glm.control              package:stats              R Documentation

_A_u_x_i_l_i_a_r_y _f_o_r _C_o_n_t_r_o_l_l_i_n_g _G_L_M _F_i_t_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Auxiliary function as user interface for 'glm' fitting. Typically
     only used when calling 'glm' or 'glm.fit'.

_U_s_a_g_e:

     glm.control(epsilon = 1e-8, maxit = 25, trace = FALSE)

_A_r_g_u_m_e_n_t_s:

 epsilon: positive convergence tolerance epsilon; the iterations
          converge when |dev - devold|/(|dev| + 0.1) < epsilon.

   maxit: integer giving the maximal number of IWLS iterations.

   trace: logical indicating if output should be produced for each
          iteration.

_D_e_t_a_i_l_s:

     If 'epsilon' is small, it is also used as the tolerance for the
     least squares solution.

     When 'trace' is true, calls to 'cat' produce the output for each
     IWLS iteration. Hence, 'options(digits = *)' can be used to
     increase the precision, see the example.

_V_a_l_u_e:

     A list with the arguments as components.

_R_e_f_e_r_e_n_c_e_s:

     Hastie, T. J. and Pregibon, D. (1992) _Generalized linear models._
     Chapter 6 of _Statistical Models in S_ eds J. M. Chambers and T.
     J. Hastie, Wadsworth & Brooks/Cole.

_S_e_e _A_l_s_o:

     'glm.fit', the fitting procedure used by 'glm'.

_E_x_a_m_p_l_e_s:

     ### A variation on  example(glm) :

     ## Annette Dobson's example ...
     counts <- c(18,17,15,20,10,20,25,13,12)
     outcome <- gl(3,1,9)
     treatment <- gl(3,3)
     oo <- options(digits = 12) # to see more when tracing :
     glm.D93X <- glm(counts ~ outcome + treatment, family=poisson(),
                     trace = TRUE, epsilon = 1e-14)
     options(oo)
     coef(glm.D93X) # the last two are closer to 0 than in ?glm's  glm.D93