step.gam {mgcv} | R Documentation

## Alternatives to step.gam

### Description

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.

### Author(s)

Simon N. Wood simon.wood@r-project.org

### Examples

    ## 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)

[Package mgcv version 1.4-1.1 Index]