R: Log likelihood for a fitted GAM, for AIC

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 1.8-17 Index]