asympvar               package:geoRglm               R Documentation

_A_s_y_m_p_t_o_t_i_c _V_a_r_i_a_n_c_e

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

     Calculates the initial monotone/positive sequence estimate of the
     asymptotic variance from CLT (Geyer 92). Useful for estimation of
     the variance of a Markov Chain Monte Carlo estimate.

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

     asympvar(timeseries, type="mon", lag.max = 100, messages)

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

timeseries: a vector with a timeseries, or a matrix where the rows are
          different timeseries.

    type: '"pos"' and '"mon"' gives the monotone and the positive
          sequence estimator, respectively, and '"all"' gives both.
          Default is 'type="mon"'.  

 lag.max: maximum lag at which to calculate the asymptotic variance. 
          Default is 'lag.max = 100'.  

messages: logical. If 'TRUE', the default, status messages are printed
          while the function is running.  

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

     A number (or a vector) with the estimate, when 'type="mon"' or
     'type="pos"'. A list with components 'mon' and 'pos' when 
     'type="all"'

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

     Ole F. Christensen olefc@birc.dk, 
      Paulo J. Ribeiro Jr. Paulo.Ribeiro@est.ufpr.br.

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

     Geyer, C. (1992). Practical Monte Carlo (with discussion). 
     _Statist. Sci._ *7*, 473-511.

     Further information about *geoRglm* can be found at:
      <URL: http://www.maths.lancs.ac.uk/~christen/geoRglm>.

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

     data(p50)
     ## Not run: 
     test <- pois.krige(p50, krige = krige.glm.control(cov.pars = c(1,1), beta = 1),
           mcmc.input = mcmc.control(S.scale = 0.5, n.iter = 1000, thin = 1))
     asympvar(test$intensity[45,])
     ass <- asympvar(test$intensity[1:10,], type = "pos")
     ## End(Not run)

