/usr/share/dynare/matlab/plot_identification.m is in dynare-common 4.4.1-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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% function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName)
%
% 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
%
% OUTPUTS
% None
%
% SPECIAL REQUIREMENTS
% None
% Copyright (C) 2008-2013 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_
[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;
hh = dyn_figure(options_,'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
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_strength_' tittxt1],options_);
if advanced,
if ~options_.nodisplay,
skipline()
disp('Press ENTER to plot advanced diagnostics'), pause(5),
end
hh = dyn_figure(options_,'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
legend('Moments','Model','LRE model','Location','Best')
title('Sensitivity bars using derivatives (log-scale)')
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1 ],options_);
% 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
hh = dyn_figure(options_,'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)'],'interpreter','none')
dyn_saveas(hh,[ IdentifDirectoryName '/' M_.fname '_ident_collinearity_' tittxt1 '_' int2str(j) ],options_);
end
skipline()
[U,S,V]=svd(idehess.AHess,0);
S=diag(S);
if idehess.flag_score,
if nparam<5,
f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (Information matrix)']);
else
f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (Information matrix): SMALLEST SV']);
f2 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (Information matrix): HIGHEST SV']);
end
else
% S = idemoments.S;
% V = idemoments.V;
if nparam<5,
f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (moments Information matrix)']);
else
f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (moments Information matrix): SMALLEST SV']);
f2 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (moments Information matrix): HIGHEST SV']);
end
end
for j=1:min(nparam,8),
if j<5,
set(0,'CurrentFigure',f1),
jj=j;
else
set(0,'CurrentFigure',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
dyn_saveas(f1,[ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_1' ],options_);
if nparam>4,
dyn_saveas(f2,[ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_2' ],options_);
end
end
else
hh = dyn_figure(options_,'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')
dyn_saveas(hh,[ IdentifDirectoryName '/' M_.fname '_MC_sensitivity' ],options_);
if advanced,
if ~options_.nodisplay,
skipline()
disp('Press ENTER to display advanced diagnostics'), pause(5),
end
% options_.nograph=1;
hh = dyn_figure(options_,'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')
dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_COND' ],options_);
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 = dyn_figure(options_,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
else
f1 = dyn_figure(options_,'Name',[tittxt,' - MC Identification patterns (moments): SMALLEST SV']);
f2 = dyn_figure(options_,'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,
set(0,'CurrentFigure',f1),
jj=j;
VVV=squeeze(abs(idemoments.V(:,:,end-j+1)));
SSS = idemoments.S(:,end-j+1);
else
set(0,'CurrentFigure',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
dyn_saveas(f1,[IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_1' ],options_);
if nparam>4,
dyn_saveas(f2,[ IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_2' ],options_);
end
end
end
% disp_identification(params, idemodel, idemoments, name)
|