Page 360 - Applied Statistics with R

360 CHAPTER 14. TRANSFORMATIONS

mpg_hp_log = lm(log(mpg) ~ hp + I(hp ^ 2), data = autompg)

par(mfrow = c(1, 2))

plot(log(mpg) ~ hp, data = autompg, col = "dodgerblue", pch = 20, cex = 1.5)
xplot = seq(min(autompg$hp), max(autompg$hp), by = 0.1)
lines(xplot, predict(mpg_hp_log, newdata = data.frame(hp = xplot)),
col = "darkorange", lwd = 2, lty = 1)

plot(fitted(mpg_hp_log), resid(mpg_hp_log), col = "dodgerblue",
pch = 20, cex = 1.5, xlab = "Fitted", ylab = "Residuals")
abline(h = 0, lty = 2, col = "darkorange", lwd = 2)

0.6
3.5 0.4
0.2
log(mpg) 3.0 Residuals 0.0
-0.2

2.5 -0.4
-0.6

50 100 150 200 2.6 2.8 3.0 3.2 3.4 3.6
hp Fitted

mpg_hp_loglog = lm(log(mpg) ~ log(hp), data = autompg)

par(mfrow = c(1, 2))
plot(log(mpg) ~ log(hp), data = autompg, col = "dodgerblue", pch = 20, cex = 1.5)
abline(mpg_hp_loglog, col = "darkorange", lwd = 2)

plot(fitted(mpg_hp_loglog), resid(mpg_hp_loglog), col = "dodgerblue",
pch = 20, cex = 1.5, xlab = "Fitted", ylab = "Residuals")
abline(h = 0, lty = 2, col = "darkorange", lwd = 2)