gam.fit                 package:mgcv                 R Documentation

Generalized Additive Models fitting using penalized regression splines and 
GCV

Description:

     This is an internal function of package 'mgcv'. It is a
     modification of the function 'glm.fit', designed to be called from
     'gam'. The major modification is that rather than solving a
     weighted least squares problem at each IRLS step,  a weighted,
     penalized least squares problem is solved at each IRLS step with
     smoothing parameters associated with each penalty chosen by GCV or
     UBRE, using routine 'mgcv'. For further information on usage see
     code for 'gam'. Some regularization of the  IRLS weights is also
     permitted as a way of addressing identifiability related problems
     (see  'gam.control'). Negative binomial parameter estimation is
     supported.

Author(s):

     Simon N. Wood simon@stats.gla.ac.uk

References:

     Gu and Wahba (1991) Minimizing GCV/GML scores with multiple
     smoothing parameters via the Newton method. SIAM J. Sci. Statist.
     Comput. 12:383-398

     Wood, S.N. (2000)  Modelling and Smoothing Parameter Estimation
     with Multiple  Quadratic Penalties. J.R.Statist.Soc.B
     62(2):413-428

     Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B
     65(1):95-114

     Wood, S.N. (in press) Stable and efficient multiple smoothing
     parameter estimation for generalized additive models. J. Amer.
     Statist. Ass.

     <URL: http://www.stats.gla.ac.uk/~simon/>

See Also:

     'gam', 'mgcv', 'magic'

