| AIC-methods {aod} | R Documentation |
Extracts the Akaike information criterion (AIC) and the corrected AIC (AICc) from fitted models of formal class “glimML” and possibly computes derived statistics.
## S4 method for signature 'glimML' AIC(object, ..., k = 2)
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. |
, 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).
Extracts the AIC and AICc from models of formal class “glimML”, fitted by functions
betabin and negbin.
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.
Examples in betabin and see AIC in package stats.