sqaradap                package:mcsm                R Documentation

_I_l_l_u_s_t_r_a_t_i_o_n _o_f _t_h_e _d_a_n_g_e_r_s _o_f _d_o_i_n_g _a_d_a_p_t_i_v_e _M_C_M_C _o_n _a _n_o_i_s_y _s_q_u_a_r_e_d _A_R _m_o_d_e_l

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

     This function constructs a non-parametric proposal after each
     iteration of the MCMC algorithm, based on the earlier simulations.
     It shows how poorly this "natural" solution fares.

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

     sqaradap(T = 10^4, TT = 10^4, scale = 0.5, factor = 1)

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

       T: Number of primary MCMC iterations

      TT: Number of further adaptive MCMC iterations

   scale: Scale of the normal random walk during the first T iterations

  factor: Factor of the 'bw.nrd0(xmc)' bandwidth estimation

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

     The function produces two graphs showing the lack of proper fit of
     the resulting sample.

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

     Christian P. Robert and George Casella

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

     Chapter 8 of *EnteR Monte Carlo Statistical Methods*

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

     sqar

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

     sqaradap()

