Page 397 - Applied Statistics with R

16.2. SELECTION PROCEDURES 397

summary(hipcenter_mod_back_bic)$adj.r.squared

## [1] 0.6244149

We can also calculate the LOOCV RMSE for both selected models, as well as
the full model.

calc_loocv_rmse(hipcenter_mod)

## [1] 44.44564

calc_loocv_rmse(hipcenter_mod_back_aic)

## [1] 37.58473

calc_loocv_rmse(hipcenter_mod_back_bic)

## [1] 37.40564

We see that we would prefer the model chosen via BIC if using LOOCV RMSE
as our metric.

16.2.2 Forward Search

Forward selection is the exact opposite of backwards selection. Here we tell
R to start with a model using no predictors, that is hipcenter ~ 1, then
at each step R will attempt to add a predictor until it finds a good model
or reaches hipcenter ~ Age + Weight + HtShoes + Ht + Seated + Arm +
Thigh + Leg.

hipcenter_mod_start = lm(hipcenter ~ 1, data = seatpos)
hipcenter_mod_forw_aic = step(
hipcenter_mod_start,
scope = hipcenter ~ Age + Weight + HtShoes + Ht + Seated + Arm + Thigh + Leg,
direction = "forward")

## Start: AIC=311.71
## hipcenter ~ 1
##
## Df Sum of Sq RSS AIC