mgcv (version 1.4-2)

gam.outer: Minimize GCV or UBRE score of a GAM using `outer' iteration

Description

Estimation of GAM smoothing parameters is most stable if optimization of the UBRE or GCV score is outer to the penalized iteratively re-weighted least squares scheme used to estimate the model given smoothing parameters.

This routine optimizes a GCV or UBRE score in this way. Basically the GCV or UBRE score is evaluated for each trial set of smoothing parameters by estimating the GAM for those smoothing parameters. The score is minimized w.r.t. the parameters numerically, using newton (default), bfgs, optim or nlm. Exact (first and second) derivatives of the score can be used by fitting with gam.fit3. This improves efficiency and reliability relative to relying on finite difference derivatives.

Not normally called directly, but rather a service routine for gam.

Usage

gam.outer(lsp,fscale,family,control,method,gamma,G,...)

Arguments

lsp

The log smoothing parameters.

fscale

Typical scale of the GCV or UBRE/AIC score.

family

the model family.

control

control argument to pass to gam.fit if pure finite differencing is being used.

method

method list returned from gam.method. This defines the optimization method to use.

gamma

The degree of freedom inflation factor for the GCV/UBRE/AIC score.

G

List produced by gam.setup, containing most of what's needed to actually fit a GAM.

...

other arguments, typically for passing on to gam.fit3 (ultimately).

Details

See Wood (2008) for full details on `outer iteration'.

References

Wood, S.N. (2008) Fast stable direct fitting and smoothness selection for generalized additive models. J.R.Statist.Soc.B 70(3):495-518

http://www.maths.bath.ac.uk/~sw283/

See Also

gam.fit3, gam, mgcv, magic