glsObject                package: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 legitimate `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 coefficients.

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.

    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', representing 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 coefficients
          estimates.

_A_u_t_h_o_r(_s):

     Jose Pinheiro and Douglas Bates

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

     `gls', `glsStruct'

