Page 408 - Applied Statistics with R

408 CHAPTER 16. VARIABLE SELECTION AND MODEL BUILDING

extractAIC(hipcenter_mod_best_bic, k = log(n))

## [1] 2.0000 278.3418

extractAIC(hipcenter_mod_back_bic, k = log(n))

## [1] 2.0000 278.7306

extractAIC(hipcenter_mod_forw_bic, k = log(n))

## [1] 2.0000 278.3418

extractAIC(hipcenter_mod_both_bic, k = log(n))

## [1] 2.0000 278.3418

16.3 Higher Order Terms

So far we have only allowed first-order terms in our models. Let’s return to the
autompg dataset to explore higher-order terms.

autompg = read.table(
"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data",
quote = "\"",
comment.char = "",
stringsAsFactors = FALSE)
colnames(autompg) = c("mpg", "cyl", "disp", "hp", "wt", "acc",
"year", "origin", "name")
autompg = subset(autompg, autompg$hp != "?")
autompg = subset(autompg, autompg$name != "plymouth reliant")
rownames(autompg) = paste(autompg$cyl, "cylinder", autompg$year, autompg$name)
autompg$hp = as.numeric(autompg$hp)
autompg$domestic = as.numeric(autompg$origin == 1)
autompg = autompg[autompg$cyl != 5,]
autompg = autompg[autompg$cyl != 3,]
autompg$cyl = as.factor(autompg$cyl)
autompg$domestic = as.factor(autompg$domestic)
autompg = subset(autompg, select = c("mpg", "cyl", "disp", "hp",
"wt", "acc", "year", "domestic"))