NMixMCMCwrapper            package:mixAK            R Documentation

_W_r_a_p_p_e_r _t_o _t_h_e _N_M_i_x_M_C_M_C _m_a_i_n _s_i_m_u_l_a_t_i_o_n.

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

     This is wrapper to the NMixMCMC main simulation which allows
     vectorized evaluation and possibly parallel computation.

     THIS FUNCTION IS NOT TO BE CALLED BY ORDINARY USERS.

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

     NMixMCMCwrapper(chain=1,
                     z0, z1, censor,
                     p, n, scale, prior, inits, RJMCMC,
                     Cinteger, Cdouble, CRJMCMC,
                     actionAll, nMCMC, keep.chains, PED,
                     dens.zero)

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

   chain: identification of the chain sampled in a particular call of
          this function, usually number like 1, 2, ...

      z0: n x p matrix with shifted and scaled main limits of observed
          intervals.

      z0: n x p matrix with shifted and scaled upper limits of observed
          intervals.

  censor: n x p matrix with censoring indicators.

       p: dimension of the response.

       n: number of observations.

   scale: a list specifying how to scale the data before running MCMC.
          See argument 'scale' in 'NMixMCMC' 

   prior: a list specifying prior hyperparameters. See argument 'prior'
          in 'NMixMCMC'. 

   inits: a list of length at least 'chain'. Its 'chain'-th component
          is used. Each component of the list should have the structure
          of 'init' argument of function 'NMixMCMC'. 

  RJMCMC: a list specifying parameters for RJ-MCMC. See argument
          'RJMCMC' in 'NMixMCMC'

Cinteger: a numeric vector with integer prior parameters.

 Cdouble: a numeric vector with double precission prior parameters.

 CRJMCMC: a numeric vector with parameters for RJ-MCMC.

actionAll: argument for underlying C++ function.

   nMCMC: vector giving the length of MCMC etc.

keep.chains: logical. If 'FALSE', only summary statistics are returned
          in the resulting object. This might be useful in the model
          searching step to save some memory.

     PED: a logical value which indicates whether the penalized
          expected deviance (see Plummer, 2008 for more details) will
          be computed (which requires two parallel chains). Even if
          'keep.chains' is 'FALSE', it is necessary to keep (for a
          while) at least some chains to compute PED. 

dens.zero: small number (1e-300) to determine whether the contribution
          to the deviance (-log density) is equal to infinity. Such
          values are trimmed when computing expected deviance.

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

     A list having almost the same components as object returned by
     'NMixMCMC' function.

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

     Arno&#353t Kom&#225rek arnost.komarek[AT]mff.cuni.cz

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

     'NMixMCMC'.

