llmnl                 package:bayesm                 R Documentation

_E_v_a_l_u_a_t_e _L_o_g _L_i_k_e_l_i_h_o_o_d _f_o_r _M_u_l_t_i_n_o_m_i_a_l _L_o_g_i_t _M_o_d_e_l

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

     'llmnl' evaluates log-likelihood for the multinomial logit model.

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

     llmnl(beta,y, X)

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

    beta: k x 1 coefficient vector 

       y: n x 1 vector of obs on y (1,..., p) 

       X: n*p x k Design matrix (use 'createX' to make) 

_D_e_t_a_i_l_s:

     Let mu_i=X_i beta, then Pr(y_i=j) =
     exp(mu_{i,j})/sum_kexp(mu_{i,k}).
      X_i is the submatrix of X corresponding to the ith observation. 
     X has n*p rows.  

     Use 'createX' to create X.

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

     value of log-likelihood (sum of log prob of observed multinomial
     outcomes).

_W_a_r_n_i_n_g:

     This routine is a utility routine that does *not* check the input
     arguments for proper dimensions and type.

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

     Peter Rossi, Graduate School of Business, University of Chicago,
     Peter.Rossi@ChicagoGsb.edu.

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

     For further discussion, see _Bayesian Statistics and Marketing_ by
     Rossi, Allenby and McCulloch. 
      <URL:
     http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html>

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

     'createX', 'rmnlIndepMetrop'

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

     ##
     ## Not run: ll=llmnl(beta,y,X)

