# Computer code for example 3.20
# HINT:
# The program assumes that all files are stored in
# C:\monographregression\computercode
# and corresponding subfolders.
# Change directory if code is located elsewhere.
# change working directory
setwd("c:/monographregression/computercode")
library(foreign)
# read data
golf <- read.dta(file="data/stata/golffull.dta")
attach(golf)
# Estimate AIC best model of example 3.19
mod9 <- lm(price~kilometerop1+kilometerop2+ageop1+ageop2, data=golf)
# Compute studentized residuals
rs<-rstudent(mod9)
# Compute predicted values
pred <- predict(mod9)
# quantiles of the t-distribution
t <- qt(0.01/344,df=172-6)
# Plot studentized residuals
# Note that the original plots in the book have been generated with STATA. Therefore the plots produced with R are slightly different.
plot(pred,rs,ylab="studentized residuals",xlab="estimated sales price",main="studentized residuals versus estimated sales price",ylim=c(-5,5))
abline(-t,0)
abline(t,0)
abline(0,0)
plot(kilometer,rs,ylab="studentized residuals",xlab="kilometer reading in 1000 km",main="studentized residuals versus kilometer reading",ylim=c(-5,5))
abline(-t,0)
abline(t,0)
abline(0,0)
plot(age,rs, ylab="studentized residuals",xlab="age in months",main="studentized residuals versus age in months",ylim=c(-5,5))
abline(-t,0)
abline(t,0)
abline(0,0)
plot(TIA,rs, ylab="studentized residuals",xlab="months until next TIA appointment",main="studentized residuals versus months until TIA",ylim=c(-5,5))
abline(-t,0)
abline(t,0)
abline(0,0)