rmixGibbs               package:bayesm               R Documentation

_G_i_b_b_s _S_a_m_p_l_e_r _f_o_r _N_o_r_m_a_l _M_i_x_t_u_r_e_s _w/_o _E_r_r_o_r _C_h_e_c_k_i_n_g

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

     'rmixGibbs' makes one draw using the Gibbs Sampler for a mixture
     of multivariate normals.

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

     rmixGibbs(y, Bbar, A, nu, V, a, p, z, comps)

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

       y: data array - rows are obs 

    Bbar: prior mean for mean vector of each norm comp 

       A: prior precision parameter

      nu: prior d.f. parm 

       V: prior location matrix for covariance priro 

       a: Dirichlet prior parms 

       p: prior prob of each mixture component 

       z: component identities for each observation - "indicators"

   comps: list of components for the normal mixture   

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

     'rmixGibbs' is not designed to be called directly. Instead, use
     'rnmixGibbs' wrapper function.

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

     a list containing: 

       p: draw mixture probabilities 

       z: draw of indicators of each component

   comps: new draw of normal component parameters 

_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):

     Rob McCulloch and 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
     Allenby, McCulloch, and Rossi, Chapter 5. 
      <URL:
     http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html>

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

     'rnmixGibbs'

