

glsObject(nlme)                              R Documentation

_F_i_t_t_e_d _g_l_s _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_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_a_l_u_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.

  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.

  logLik: the log-likelihood at convergence.

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

 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_u_t_h_o_r_(_s_)_:

     Jose Pinheiro and Douglas Bates

_S_e_e _A_l_s_o_:

     `gls', `glsStruct'

