mixDenBi               package:bayesm               R Documentation

_C_o_m_p_u_t_e _B_i_v_a_r_i_a_t_e _M_a_r_g_i_n_a_l _D_e_n_s_i_t_y _f_o_r _a _N_o_r_m_a_l _M_i_x_t_u_r_e

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

     'mixDenBi' computes the implied bivariate marginal density from a
     mixture of normals with specified mixture probabilities and
     component parameters.

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

     mixDenBi(i, j, xi, xj, pvec, comps)

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

       i: index of first variable 

       j: index of second variable 

      xi: grid of values of first variable 

      xj: grid of values of second variable 

    pvec: normal mixture probabilities 

   comps: list of lists of components 

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

     length(comps) is the number of mixture components.  comps[[j]] is
     a list of parameters of the jth component. comps[[j]]$mu is mean
     vector; comps[[j]]$rooti is the UL decomp of Sigma^{-1}.

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

     an array (length(xi)=length(xj) x 2) with density value

_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:

     'rnmixGibbs', 'mixDen'

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

     ## Not run: 
     ##
     ##  see examples in rnmixGibbs documentation
     ##
     ## End(Not run)

