spatBMS-package           package:spatBMS           R Documentation

_B_a_y_e_s_i_a_n _M_o_d_e_l _A_v_e_r_a_g_i_n_g _w_i_t_h _u_n_c_e_r_t_a_i_n _S_p_a_t_i_a_l _E_f_f_e_c_t_s _0._0._0

_D_e_s_c_r_i_p_t_i_o_n:

     This package enables Bayesian Model Averaging with uncertain
     spatial effects data over the classical normal-conjugate model
     with many prior options and posterior statistics.

_D_e_t_a_i_l_s:


       Package:  spatBMS
       Type:     Package
       Version:  0.0.0
       Date:     2010-10-27
       License:  Artistic 2.0

     The most important function is 'spatFilt.bms' to perform bayesian
     model sampling augmented with  extracted eigenvectors of different
     underlying spatial weight matrices.
      It basically offers to sample data according to different
     g-priors and model priors, and leaves the choice of different
     samplers (MCMC samplers and interaction samplers).
      The results provide analysis into models according to MCMC
     frequencies, and according to the posterior likelihood of the best
     'nmodel' models (cf. 'spatFilt.bms').

     All functions that come with the 'bms' package are still
     applicable. The functions 'coef.bma' and 'summary.bma' summarize
     the most important results.

     The plotting functions 'plot.bma', 'image.bma', 'density.bma', and
     'gdensity' are the most important plotting functions (inter alia).

     Moreover there are other functions for posterior results, such as
     'beta.draws.bma', 'pmp.bma', and 'topmodels.bma', while 'c.bma'
     helps to combine and parallelize sampling chains.

     The function 'zlm' estimates a Bayesian linear regression under
     Zellner's g prior, i.e. estimating a particular model without
     taking model uncertainty into account.

     Finally, the small-scale functions 'f21hyper', 'hex2bin' and
     'fullmodel.ssq' provide addidtional utilities.

     Consider the functions 'topmod' and as well as the internal
     functions '.choose.mprior' and '.choose.gprior' for more advanced
     programming tasks.

_A_u_t_h_o_r(_s):

     Martin Feldkircher 

     Maintainer: Martin Feldkircher <bms@zeugner.eu>

_R_e_f_e_r_e_n_c_e_s:

     Crespo Cuaresma, Jesus and M. Feldkircher (2010): Spatial
     Filtering, Model Uncertainty and the Speed of Income Convergence
     in Europe. Working Papers 160, Oesterreichische Nationalbank.

     Feldkircher, M. and S. Zeugner (2009): Benchmark Priors Revisited:
     On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model
     Averaging; IMF Working Paper 09-202

_S_e_e _A_l_s_o:

     <URL: http://bms.zeugner.eu>

_E_x_a_m_p_l_e_s:

       library(BMS)
       data(dataBoston); data(WL.boston)
       # estimating a standard MC3 chain 
       bma1=spatFilt.bms(X.data=dataBoston,WList=WL.boston,burn=1e05,iter=1e05,
                   nmodel=100,mcmc="bd",g="bric",mprior="random",mprior.size=(ncol(dataBoston)-1)/2)
       
       estimates.bma(bma1)            
       coef(bma1,exact=TRUE, std.coefs=TRUE) #standard coefficients based on exact likelihoods of the 100 best models
       # look at posterior inclusion probabilities of weight matrices
       pmpW.bma(bma1)
       
       # test for remaining spatial residual autocorrelation
       library(spdep)
       data(boston.soi)
       moran=moranTest.bma(bma1,W=nb2listw(boston.soi))           

