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 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;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)

[Package mgcv version 1.3-17 Index]