glm.control               package:base               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-04, maxit=10, 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.

_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

