randogit                package:mcsm                R Documentation

_M_C_E_M _r_e_s_o_l_u_t_i_o_n _f_o_r _a _p_r_o_b_i_t _m_a_x_i_m_u_m _l_i_k_e_l_i_h_o_o_d

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

     Based on Booth and Hobert (_JRSS B_, 1999), this function
     evaluates the maximum likelihood estimate of a simulated probit
     model with random effects. The random effects are simulated by a
     MCMC algorithm.

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

     randogit(Tem = 10^3, Tmc = 10^2)

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

     Tem: starting number of MCEM iterations

     Tmc: number of Monte Carlo points in the likelihood approximations

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

     The function returns two plots, one of (beta,sigma) and one of the
     true likelihood L(beta,sigma,u0), where u0 is the true vector of
     random effects.

_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 2 of *EnteR Monte Carlo Statistical Methods*

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

     ## Not run: randogit(20,10)
     #very small values to let the example run

