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 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;sig<-2 x0 <- runif(n, 0, 1);x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1);x3 <- runif(n, 0, 1) x4 <- runif(n, 0, 1);x5 <- runif(n, 0, 1) f <- 2 * sin(pi * x0) f <- f + exp(2 * x1) - 3.75887 f <- f+0.2*x2^11*(10*(1-x2))^6+10*(10*x2)^3*(1-x2)^10-1.396 e <- rnorm(n, 0, sig) y <- f + e ## 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)