% A demo for basic Bayesian Model Averaging
% with R and Matlab
% see http://bms.zeugner.eu for further information
%load all the neccessary information
% and start an R session in the background:
libraryBMS;
% load some sample data on economic growth;
load('datafls') %Note that this is the data from Fernandez, Ley and Steel (2001): Model uncertainty in cross-country growth regressions; JAE
% do BMA with the default settings, with 3000 bur-in draws and 5000 counted
% iterations
% type 'help bms' for more information
mm=bms(datafls, 3000, 5000);
% note that this produces a chart that can be reproduced with plotConv
% show the PIPs and coefficients in their original order
coef_bma(mm,'',false);
% show the Posterior model probabilities of the 10 best models
pmp_bma(mm,1:10);
% plot prior and posterior model size distribution
plotModelsize(mm)
%plot the Posterior model probabilities and signs for the the 50 best
% models
image_bma(mm, NaN, false, 1:50);
set(gca,'FontSize',7); %this decreases font size in the chart in order to deal with the many variables
%plot the posterior density for the first regressor
ll=density_bma(mm,1);
%reprint the information from the bms call
print_bma(mm);
% Consider moreover the example in
help saveRworkspace
%Now try to do more with bms command.
%try for instance the according GUI
bmsGUI('datafls');
%close the R session and remove Matlab paths
closeBMS;