new wrapper for graphical outputs in identification.
Marco Ratto
parent
cda26e6fcc
commit
a512436295
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function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, save_figure)
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global M_ options_
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if nargin<10 || isempty(save_figure),
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save_figure=0;
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end
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[SampleSize, nparam]=size(params);
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siJnorm = idemoments.siJnorm;
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siHnorm = idemodel.siHnorm;
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siLREnorm = idelre.siLREnorm;
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% if prior_exist,
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% tittxt = 'Prior mean - ';
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% else
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% tittxt = '';
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% end
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if SampleSize == 1,
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siJ = idemoments.siJ;
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normJ = max(abs(siJ)')';
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figure('Name',[tittxt, 'Identification using info from observables']),
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subplot(211)
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mmm = (idehess.ide_strength_J);
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[ss, is] = sort(mmm);
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bar(log([idehess.ide_strength_J(:,is)' idehess.ide_strength_J_prior(:,is)']))
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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for ip=1:nparam,
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text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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legend('relative to param value','relative to prior std','Location','Best')
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if idehess.flag_score,
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title('Identification strength in the asymptotic Information matrix (log-scale)')
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else
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title('Identification strength in the moments (log-scale)')
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end
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subplot(212)
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mmm = (siJnorm)'./max(siJnorm);
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if advanced,
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mmm1 = (siHnorm)'./max(siHnorm);
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mmm=[mmm mmm1];
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mmm1 = (siLREnorm)'./max(siLREnorm);
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offset=length(siHnorm)-length(siLREnorm);
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mmm1 = [NaN(offset,1); mmm1];
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mmm=[mmm mmm1];
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end
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bar(mmm(is,:))
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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for ip=1:nparam,
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text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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if advanced,
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legend('Moments','Model','LRE model','Location','Best')
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end
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title('Sensitivity bars')
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if advanced
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% identificaton patterns
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for j=1:size(idemoments.cosnJ,2),
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pax=NaN(nparam,nparam);
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fprintf('\n\n')
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disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
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fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
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for i=1:nparam,
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namx='';
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for in=1:j,
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dumpindx = idemoments.pars{i,j}(in);
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if isnan(dumpindx),
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namx=[namx ' ' sprintf('%-15s','--')];
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else
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namx=[namx ' ' sprintf('%-15s',name{dumpindx})];
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pax(i,dumpindx)=idemoments.cosnJ(i,j);
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end
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end
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fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosnJ(i,j))
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end
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figure('name',[tittxt,'Collinearity patterns with ', int2str(j) ,' parameter(s)']),
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imagesc(pax,[0 1]);
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set(gca,'xticklabel','')
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set(gca,'yticklabel','')
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for ip=1:nparam,
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text(ip,(0.5),name{ip},'rotation',90,'HorizontalAlignment','left','interpreter','none')
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text(0.5,ip,name{ip},'rotation',0,'HorizontalAlignment','right','interpreter','none')
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end
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colorbar;
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ax=colormap;
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ax(1,:)=[0.9 0.9 0.9];
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colormap(ax);
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if nparam>10,
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set(gca,'xtick',(5:5:nparam))
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set(gca,'ytick',(5:5:nparam))
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end
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set(gca,'xgrid','on')
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set(gca,'ygrid','on')
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if save_figure
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saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_collinearity_', int2str(j)])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
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if options_.nograph, close(gcf); end
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end
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end
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disp('')
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if idehess.flag_score,
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[U,S,V]=svd(idehess.AHess,0);
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if nparam<5,
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f1 = figure('name',[tittxt,'Identification patterns (Information matrix)']);
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else
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f1 = figure('name',[tittxt,'Identification patterns (Information matrix): SMALLEST SV']);
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f2 = figure('name',[tittxt,'Identification patterns (Information matrix): HIGHEST SV']);
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end
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else
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[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);
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if nparam<5,
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f1 = figure('name',[tittxt,'Identification patterns (moments)']);
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else
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f1 = figure('name',[tittxt,'Identification patterns (moments): SMALLEST SV']);
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f2 = figure('name',[tittxt,'Identification patterns (moments): HIGHEST SV']);
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end
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end
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for j=1:min(nparam,8),
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if j<5,
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figure(f1),
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jj=j;
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else
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figure(f2),
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jj=j-4;
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end
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subplot(4,1,jj),
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if j<5
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bar(abs(V(:,end-j+1))),
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Stit = S(end-j+1,end-j+1);
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else
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bar(abs(V(:,jj))),
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Stit = S(jj,jj);
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end
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set(gca,'xticklabel','')
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if j==4 || j==nparam || j==8,
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for ip=1:nparam,
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text(ip,-0.02,name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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end
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title(['Singular value ',num2str(Stit)])
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end
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if save_figure,
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figure(f1);
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saveas(f1,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_1'])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_1']);
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if nparam>4,
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figure(f2),
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saveas(f2,[IdentifDirectoryName,'/',M_.fname,'_ident_pattern_2'])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
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end
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end
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end
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else
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figure('Name',['MC sensitivities']),
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mmm = (idehess.ide_strength_J);
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[ss, is] = sort(mmm);
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mmm = mean(siJnorm)';
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mmm = mmm./max(mmm);
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if advanced,
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mmm1 = mean(siHnorm)';
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mmm=[mmm mmm1./max(mmm1)];
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mmm1 = mean(siLREnorm)';
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offset=size(siHnorm,2)-size(siLREnorm,2);
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mmm1 = [NaN(offset,1); mmm1./max(mmm1)];
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mmm=[mmm mmm1];
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end
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bar(mmm(is,:))
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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for ip=1:nparam,
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text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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if advanced,
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legend('Moments','Model','LRE model','Location','Best')
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end
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title('MC mean of sensitivity measures')
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if advanced,
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options_.nograph=1;
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figure('Name','MC Condition Number'),
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subplot(221)
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hist(log10(idemodel.cond))
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title('log10 of Condition number in the model')
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subplot(222)
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hist(log10(idemoments.cond))
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title('log10 of Condition number in the moments')
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subplot(223)
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hist(log10(idelre.cond))
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title('log10 of Condition number in the LRE model')
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saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_COND'])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
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if options_.nograph, close(gcf); end
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ncut=floor(SampleSize/10*9);
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[~,is]=sort(idelre.cond);
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[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberLRE', 1, [], IdentifDirectoryName, 0.1);
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[~,is]=sort(idemodel.cond);
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[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberModel', 1, [], IdentifDirectoryName, 0.1);
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[~,is]=sort(idemoments.cond);
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[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), 'MC_HighestCondNumberMoments', 1, [], IdentifDirectoryName, 0.1);
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% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
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for j=1:nparam,
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% ibeh=find(idemoments.Mco(j,:)<0.9);
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% inonbeh=find(idemoments.Mco(j,:)>=0.9);
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% if ~isempty(ibeh) && ~isempty(inonbeh)
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% [proba, dproba] = stab_map_1(params, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
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% end
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[~,is]=sort(idemoments.Mco(:,j));
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[proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), ['MC_HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName, 0.15);
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end
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end
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end
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% disp_identification(params, idemodel, idemoments, name)
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