> install.packages("partykit")
> library(partykit)
> library(rpart)
> data(Boston,package="MASS")
> bst.index <- sample(nrow(Boston), nrow(Boston) * 0.2)
> bst.test = Boston[bst.index,]
> bst.train = Boston[-bst.index,]
> bst.rf <- randomForest(medv~ ., data = bst.train)
> bst.rf

Call:
 randomForest(formula = medv ~ ., data = bst.train) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 4

          Mean of squared residuals: 8.997005
                    % Var explained: 89.4

> prediction = predict(bst.rf, bst.train)
> plot(prediction,bst.train[,14])
prediction = predict(bst.rf, bst.test)
plot(prediction,bst.test[,14])
