris.xgb.model.dump')
 }
 

R-package/man/xgb.load.Rd

@@ -13,7 +13,7 @@ Load xgboost model from the binary model file
 }
 \examples{
 data(iris)
-bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+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]))

R-package/man/xgb.save.Rd

@@ -15,7 +15,7 @@ Save xgboost model from xgboost or xgb.train
 }
 \examples{
 data(iris)
-bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+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]))

R-package/man/xgb.train.Rd

@@ -56,7 +56,7 @@ therefore it is more flexible than \code{\link{xgboost}}.
 }
 \examples{
 data(iris)
-iris[,5] <- as.numeric(iris[,5])
+iris[,5] <- as.numeric(iris[,5]=='setosa')
 dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
 dtest <- dtrain
 watchlist <- list(eval = dtest, train = dtrain)

R-package/man/xgboost.Rd

@@ -46,7 +46,7 @@ Number of threads can also be manually specified via "nthread" parameter
 }
 \examples{
 data(iris)
-bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
 pred <- predict(bst, as.matrix(iris[,1:4]))
 }