

gnlsObject(nlme)                             R Documentation

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

_D_e_s_c_r_i_p_t_i_o_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_a_l_u_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_u_t_h_o_r_(_s_)_:

     Jose Pinheiro and Douglas Bates

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

     `gnls', `gnlsStruct'

