AIC,rateReg-method is an S4 class method calculating Akaike
information criterion (AIC) for one or several rateReg objects,
according to the formula - 2 * log-likelihood + 2 * nPar, where nPar
represents the number of parameters in the fitted model.
Usage
# S4 method for rateReg
AIC(object, ..., k = 2)Arguments
- object
An object used to dispatch a method.
- ...
Optionally more fitted model objects.
- k
An optional numeric value used as the penalty per parameter. The default
k = 2is the classic AIC.
Value
If just one object is provided, a numeric value representing
calculated AIC. If multiple objects are provided, a data frame with
rows corresponding to the objects and columns df and AIC,
where df means degree of freedom, which is the number of
parameters in the fitted model.
Details
When comparing models fitted by maximum likelihood to the same data, the
smaller the AIC, the better the fit. A friendly warning will be thrown out
if the numbers of observation were different in the model comparison.
help(AIC, stats) for other details.
See also
rateReg for model fitting;
summary,rateReg-method for summary of a fitted model;
BIC,rateReg-method for BIC.