lm.gls                 package:MASS                 R Documentation

_F_i_t _L_i_n_e_a_r _M_o_d_e_l_s _b_y _G_e_n_e_r_a_l_i_z_e_d _L_e_a_s_t _S_q_u_a_r_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fit linear models by Generalized Least Squares

_U_s_a_g_e:

     lm.gls(formula, data, W, subset, na.action, inverse = FALSE,
            method = "qr", model = FALSE, x = FALSE, y = FALSE,
            contrasts = NULL, ...)

_A_r_g_u_m_e_n_t_s:

 formula: a formula expression as for regression models, of the form
          'response ~ predictors'. See the documentation of 'formula'
          for other details. 

    data: an optional data frame in which to interpret the variables
          occurring in 'formula'. 

       W: a weight matrix. 

  subset: expression saying which subset of the rows of the data should
           be used in the fit. All observations are included by
          default. 

na.action: a function to filter missing data. 

 inverse: logical: if true 'W' specifies the inverse of the weight
          matrix: this is appropriate if a variance matrix is used. 

  method: method to be used by 'lm.fit'. 

   model: should the model frame be returned? 

       x: should the design matrix be returned? 

       y: should the response be returned? 

contrasts: a list of contrasts to be used for some or all of 

     ...: additional arguments to 'lm.fit'. 

_D_e_t_a_i_l_s:

     The problem is transformed to uncorrelated form and passed to
     'lm.fit'.

_V_a_l_u_e:

     An object of class '"lm.gls"', which is similar to an '"lm"'
     object.  There is no '"weights"' component, and only a few '"lm"'
     methods will work correctly.  As from version 7.1-22 the residuals
     and fitted values refer to the untransformed problem.

_S_e_e _A_l_s_o:

     'gls', 'lm', 'lm.ridge'

