

   FFiitttteedd ggllss OObbjjeecctt

   DDeessccrriippttiioonn::

        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'.

   VVaalluuee::

        The following components must be included in a legiti-
        mate `gls' object.

      apVar: 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'.

       call: a list containing an image of the `gls' call that
             produced the object.

   coefficients: a vector with the estimated linear model coef-
             ficients.

   contrasts: a list with the contrasts used to represent fac-
             tors in the model formula. This information is
             important for making predictions from a new data
             frame in which not all levels of the original fac-
             tors are observed. If no factors are used in the
             model, this component will be an empty list.

       dims: 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.

   estMethod: the estimation method: either `"ML"' for maximum
             likelihood, or `"REML"' for restricted maximum
             likelihood.

     fitted: a vector with the fitted values..

   glsStruct: an object inheriting from class `glsStruct', rep-
             resenting a list of linear model components, such
             as `corStruct' and `varFunc' objects.

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

    numIter: the number of iterations used in the iterative
             algorithm.

   residuals: a vector with the residuals.

      sigma: the estimated residual standard error.

    varBeta: an approximate covariance matrix of the coeffi-
             cients estimates.

   AAuutthhoorr((ss))::

        Jose Pinheiro and Douglas Bates

   SSeeee AAllssoo::

        `gls', `glsStruct'

