gam2objective              package:mgcv              R Documentation

Objective functions for GAM smoothing parameter estimation

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. These functions evaluate the
     GCV/UBRE score of a GAM model, given smoothing parameters, in a
     manner sutable for use by 'optim' or 'nlm'. Not normally called
     directly, but rather service routines for 'gam.outer'.

Usage:

     gam2objective(lsp,args,printWarn=FALSE,...)
     gam2derivative(lsp,args,...)
     gam3objective(lsp,args,...)

Arguments:

     lsp: The log smoothing parameters.

    args: List of arguments required to call 'gam.fit2'.

printWarn: Should 'gam.fit2' print some warnings? Used to suppress
          warnings that are only of interest for the final fitted
          model, until that model is reached.

     ...: Other arguments for passing to 'gam.fit2'.

Details:

     'gam2objective' and 'gam2derivative' are functions suitable for
     calling by 'optim', to evaluate the GCV/UBRE score and it's
     derivatives w.r.t. log smoothing parameters.

     'gam3objective' is an equaivalent to 'gam2objective', suitable for
     optimization by 'nlm' - derivatives of the GCV/UBRE function are
     calculated and returned as attributes.

     The basic idea of optimizing smoothing parameters `outer' to the
     P-IRLS loop was first proposed in O'Sullivan et al. (1986).

Author(s):

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

References:

     O 'Sullivan, Yandall & Raynor (1986) Automatic smoothing of
     regression functions in generalized linear models. J. Amer.
     Statist. Assoc. 81:96-103.

     <URL: http://www.maths.bath.ac.uk/~sw283/>

See Also:

     'gam.fit2',  'gam', 'mgcv', 'magic'

