mnlHess                package:bayesm                R Documentation

_C_o_m_p_u_t_e_s -_E_x_p_e_c_t_e_d _H_e_s_s_i_a_n _f_o_r _M_u_l_t_i_n_o_m_i_a_l _L_o_g_i_t

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

     'mnlHess' computes -Expected[Hessian] for Multinomial Logit Model

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

     mnlHess(beta,y, X)

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

    beta: k x 1 vector of coefficients 

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

       X: n*p x k Design matrix 

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

     See 'llmnl' for information on structure of X array.  Use
     'createX' to make X.

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

     k x k matrix

_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, Chapter 3. 
      <URL:
     http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html>

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

     'llmnl', 'createX', 'rmnlIndepMetrop'

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

     ##
     ## Not run: mnlHess(beta,y,X)

