llnhlogit               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 _n_o_n-_h_o_m_o_t_h_e_t_i_c _L_o_g_i_t _M_o_d_e_l

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

     'llmnp' evaluates log-likelihood for the Non-homothetic Logit
     model.

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

     llnhlogit(theta, choice, lnprices, Xexpend)

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

   theta: parameter vector (see details section) 

  choice: n x 1 vector of choice (1, ..., p) 

lnprices: n x p  array of log-prices

 Xexpend: n x d array of vars predicting expenditure 

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

     Non-homothetic logit model with: ln(psi_i(U)) = alpha_i - e^{k_i}U 

     Structure of theta vector 
      alpha: (p x 1) vector of utility intercepts.
      k: (p x 1) vector of utility rotation parms. 
      gamma: (k x 1) - expenditure variable coefs.
      tau: (1 x 1) - logit scale parameter.

_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,Chapter 4. 
      <URL:
     http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html>

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

     'simnhlogit'

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

     ##
     ## Not run: ll=llnhlogit(theta,choice,lnprices,Xexpend)

