An object returned by the `gls`

function, inheriting from class
`"gls"`

and representing a generalized least squares fitted linear
model. Objects of this class have methods for the generic functions
`anova`

, `coef`

, `fitted`

, `formula`

,
`getGroups`

, `getResponse`

, `intervals`

, `logLik`

,
`plot`

, `predict`

, `print`

, `residuals`

,
`summary`

, and `update`

.

The following components must be included in a legitimate `"gls"`

object.

an approximate covariance matrix for the
variance-covariance coefficients. If `apVar = FALSE`

in the list
of control values used in the call to `gls`

, this
component is equal to `NULL`

.

a list containing an image of the `gls`

call that
produced the object.

a vector with the estimated linear model coefficients.

a list of the contrast matrices 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.

a list with basic dimensions used in the model fit,
including the components `N`

- the number of observations in
the data and `p`

- the number of coefficients in the linear
model.

a vector with the fitted values.

an object inheriting from class `glsStruct`

,
representing a list of linear model components, such as
`corStruct`

and `varFunc`

objects.

a vector with the correlation structure grouping factor, if any is present.

the log-likelihood at convergence.

the estimation method: either `"ML"`

for maximum
likelihood, or `"REML"`

for restricted maximum likelihood.

the number of iterations used in the iterative algorithm.

a vector with the residuals.

the estimated residual standard error.

an approximate covariance matrix of the coefficients estimates.