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Akaike Information Criterion (AIC)

Source: R/aic.R
AIC-rateReg-method.Rd

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 class '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 = 2 is 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.

Examples

## See examples given in function rateReg.