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 Jose.Pinheiro@pharma.novartis.com and Douglas Bates
     bates@stat.wisc.edu

_S_e_e _A_l_s_o:

     'gls', 'glsStruct'

