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identification_analysis.m
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plot_identification.m 14.61 KiB
function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, save_figure)
% function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, save_figure)
%
% INPUTS
% o params [array] parameter values for identification checks
% o idemoments [structure] identification results for the moments
% o idehess [structure] identification results for the Hessian
% o idemodel [structure] identification results for the reduced form solution
% o idelre [structure] identification results for the LRE model
% o advanced [integer] flag for advanced identification checks
% o tittxt [char] name of the results to plot
% o name [char] list of names
% o IdentifDirectoryName [char] directory name
% o save_figure [integer] flag for saving plots (=1) or not (=0)
%
% OUTPUTS
% None
%
% SPECIAL REQUIREMENTS
% None
% Copyright (C) 2008-2011 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_
if nargin<10 || isempty(save_figure),
save_figure=0;
end
[SampleSize, nparam]=size(params);
siJnorm = idemoments.siJnorm;
siHnorm = idemodel.siHnorm;
siLREnorm = idelre.siLREnorm;
% if prior_exist,
% tittxt = 'Prior mean - ';
% else
% tittxt = '';
% end
tittxt1=regexprep(tittxt, ' ', '_');
tittxt1=strrep(tittxt1, '.', '');
if SampleSize == 1,
siJ = idemoments.siJ;
figure('Name',[tittxt, ' - Identification using info from observables']),
subplot(211)
mmm = (idehess.ide_strength_J);
[ss, is] = sort(mmm);
bar(log([idehess.ide_strength_J(:,is)' idehess.ide_strength_J_prior(:,is)']))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
legend('relative to param value','relative to prior std','Location','Best') if idehess.flag_score,
title('Identification strength with asymptotic Information matrix (log-scale)')
else
title('Identification strength with moments Information matrix (log-scale)')
end
subplot(212)
bar(log([idehess.deltaM(is) idehess.deltaM_prior(is)]))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
legend('relative to param value','relative to prior std','Location','Best')
if idehess.flag_score,
title('Sensitivity component with asymptotic Information matrix (log-scale)')
else
title('Sensitivity component with moments Information matrix (log-scale)')
end
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_strength_',tittxt1])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_strength_' tittxt1]);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_strength_' tittxt1]);
if advanced,
disp(' ')
disp('Press ENTER to display advanced diagnostics'), pause,
figure('Name',[tittxt, ' - Sensitivity plot']),
subplot(211)
mmm = (siJnorm)'./max(siJnorm);
mmm1 = (siHnorm)'./max(siHnorm);
mmm=[mmm mmm1];
mmm1 = (siLREnorm)'./max(siLREnorm);
offset=length(siHnorm)-length(siLREnorm);
mmm1 = [NaN(offset,1); mmm1];
mmm=[mmm mmm1];
bar(log(mmm(is,:).*100))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
if advanced,
legend('Moments','Model','LRE model','Location','Best')
end
title('Sensitivity bars using derivatives (log-scale)')
if save_figure
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_sensitivity_',tittxt1])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1]);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1]);
end
% identificaton patterns
for j=1:size(idemoments.cosnJ,2),
pax=NaN(nparam,nparam);
fprintf('\n')
disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
for i=1:nparam,
namx='';
for in=1:j,
dumpindx = idemoments.pars{i,j}(in);
if isnan(dumpindx),
namx=[namx ' ' sprintf('%-15s','--')];
else
namx=[namx ' ' sprintf('%-15s',name{dumpindx})];
pax(i,dumpindx)=idemoments.cosnJ(i,j);
end end
fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosnJ(i,j))
end
figure('name',[tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)']),
imagesc(pax,[0 1]);
set(gca,'xticklabel','')
set(gca,'yticklabel','')
for ip=1:nparam,
text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
end
colorbar;
ax=colormap;
ax(1,:)=[0.9 0.9 0.9];
colormap(ax);
if nparam>10,
set(gca,'xtick',(5:5:nparam))
set(gca,'ytick',(5:5:nparam))
end
set(gca,'xgrid','on')
set(gca,'ygrid','on')
xlabel([tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)'])
if save_figure
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_collinearity_', tittxt1, '_', int2str(j)])
eval(['print -depsc ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_' tittxt1 '_' int2str(j)]);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_' tittxt1 '_' int2str(j)]);
if options_.nograph, close(gcf); end
end
end
disp('')
if idehess.flag_score,
[U,S,V]=svd(idehess.AHess,0);
S=diag(S);
if nparam<5,
f1 = figure('name',[tittxt,' - Identification patterns (Information matrix)']);
else
f1 = figure('name',[tittxt,' - Identification patterns (Information matrix): SMALLEST SV']);
f2 = figure('name',[tittxt,' - Identification patterns (Information matrix): HIGHEST SV']);
end
else
S = idemoments.S;
V = idemoments.V;
if nparam<5,
f1 = figure('name',[tittxt,' - Identification patterns (moments)']);
else
f1 = figure('name',[tittxt,' - Identification patterns (moments): SMALLEST SV']);
f2 = figure('name',[tittxt,' - Identification patterns (moments): HIGHEST SV']);
end
end
for j=1:min(nparam,8),
if j<5,
figure(f1),
jj=j;
else
figure(f2),
jj=j-4;
end
subplot(4,1,jj),
if j<5
bar(abs(V(:,end-j+1))),
Stit = S(end-j+1);
else
bar(abs(V(:,jj))),
Stit = S(jj);
end
set(gca,'xticklabel','')
if j==4 || j==nparam || j==8,
for ip=1:nparam,
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end end
title(['Singular value ',num2str(Stit)])
end
if save_figure,
figure(f1);
saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_',tittxt1,'_1'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_1']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_1']);
if nparam>4,
figure(f2),
saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_',tittxt1,'_2'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_2']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_2']);
end
end
end
else
figure('Name',['MC sensitivities']),
subplot(211)
mmm = (idehess.ide_strength_J);
[ss, is] = sort(mmm);
mmm = mean(siJnorm)';
mmm = mmm./max(mmm);
if advanced,
mmm1 = mean(siHnorm)';
mmm=[mmm mmm1./max(mmm1)];
mmm1 = mean(siLREnorm)';
offset=size(siHnorm,2)-size(siLREnorm,2);
mmm1 = [NaN(offset,1); mmm1./max(mmm1)];
mmm=[mmm mmm1];
end
bar(mmm(is,:))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
for ip=1:nparam,
text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
if advanced,
legend('Moments','Model','LRE model','Location','Best')
end
title('MC mean of sensitivity measures')
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_MC_sensitivity'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_MC_sensitivity']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_MC_sensitivity']);
if options_.nograph, close(gcf); end
if advanced,
disp(' ')
disp('Press ENTER to display advanced diagnostics'), pause,
options_.nograph=1;
figure('Name','MC Condition Number'),
subplot(221)
hist(log10(idemodel.cond))
title('log10 of Condition number in the model')
subplot(222)
hist(log10(idemoments.cond))
title('log10 of Condition number in the moments')
subplot(223)
hist(log10(idelre.cond))
title('log10 of Condition number in the LRE model')
saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
if options_.nograph, close(gcf); end
ncut=floor(SampleSize/10*9);
[dum,is]=sort(idelre.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberLRE', 1, [], IdentifDirectoryName, 0.1);
[dum,is]=sort(idemodel.cond); [proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberModel', 1, [], IdentifDirectoryName, 0.1);
[dum,is]=sort(idemoments.cond);
[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberMoments', 1, [], IdentifDirectoryName, 0.1);
% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
% for j=1:nparam,
% % ibeh=find(idemoments.Mco(j,:)<0.9);
% % inonbeh=find(idemoments.Mco(j,:)>=0.9);
% % if ~isempty(ibeh) && ~isempty(inonbeh)
% % [proba, dproba] = stab_map_1(params, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
% % end
% [~,is]=sort(idemoments.Mco(:,j));
% [proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), ['MC_HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName, 0.15);
% end
if nparam<5,
f1 = figure('name',[tittxt,' - MC Identification patterns (moments)']);
else
f1 = figure('name',[tittxt,' - MC Identification patterns (moments): SMALLEST SV']);
f2 = figure('name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
end
nplots=min(nparam,8);
if nplots>4,
nsubplo=ceil(nplots/2);
else
nsubplo=nplots;
end
for j=1:nplots,
if (nparam>4 && j<=ceil(nplots/2)) || nparam<5,
figure(f1),
jj=j;
VVV=squeeze(abs(idemoments.V(:,:,end-j+1)));
SSS = idemoments.S(:,end-j+1);
else
figure(f2),
jj=j-ceil(nplots/2);
VVV=squeeze(abs(idemoments.V(:,:,jj)));
SSS = idemoments.S(:,jj);
end
subplot(nsubplo,1,jj),
for i=1:nparam,
[post_mean, post_median(:,i), post_var, hpd_interval(i,:), post_deciles] = posterior_moments(VVV(:,i),0,0.9);
end
bar(post_median)
hold on, plot(hpd_interval,'--*r'),
Stit=mean(SSS);
set(gca,'xticklabel','')
if j==4 || j==nparam || j==8,
for ip=1:nparam,
text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
title(['MEAN Singular value ',num2str(Stit)])
end
if save_figure,
figure(f1);
saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_MC_dent_pattern_1'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_1']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_1']);
if nparam>4,
figure(f2),
saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_MC_ident_pattern_2'])
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_2']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_2']);
end
end
end
end
% disp_identification(params, idemodel, idemoments, name)