gam.outer                package:mgcv                R Documentation

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), 'optim' or 'nlm'. Exact derivatives of the
     score can be used by fitting with 'gam.fit2' or 'link{gam.fit3}'
     (for exact first and second derivatives). 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 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
          score.

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

     ...: other arguments, typically for passing on to 'gam.fit2'
          (ultimately).

Details:

     Estimation of smoothing parameters by optimizing GCV scores
     obtained at convergence of the P-IRLS iteration was proposed by
     O'Sullivan et al. (1986), and is here termed `outer' iteration.

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'

