ordglm                 package:gnlm                 R Documentation

_G_e_n_e_r_a_l_i_z_e_d _L_i_n_e_a_r _O_r_d_i_n_a_l _R_e_g_r_e_s_s_i_o_n _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     'ordglm' fits linear regression functions with logistic or probit
     link to ordinal response data by proportional odds.

_U_s_a_g_e:

     ordglm(formula, data=parent.frame(), link="logit", maxiter=10, weights=1)

_A_r_g_u_m_e_n_t_s:

 formula: A model formula. The response must be integers numbered from
          zero to one less than the number of ordered categories.

    data: An optional data frame containing the variables in the model.

    link: Logit or probit link function.

 maxiter: Maximum number of iterations allowed.

 weights: A vector containing the frequencies for grouped data.

_V_a_l_u_e:

     A list of class ordglm is returned. The printed output includes
     the -log likelihood, the corresponding AIC, the deviance, the
     maximum likelihood estimates, standard errors, and correlations.

_A_u_t_h_o_r(_s):

     J.K. Lindsey, adapted and heavily modified from Matlab code
     (ordinalMLE) by J.H. Albert.

_R_e_f_e_r_e_n_c_e_s:

     Jansen, J. (1991) Fitting regression models to ordinal data.
     Biometrical Journal 33, 807-815.

     Johnson, V.E. and Albert, J.H. (1999) Ordinal Data Modeling.
     Springer-Verlag.

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

     'glm', 'nordr'

_E_x_a_m_p_l_e_s:

     # McCullagh (1980) JRSS B42, 109-142
     # tonsil size: 2x3 contingency table
     y <- c(0:2,0:2)
     carrier <- gl(2,3,6)
     wt <- c(19,29,24,497,560,269)
     ordglm(y~carrier, weights=wt)

