R packages: tree and rpart
Sample codes:
library("tree")
tree.result <- tree(Y~., data=mydata)
summary(tree.result)
plot(tree.result); text(tree.result)
library("rpart")
fit <- rpart(Y~., data=mydata, control=rpart.control(minsplit=100, method=class, cp=0.001))
printcp(fit) # display the results
plotcp(fit) # visualize cross-validation results
summary(fit) # detailed summary of splits
rsq.rpart(fit) # generate two plots - r square and relative error
summary(predict(fit, mydata, type="class"))
Bagging with Regression Trees (BRT)
R package: ipred
Sample codes:
library(ipred)
bag.result <- bagging(Y~., data=mydata, nbagg=30)
R package: randomForest
library("randomForest")
rf <- randomForest(Y~., data=training.data, ntree=100, mtry=40)
pred <- predict(rf, test.data)
cmatrix <- table(observed=test.data [, "Y"], predicted=pred) # Confusion matrix
# pred <- predict(rf, test.data , type="prob")
