logima                 package:mcsm                 R Documentation

_L_o_g_i_s_t_i_c _a_n_a_l_y_s_i_s _o_f _t_h_e _P_i_m_a._t_r _d_a_t_a_s_e_t _w_i_t_h _c_o_n_t_r_o_l _v_a_r_i_a_t_e_s

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

     Provides a logistic analysis of the 'Pima.tr' by comparing the
     standard Bayesian estimate with another one based on log-cumulant
     control variates. The effect is visible if not huge.

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

     logima(N = 10^3)

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

       N: Number of Monte Carlo iterations

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

     Returns a graph for both the estimate of the intercept and the
     estimate of the coefficient of the body mass index.

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

     Christian P. Robert and George Casella

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

     Chapter 4 of *EnteR Monte Carlo Statistical Methods*

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

     Pima

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

     logima()

