pmpW.bma               package:spatBMS               R Documentation

_P_o_s_t_e_r_i_o_r _M_o_d_e_l _P_r_o_b_a_b_i_l_i_t_i_e_s _o_f _W_e_i_g_h_t _M_a_t_r_i_c_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Returns the posterior models probabilites based on the best models
     encountered by a 'bma' object

_U_s_a_g_e:

     pmpW.bma(object)

_A_r_g_u_m_e_n_t_s:

  object: A spatial filtering bma object see argument 'nmodel' in
          'spatFilt.bms') 

_D_e_t_a_i_l_s:

     A call to bms with an MCMC sampler (e.g.
     'spatFilt.bms(datafls,mcmc="bd",nmodel=100)' uses a
     Metropolis-Hastings algorithm to sample through the model space -
     and the frequency of how often models are drawn converges to the
     distribution of their posterior marginal likelihoods. While
     sampling, each 'bma' object stores the best models encountered by
     its sampling chain with their marginal likelihood and their MCMC
     frequencies.

     'pmp.bma' then allows for comparing the posterior model
     probabilities (PMPs) for the two different methods, similar to
     'plotConv'. It calculates the PMPs based on marginal likelihoods
     (first column) and the PMPs based on MCMC frequencies (second
     column) for the best x models stored in the bma object.

     The correlation of the two columns is an indicator of how well the
     MCMC sampler has converged to the actual PMP distribution - it is
     therefore also given in the output of 'summary.bma'.

     The second column thus shows the PMPs of the best models relative
     to all models the call to 'bms' has sampled through (therefore
     typically the second column adds up to less than one). However,
     the first column only relates to the likelihoods of the best
     models, therefore it adds up to 1. (the marginal likelihoods for
     the models not retained in the best models cannot be known).

_V_a_l_u_e:

     the result is a matrix with rownames corresponding to the names of
     the weight matrices
      There are two columns in the matrix: 

PMP (Exact): posterior model probabilities based on the posterior
          likelihoods of the best models in 'object', relative to all
          'best' models

PMP (MCMC): posterior model probabilities of the best models in
          'object' based on their MCMC frequencies, relative to all
          models encountered by 'object' - see 'Details' 

_A_u_t_h_o_r(_s):

     Martin Feldkircher

_S_e_e _A_l_s_o:

     'spatFilt.bms' for sampling bma objects 

     Check <URL: http://bms.zeugner.eu> for additional help.

_E_x_a_m_p_l_e_s:

        # sample BMA for boston housing data MCMC sampler
        data(dataBoston); data(WL.boston)
        bma1=spatFilt.bms(X.data=dataBoston,WList=WL.boston,burn=1e04,iter=1e04,nmodel=100)
        pmpW.bma(bma1)

