\HeaderA{glsObject}{Fitted gls Object}{glsObject}
\keyword{models}{glsObject}
\begin{Description}\relax
An object returned by the \code{gls} function, inheriting from class
\code{gls} and representing a generalized least squares fitted linear 
model. Objects of this class have methods for the generic functions 
\code{anova}, \code{coef}, \code{fitted}, \code{formula},
\code{getGroups}, \code{getResponse}, \code{intervals}, \code{logLik},
\code{plot}, \code{predict}, \code{print}, \code{residuals},
\code{summary}, and \code{update}.
\end{Description}
\begin{Value}
The following components must be included in a legitimate \code{gls}
object. 
\begin{ldescription}
\item[\code{apVar}] an approximate covariance matrix for the
variance-covariance coefficients. If \code{apVar = FALSE} in the list
of control values used in the call to \code{gls}, this
component is equal to \code{NULL}.
\item[\code{call}] a list containing an image of the \code{gls} call that
produced the object.
\item[\code{coefficients}] a vector with the estimated linear model
coefficients.
\item[\code{contrasts}] a list with the contrasts used to represent factors
in the model formula. This information is important for making
predictions from a new data frame in which not all levels of the
original factors are observed. If no factors are used in the model,
this component will be an empty list.
\item[\code{dims}] a list with basic dimensions used in the model fit,
including the components \code{N} - the number of observations in
the data and \code{p} - the number of coefficients in the linear
model.
\item[\code{fitted}] a vector with the fitted values..
\item[\code{glsStruct}] an object inheriting from class \code{glsStruct},
representing a list of linear model components, such as
\code{corStruct} and \code{varFunc} objects.
\item[\code{groups}] a vector with the correlation structure grouping factor,
if any is present.
\item[\code{logLik}] the log-likelihood at convergence.
\item[\code{method}] the estimation method: either \code{"ML"} for maximum
likelihood, or \code{"REML"} for restricted maximum likelihood.
\item[\code{numIter}] the number of iterations used in the iterative
algorithm.
\item[\code{residuals}] a vector with the residuals.
\item[\code{sigma}] the estimated residual standard error.
\item[\code{varBeta}] an approximate covariance matrix of the
coefficients estimates.
\end{ldescription}
\end{Value}
\begin{Author}\relax
Jose Pinheiro \email{Jose.Pinheiro@pharma.novartis.com} and Douglas Bates \email{bates@stat.wisc.edu}
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{gls}{gls}}, \code{glsStruct}
\end{SeeAlso}

