predict.gls               package:nlme               R Documentation

_P_r_e_d_i_c_t_i_o_n_s _f_r_o_m _a _g_l_s _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     The predictions for the linear model represented by 'object' are
     obtained at the covariate values defined in 'newdata'.

_U_s_a_g_e:

     ## S3 method for class 'gls':
     predict(object, newdata, na.action, ...)

_A_r_g_u_m_e_n_t_s:

  object: an object inheriting from class 'gls', representing a
          generalized least squares fitted linear model.

 newdata: an optional data frame to be used for obtaining the
          predictions. All variables used in the linear model must be
          present in the data frame. If missing, the fitted values are
          returned.

na.action: a function that indicates what should happen when 'newdata'
          contains 'NA's.  The default action ('na.fail') causes the
          function to print an error message and terminate if there are
          any incomplete observations.

     ...: some methods for this generic require additional arguments. 
          None are used in this method.

_V_a_l_u_e:

     a vector with the predicted values.

_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'

_E_x_a_m_p_l_e_s:

     fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
                correlation = corAR1(form = ~ 1 | Mare))
     newOvary <- data.frame(Time = c(-0.75, -0.5, 0, 0.5, 0.75))
     predict(fm1, newOvary)

