step.gam {mgcv} | R Documentation |
There is no step.gam
in package mgcv
. The
mgcv
default for model selection is to use MSE/KL-distance criteria
such as GCV or UBRE/AIC. Since the smoothness estimation part of model
selection is done in this way it is logically most consistent to perform model
selection on the basis of such criteria: i.e. to decide which terms to include
or omit by looking at changes in GCV/UBRE/AIC score.
To facilitate fully automatic model selection the package includes 2 classes
of smoothers ("cs"
and "ts"
: see s
) which can be
penalized to zero for sufficiently high smoothing parameter estimates: use of
such smooths provides an effective alternative to step-wise model
selection. The example below shows an example of the application of this
approach, where selection is a fully integrated part of model estimation.
Simon N. Wood simon.wood@r-project.org
## an example of GCV based model selection as ## an alternative to stepwise selection 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) detach(dat);rm(dat)