AIC-methods {aod} | R Documentation |
Extracts the Akaike information criterion (AIC) from fitted models of formal class “glimML”.
## S4 method for signature 'glimML': AIC(object, ..., k = 2)
object |
A fitted model of formal class “glimML” (functions betabin or negbin ). |
... |
An optional list of fitted models separated by commas. |
k |
A numeric scalar, with a default value set to 2, thus providing the regular AIC. |
AIC
.betabin
and negbin
.
The AIC is defined as -2*log-likelihood + 2*npar, were npar
represents the number of parameters in the fitted model.Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical information-theoretic approach. New-York, Springer-Verlag, 496 p.
Check the examples in betabin
and see AIC
in package stats.