\HeaderA{logLik}{Extract Log-Likelihood}{logLik}
\methaliasA{logLik.lm}{logLik}{logLik.lm}
\aliasA{print.logLik}{logLik}{print.logLik}
\aliasA{str.logLik}{logLik}{str.logLik}
\keyword{models}{logLik}
\begin{Description}\relax
This function is generic; method functions can be written to handle
specific classes of objects.  Classes which already have methods for
this function include: \code{glm}, \code{lm}, \code{nls}, \code{Arima}
and \code{gls}, \code{lme} and others in package \pkg{nlme}.
\end{Description}
\begin{Usage}
\begin{verbatim}
logLik(object, ...)

## S3 method for class 'lm':
logLik(object, REML = FALSE, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] any object from which a log-likelihood value, or a
contribution to a log-likelihood value, can be extracted.
\item[\code{...}] some methods for this generic function require additional
arguments.
\item[\code{REML}] an optional logical value.  If \code{TRUE} the restricted
log-likelihood is returned, else, if \code{FALSE}, the
log-likelihood is returned.  Defaults to \code{FALSE}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
For a \code{"glm"} fit the \code{\LinkA{family}{family}} does not have to specify
how to calculate the log-likelihood, so this is based on the
family's \code{aic()} function to compute the AIC.  For the
\code{\LinkA{gaussian}{gaussian}}, \code{\LinkA{Gamma}{Gamma}} and
\code{\LinkA{inverse.gaussian}{inverse.gaussian}} families it assumed that the dispersion
of the GLM is estimated has been counted as a parameter in the AIC
value, and for all other families it is assumed that the dispersion is
known.

Note that this procedure is not completely accurate for the gamma and
inverse gaussian families, as the estimate of dispersion used is not
the MLE.

For \code{"lm"} fits it is assumed that the scale has been estimated
(by maximum likelihood or REML), and all the constants in the
log-likelihood are included.
\end{Details}
\begin{Value}
Returns an object, say \code{r}, of class \code{logLik} which is a
number with attributes, \code{attr(r, "df")} (\bold{d}egrees of
\bold{f}reedom) giving the number of (estimated) parameters in the model.
There is a simple \code{print} method for \code{logLik} objects.

The details depend on the method function used; see the appropriate
documentation.
\end{Value}
\begin{Author}\relax
Jose Pinheiro and Douglas Bates
\end{Author}
\begin{References}\relax
For \code{logLik.lm}:

Harville, D.A. (1974).
Bayesian inference for variance components using only error contrasts.
\emph{Biometrika}, \bold{61}, 383--385.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{logLik.gls}{logLik.gls}}, \code{\LinkA{logLik.lme}{logLik.lme}}, in
package \pkg{nlme}, etc.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
x <- 1:5
lmx <- lm(x ~ 1)
logLik(lmx) # using print.logLik() method
utils::str(logLik(lmx))

## lm method
(fm1 <- lm(rating ~ ., data = attitude))
logLik(fm1)
logLik(fm1, REML = TRUE)

res <- try(utils::data(Orthodont, package="nlme"))
if(!inherits(res, "try-error")) {
  fm1 <- lm(distance ~ Sex * age, Orthodont)
  print(logLik(fm1))
  print(logLik(fm1, REML = TRUE))
}
\end{ExampleCode}
\end{Examples}

