numEff                package:bayesm                R Documentation

_C_o_m_p_u_t_e _N_u_m_e_r_i_c_a_l _S_t_a_n_d_a_r_d _E_r_r_o_r _a_n_d _R_e_l_a_t_i_v_e _N_u_m_e_r_i_c_a_l _E_f_f_i_c_i_e_n_c_y

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

     'numEff' computes the numerical standard error for the mean of a
     vector of draws as well as the relative numerical efficiency
     (ratio of variance of mean of this time series process relative to
     iid sequence).

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

     numEff(x, m = as.integer(min(length(x), (100/sqrt(5000)) * sqrt(length(x)))))

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

       x: R x 1 vector of draws 

       m: number of lags for autocorrelations 

_D_e_t_a_i_l_s:

     default for number of lags is chosen so that if R = 5000, m =100
     and increases as the sqrt(R).

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

 stderr : standard error of the mean of x

      f : variance ratio (relative numerical efficiency) 

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

     This routine is a utility routine that does *not* check the input
     arguments for proper dimensions and type.

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

     Peter Rossi, Graduate School of Business, University of Chicago,
     Peter.Rossi@ChicagoGsb.edu.

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

     For further discussion, see _Bayesian Statistics and Marketing_ by
     Rossi, Allenby and McCulloch, Chapter 3. 
      <URL:
     http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html>

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

      numEff(rnorm(1000),m=20)
      numEff(rnorm(1000))

