mochoice                package:mcsm                R Documentation

_A_n _M_C_M_C _m_o_d_e_l _c_h_o_i_c_e _i_l_l_u_s_t_r_a_t_i_o_n _f_o_r _t_h_e _l_i_n_e_a_r _m_o_d_e_l

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

     Using a Gibbs sampling strategy of changing one indicator at a
     time, this function explores the space of models and returns the
     most likely models among those visited. The data used in this
     example is swiss, with four explanatory variables.

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

     mochoice(Niter = 10^4)

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

   Niter: Number of MCMC iterations

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

   model: Sequence of model indicators visited by the MCMC algorithm

     top: Five most likely models

_N_o_t_e:

     For more details, see Chapter 3 of *Bayesian Core* (2007,
     Springer-Verlag) by J.-M. Marin and C.P. Robert, since the
     procedure is derived from the developments in this chapter.

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

     Christian P. Robert and George Casella

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

     From Chapter 6 of *EnteR Monte Carlo Statistical Methods*

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

     mochoice(10^3)

