\HeaderA{AIC}{Akaike's An Information Criterion}{AIC}
\keyword{models}{AIC}
\begin{Description}\relax
Generic function calculating the Akaike information criterion for
one or several fitted model objects for which a log-likelihood value
can be obtained, according to the formula
\eqn{-2 \mbox{log-likelihood} + k n_{par}}{-2*log-likelihood + k*npar},
where \eqn{n_{par}}{npar} represents the number of parameters in the
fitted model, and \eqn{k = 2}{} for the usual AIC, or \eqn{k = \log(n)}{}
(\eqn{n}{} the number of observations) for the so-called BIC or SBC
(Schwarz's Bayesian criterion).
\end{Description}
\begin{Usage}
\begin{verbatim}
AIC(object, ..., k = 2)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] a fitted model object, for which there exists a
\code{logLik} method to extract the corresponding log-likelihood, or
an object inheriting from class \code{logLik}.
\item[\code{...}] optionally more fitted model objects.
\item[\code{k}] numeric, the \emph{penalty} per parameter to be used; the
default \code{k = 2} is the classical AIC.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The default method for \code{AIC}, \code{AIC.default()} entirely
relies on the existence of a \code{\LinkA{logLik}{logLik}} method
computing the log-likelihood for the given class.

When comparing fitted objects, the smaller the AIC, the better the fit.

The log-likelihood and hence the AIC is only defined up to an additive
constant.  Different constants have conventionally be used for
different purposes and so \code{\LinkA{extractAIC}{extractAIC}} and \code{AIC} may
give different values (and do for models of class \code{"lm"}: see the
help for \code{\LinkA{extractAIC}{extractAIC}}).
\end{Details}
\begin{Value}
If just one object is provided, returns a numeric value
with the corresponding AIC (or BIC, or \dots, depending on \code{k});
if multiple objects are provided, returns a \code{data.frame} with
rows corresponding to the objects and columns representing the number
of parameters in the model (\code{df}) and the AIC.
\end{Value}
\begin{Author}\relax
Jose Pinheiro and Douglas Bates
\end{Author}
\begin{References}\relax
Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986).
\emph{Akaike Information Criterion Statistics}.
D. Reidel Publishing Company.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{extractAIC}{extractAIC}}, \code{\LinkA{logLik}{logLik}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
lm1 <- lm(Fertility ~ . , data = swiss)
AIC(lm1)
stopifnot(all.equal(AIC(lm1),
                    AIC(logLik(lm1))))
## a version of BIC or Schwarz' BC :
AIC(lm1, k = log(nrow(swiss)))
\end{ExampleCode}
\end{Examples}

