Merge branch 'master' of ssh://github.com/tqchen/xgboost

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

@@ -6,7 +6,7 @@ setClass('xgb.DMatrix')
 #' 
 #' @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")
 #' @rdname getinfo

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

@@ -11,11 +11,12 @@ setClass("xgb.Booster")
 #'   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.
-#' @param 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.
+#' @param 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.
 #' @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]))
 #' @export
 #' 

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

@@ -8,7 +8,7 @@ setClass('xgb.DMatrix')
 #' 
 #' @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)
 #' @rdname slice

R-package/R/xgb.DMatrix.R

@@ -12,7 +12,7 @@
 #' 
 #' @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/R/xgb.DMatrix.save.R

@@ -7,7 +7,7 @@
 #' 
 #' @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/R/xgb.dump.R

@@ -13,7 +13,7 @@
 #'
 #' @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')
 #' @export
 #' 

R-package/R/xgb.load.R

@@ -6,7 +6,7 @@
 #' 
 #' @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/R/xgb.save.R

@@ -7,7 +7,7 @@
 #' 
 #' @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/R/xgb.train.R

@@ -44,7 +44,7 @@
 #' 
 #' @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/R/xgboost.R

@@ -34,7 +34,7 @@
 #' 
 #' @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]))
 #' @export
 #'