logLik.gam {mgcv}R Documentation

Log likelihood for a fitted GAM, for AIC

Description

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

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. (See e.g. Hastie and Tibshirani, 1990, section 6.8.3 and also Wood 2008),

By default this routine uses a definition of the effective degrees of freedom that includes smoothing parameter uncertainty, if this is available (i.e. if smoothing parameter selection is by some variety of marginal likelihood).

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


[Package mgcv version 1.8-23 Index]