`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 = 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.