step.gam {mgcv}R Documentation

Alternatives to step.gam


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


## an example of GCV based model selection as
## an alternative to stepwise selection
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...

[Package mgcv version 1.2-3 Index]