sqar                  package:mcsm                  R Documentation

_I_l_l_u_s_t_r_a_t_i_o_n _o_f _s_o_m_e _o_f _c_o_d_a'_s _c_r_i_t_e_r_i_o_n_s _o_n _t_h_e _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 illustrates some of 'coda''s criterions on the noisy
     squared AR model, using a Metro\-polis-Has\-tings algorithm based
     on a random walk. Depending on the value of the boolean 'multies',
     those criterions are either the 'geweke.diag' and 'heidel.diag'
     diagnostics, along with a Kolmo\-gorov-Smir\-nov test of our own,
     or  'plot(mcmc.list())' if several parallel chains are produced
     together.

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

     sqar(T = 10^4, multies = FALSE, outsave = FALSE, npara = 5)

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

       T: Number of MCMC iterations

 multies: Boolean variable determining whether or not parallel chains
          are simulated

 outsave: Boolean variable determining whether or not the MCMC output
          is saved

   npara: Number of parallel chains

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

     This function produces plots and, if 'outsave' is 'TRUE', it
     produces a 'list' with value the MMC sample(s).

_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:

     sqaradap

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

     ousqar=sqar(outsave=TRUE)

