R: Extract the log likelihood for a fitted GAM

logLik.gam {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`

[Package mgcv version 1.7-18 Index]