### grow CART tree on kyphosis data
kyph.tree <- tree(Kyphosis~Age+Number+Start,data=kyphosis)

# plot the tree
plot(kyph.tree)
text(kyph.tree)

# make fancy postscript plot
post.tree(kyph.tree)

# tree info
summary(kyph.tree)
print(kyph.tree)


### AT&T data
attach(market.frame)
z.survey <- tree(market.survey,na.action=na.omit)

# two types of tree display
par(mfrow=c(1,2))
plot(z.survey)
plot(z.survey,type="u")

# size versus deviance
plot(prune.tree(z.survey))

# prune tree to 50 leaves
z.survey.sm <- prune.tree(z.survey,best=50)

# cross validation
survey.cv <- cv.tree(z.survey.sm,,prune.tree)
plot(survey.cv)

# best fit: tree of size 3
z.survey.best <- prune.tree(z.survey,best=3)
plot(z.survey.best)
text(z.survey.best)


### car data
attach(car.test.frame)
car <- car.test.frame
car.tree <- tree(Mileage~.,data=car,na.action=na.omit)

# residuals, fitted values,...
par(mfrow=c(1,2))
plot(predict(car.tree),residuals(car.tree))
qqnorm(residuals(car.tree))


### some other car data
z.cu <- tree(Reliability~Price+Country+Mileage+Type,
             data=cu.summary,na.action=na.tree.replace.all)

# price and mileage differences after split 1 
tree.screens()
plot(z.cu)
text(z.cu)
hist.tree(z.cu,Price,Mileage,nodes=1)