houseprices {DAAG} | R Documentation |
The houseprices
data frame consists of the floor
area, price, and the number
of bedrooms for a sample of houses sold in Aranda in 1999.
Aranda is a suburb of Canberra, Australia.
houseprices
This data frame contains the following columns:
J.H. Maindonald
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)$sigma^2 pause() cv.lm() pause() print("Bootstrapping - Example 5.5.3") houseprices.fn <- function (houseprices, index){ house.resample <- houseprices[index,] house.lm <- lm(sale.price ~ area, data=house.resample) coef(house.lm)[2] # slope estimate for resampled data } require(boot) # ensure that the boot package is loaded houseprices.boot <- boot(houseprices, R=999, statistic=houseprices.fn) houseprices1.fn <- function (houseprices, index){ house.resample <- houseprices[index,] house.lm <- lm(sale.price ~ area, data=house.resample) predict(house.lm, newdata=data.frame(area=1200)) } houseprices1.boot <- boot(houseprices, R=999, statistic=houseprices1.fn) boot.ci(houseprices1.boot, type="perc") # "basic" is an alternative to "perc" houseprices2.fn <- function (houseprices, index){ house.resample <- houseprices[index,] house.lm <- lm(sale.price ~ area, data=house.resample) houseprices$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)