logLik.gam               package:mgcv               R Documentation

Extract the log likelihood for a fitted GAM

Description:

     Function to extract the log-likelihood for a fitted 'gam' model
     (note that the models are usually fitted by penalized likelihood
     maximization).

Usage:

     ## S3 method for class 'gam':
     logLik(object,...)

Arguments:

  object: fitted model objects of class 'gam' as produced by 'gam()'.

     ...: un-used in this case

Details:

     Modification of 'logLik.glm' which corrects the degrees of freedom
     for use with 'gam' objects. 

     The function is provided so that 'AIC' functions correctly with
     'gam' objects, and uses the appropriate degrees of freedom
     (accounting for penalization). Note,  when using 'AIC' for
     penalized models, that the degrees of freedom are the effective
     degrees of freedom and not the number of parameters, and the model
     maximizes the penalized likelihood, not the actual likelihood!
     This seems to be reasonably well founded for the known scale
     parameter case (see Hastie and Tibshirani, 1990, section 6.8.3 and
     also Wood 2008),  and in fact in this case the default smoothing
     parameter estimation criterion is effectively this modified AIC.

Value:

     Standard 'logLik' object: see 'logLik'.

Author(s):

     Simon N. Wood simon.wood@r-project.org based directly on
     'logLik.glm'

References:

     Hastie and Tibshirani, 1990, Generalized Additive Models.

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

See Also:

     'AIC'

