step.gam {mgcv} | R Documentation |
There is no step.gam
in package mgcv
. The
mgcv
default for model selection is to use either prediction error criteria such as
GCV, GACV, Mallows' Cp/AIC/UBRE or the likelihood based methods of REML or ML. Since the
smoothness estimation part of model
selection is done in this way it is logically most consistent to perform the rest of model
selection in the same way. i.e. to decide which terms to include
or omit by looking at changes in GCV, AIC, REML etc.
To facilitate fully automatic model selection the package implements two smooth modification techniques which can be used to allow smooths to be shrunk to zero as part of smoothness selection.
"cs"
and "ts"
, based on
cubic regression spline and thin plate regression spline smoothers (see s
) select
argument to gam
causes
this latter approach to be used.REML and ML smoothness selection are equivalent under this approach, and simulation evidence suggests that they tend to perform a little better than prediction error criteria, for model selection.
Simon N. Wood simon.wood@r-project.org
## an example of GCV based model selection as ## an alternative to stepwise selection, using ## shrinkage smoothers... library(mgcv) set.seed(0);n <- 400 dat <- gamSim(1,n=n,scale=2) dat$x4 <- runif(n, 0, 1) dat$x5 <- runif(n, 0, 1) attach(dat) ## Note the increased gamma parameter below to favour ## slightly smoother models... b<-gam(y~s(x0,bs="ts")+s(x1,bs="ts")+s(x2,bs="ts")+ s(x3,bs="ts")+s(x4,bs="ts")+s(x5,bs="ts"),gamma=1.4) summary(b) plot(b,pages=1) ## Same again using REML/ML b<-gam(y~s(x0,bs="ts")+s(x1,bs="ts")+s(x2,bs="ts")+ s(x3,bs="ts")+s(x4,bs="ts")+s(x5,bs="ts"),method="REML") summary(b) plot(b,pages=1) ## And once more, but using the null space penalization b<-gam(y~s(x0,bs="cr")+s(x1,bs="cr")+s(x2,bs="cr")+ s(x3,bs="cr")+s(x4,bs="cr")+s(x5,bs="cr"), method="REML",select=TRUE) summary(b) plot(b,pages=1) detach(dat);rm(dat)