pimamh                 package:mcsm                 R Documentation

_L_a_n_g_e_v_i_n _M_C_M_C _a_l_g_o_r_i_t_h_m _f_o_r _t_h_e _p_r_o_b_i_t _p_o_s_t_e_r_i_o_r

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

     This function implements a Langevin version of the
     Metropolis-Hastings algorithm on  the posterior of a probit model,
     applied to the 'Pima.tr' dataset.

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

     pimamh(Niter = 10^4, scale = 0.01)

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

   Niter: Number of MCMC iterations

   scale: Scale of the Gaussian noise in the MCMC proposal

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

     The function produces an 'image' plot of the log-posterior, along
     with the simulated values of the parameters represented as dots.

_W_a_r_n_i_n_g:

     This function is fragile since, as described in the book,  too
     large a value of 'scale' may induce divergent behaviour and
     crashes with error messages


     Error in if (log(runif(1)) > like(prop[1], prop[2]) - likecur -
     sum(dnorm(prop,..)))  :
             missing value where TRUE/FALSE needed


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

     Christian P. Robert and George Casella

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

     Chapter 6 of *EnteR Monte Carlo Statistical Methods*

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

     Pima.tr,pimax

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

     ## Not run: pimamh(10^4,scale=.01)

