houseprices

plot(sale.price~area, data=houseprices)
pause()

coplot(sale.price~area|bedrooms, data=houseprices)
pause()

print("Cross-Validation - Example 5.5.2")

houseprices.lm <- lm(sale.price ~ area, data=houseprices)
summary(houseprices.lm)$sigma^2
pause()

CVlm()
pause()

print("Bootstrapping - Example 5.5.3")
houseprices.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
coef(house.lm)[2]    # slope estimate for resampled data
}
require(boot)       # ensure that the boot package is loaded
houseprices.boot <- boot(houseprices, R=999, statistic=houseprices.fn)

houseprices1.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
predict(house.lm, newdata=data.frame(area=1200))
}

houseprices1.boot <- boot(houseprices, R=999, statistic=houseprices1.fn)
boot.ci(houseprices1.boot, type="perc") # "basic" is an alternative to "perc"
houseprices2.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
houseprices$sale.price-predict(house.lm, houseprices)  # resampled prediction errors
}

n <- length(houseprices$area)
R <- 200   
houseprices2.boot <- boot(houseprices, R=R, statistic=houseprices2.fn)
house.fac <- factor(rep(1:n, rep(R, n)))
plot(house.fac, as.vector(houseprices2.boot$t), ylab="Prediction Errors", 
xlab="House")
pause()

plot(apply(houseprices2.boot$t,2, sd)/predict.lm(houseprices.lm, se.fit=TRUE)$se.fit,
     ylab="Ratio of Bootstrap SE's to Model-Based SE's", xlab="House", pch=16)
abline(1,0)