314 lines
13 KiB
Matlab
314 lines
13 KiB
Matlab
function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName)
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% function plot_identification(params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName)
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%
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% INPUTS
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% o params [array] parameter values for identification checks
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% o idemoments [structure] identification results for the moments
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% o idehess [structure] identification results for the Hessian
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% o idemodel [structure] identification results for the reduced form solution
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% o idelre [structure] identification results for the LRE model
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% o advanced [integer] flag for advanced identification checks
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% o tittxt [char] name of the results to plot
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% o name [char] list of names
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% o IdentifDirectoryName [char] directory name
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%
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% OUTPUTS
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% None
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%
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% SPECIAL REQUIREMENTS
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% None
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% Copyright (C) 2008-2012 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_ options_
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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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tittxt1=regexprep(tittxt, ' ', '_');
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tittxt1=strrep(tittxt1, '.', '');
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if SampleSize == 1,
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siJ = idemoments.siJ;
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hh = dyn_figure(options_,'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 with asymptotic Information matrix (log-scale)')
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else
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title('Identification strength with moments Information matrix (log-scale)')
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end
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subplot(212)
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bar(log([idehess.deltaM(is) idehess.deltaM_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('Sensitivity component with asymptotic Information matrix (log-scale)')
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else
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title('Sensitivity component with moments Information matrix (log-scale)')
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end
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dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_strength_' tittxt1],options_);
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if advanced,
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if ~options_.nodisplay,
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disp(' ')
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disp('Press ENTER to plot advanced diagnostics'), pause(5),
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end
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hh = dyn_figure(options_,'Name',[tittxt, ' - Sensitivity plot']);
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subplot(211)
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mmm = (siJnorm)'./max(siJnorm);
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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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bar(log(mmm(is,:).*100))
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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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title('Sensitivity bars using derivatives (log-scale)')
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dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_sensitivity_' tittxt1 ],options_);
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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')
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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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hh = dyn_figure(options_,'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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xlabel([tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)'],'interpreter','none')
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dyn_saveas(hh,[ IdentifDirectoryName '/' M_.fname '_ident_collinearity_' tittxt1 '_' int2str(j) ],options_);
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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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S=diag(S);
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if nparam<5,
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f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (Information matrix)']);
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else
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f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (Information matrix): SMALLEST SV']);
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f2 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (Information matrix): HIGHEST SV']);
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end
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else
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S = idemoments.S;
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V = idemoments.V;
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if nparam<5,
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f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (moments)']);
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else
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f1 = dyn_figure(options_,'Name',[tittxt,' - Identification patterns (moments): SMALLEST SV']);
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f2 = dyn_figure(options_,'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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set(0,'CurrentFigure',f1),
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jj=j;
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else
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set(0,'CurrentFigure',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);
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else
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bar(abs(V(:,jj))),
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Stit = S(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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dyn_saveas(f1,[ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_1' ],options_);
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if nparam>4,
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dyn_saveas(f2,[ IdentifDirectoryName '/' M_.fname '_ident_pattern_' tittxt1 '_2' ],options_);
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end
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end
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else
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hh = dyn_figure(options_,'Name',['MC sensitivities']);
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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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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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dyn_saveas(hh,[ IdentifDirectoryName '/' M_.fname '_MC_sensitivity' ],options_);
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if advanced,
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if ~options_.nodisplay,
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disp(' ')
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disp('Press ENTER to display advanced diagnostics'), pause(5),
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end
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% options_.nograph=1;
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hh = dyn_figure(options_,'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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dyn_saveas(hh,[IdentifDirectoryName '/' M_.fname '_ident_COND' ],options_);
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ncut=floor(SampleSize/10*9);
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[dum,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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[dum,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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[dum,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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if nparam<5,
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f1 = dyn_figure(options_,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
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else
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f1 = dyn_figure(options_,'Name',[tittxt,' - MC Identification patterns (moments): SMALLEST SV']);
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f2 = dyn_figure(options_,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
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end
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nplots=min(nparam,8);
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if nplots>4,
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nsubplo=ceil(nplots/2);
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else
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nsubplo=nplots;
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end
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for j=1:nplots,
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if (nparam>4 && j<=ceil(nplots/2)) || nparam<5,
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set(0,'CurrentFigure',f1),
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jj=j;
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VVV=squeeze(abs(idemoments.V(:,:,end-j+1)));
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SSS = idemoments.S(:,end-j+1);
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else
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set(0,'CurrentFigure',f2),
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jj=j-ceil(nplots/2);
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VVV=squeeze(abs(idemoments.V(:,:,jj)));
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SSS = idemoments.S(:,jj);
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end
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subplot(nsubplo,1,jj),
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for i=1:nparam,
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[post_mean, post_median(:,i), post_var, hpd_interval(i,:), post_deciles] = posterior_moments(VVV(:,i),0,0.9);
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end
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bar(post_median)
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hold on, plot(hpd_interval,'--*r'),
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Stit=mean(SSS);
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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(['MEAN Singular value ',num2str(Stit)])
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end
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dyn_saveas(f1,[IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_1' ],options_);
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if nparam>4,
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dyn_saveas(f2,[ IdentifDirectoryName '/' M_.fname '_MC_ident_pattern_2' ],options_);
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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) |