R: Akaike Information Criteria

AIC-methods {aod} | R Documentation

## Akaike Information Criteria

### Description

Extracts the Akaike information criterion (AIC) and the corrected AIC (AICc) from fitted models of formal class “glimML” and possibly computes derived statistics.

### Usage

    ## S4 method for signature 'glimML'
    AIC(object, ..., k = 2)

### Arguments

`object` | fitted model of formal class “glimML” (functions `betabin` or `negbin`).
`...` | optional list of fitted models separated by commas.
`k` | numeric scalar, with a default value set to 2, thus providing the regular AIC.

### Details

-2 * log-likelihood + 2 * npar, where npar represents the number of parameters in the fitted model.
AICc = AIC + 2 * npar * (npar + 1) / (nobs - npar + 1), where nobs is the number of observations used to compute the log-likelihood. It should be used when the number of fitted parameters is large compared to sample size, i.e., when nobs / npar < 40 (Hurvich and Tsai, 1995).

### Methods

glimML

  Extracts the AIC and AICc from models of formal class “glimML”, fitted by functions `betabin` and `negbin`.

### References

Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical information-theoretic approach. New-York, Springer-Verlag, 496 p.
Hurvich, C.M., Tsai, C.-L., 1995. Model selection for extended quasi-likelihood models in small samples. Biometrics, 51 (3): 1077-1084.

### See Also

Examples in `betabin` and see `AIC` in package stats.

[Package aod version 1.3 Index]