| 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 |
... |
optional list of fitted models separated by commas. |
k |
numeric scalar, with a default value set to 2, thus providing the regular AIC. |
Details
AIC = -2~\mbox{log-likelihood} + 2*n_{par}, where n_{par}
represents the number of parameters in the fitted model.
AICc = AIC + 2 * n_{par} * (n_{par} + 1) / (n_{obs} - n_{par} + 1),
where n_{obs} 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 n_{obs} / n_{par} < 40 (Hurvich and Tsai, 1995).
Methods
- glimML
Extracts the AIC and AICc from models of formal class “glimML”, fitted by functions
betabinandnegbin.
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.