

   FFiitttteedd ggnnl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 `gnls' function, inheriting
        from class `gnls' and also from class `gls', and repre-
        senting a generalized nonlinear least squares fitted
        model. Objects of this class have methods for the
        generic functions  `anova', `coef', `fitted', `for-
        mula', `getGroups', `getResponse', `intervals', `log-
        Lik', `plot', `predict', `print', `residuals', `sum-
        mary', and `update'.

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

        The following components must be included in a legiti-
        mate `gnls' 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 `gnls',
             this component is equal to `NULL'.

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

   coefficients: a vector with the estimated nonlinear model
             coefficients.

   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 used in the fit and `p' - the number
             of coefficients in the nonlinear model.

     fitted: a vector with the fitted values.

   modelStruct: an object inheriting from class `gnlsStruct',
             representing a list of model components, such as
             `corStruct' and `varFunc' objects.

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

     logLik: the log-likelihood at convergence.

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

      plist:

       pmap:

   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::

        `gnls', `gnlsStruct'

