diff --git a/matlab/dynare_identification.m b/matlab/dynare_identification.m
index b2f6c88f93abfe32b93e4a96e984d3313fecb6e9..4a8ec94381697f26ec0270d4712e7e238e9feab9 100644
--- a/matlab/dynare_identification.m
+++ b/matlab/dynare_identification.m
@@ -53,6 +53,7 @@ options_ident = set_default_option(options_ident,'prior_range',0);
options_ident = set_default_option(options_ident,'periods',300);
options_ident = set_default_option(options_ident,'replic',100);
options_ident = set_default_option(options_ident,'advanced',0);
+
if nargin==2,
options_ident.prior_mc=size(pdraws0,1);
end
@@ -78,7 +79,7 @@ options_.Schur_vec_tol = 1.e-8;
options_ = set_default_option(options_,'datafile',[]);
options_.mode_compute = 0;
options_.plot_priors = 0;
-[data,rawdata,xparam1,data_info]=dynare_estimation_init([],fname_,1);
+[data,rawdata,xparam1,data_info]=dynare_estimation_init(M_.endo_names,fname_,1);
if isempty(data_info),
data_info.gend = periods;
data_info.data = [];
@@ -138,6 +139,10 @@ else
name_tex = [cellstr(M_.exo_names_tex); cellstr(M_.param_names_tex)];
end
+options_ident = set_default_option(options_ident,'max_ord_bruteforce',min([2,nparam-1]));
+max_ord_bruteforce = options_ident.max_ord_bruteforce;
+
+
MaxNumberOfBytes=options_.MaxNumberOfBytes;
disp(' ')
@@ -321,12 +326,12 @@ if iload <=0,
identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
% identification_checks(H(indH,:),JJ(indJJ,:), gp(indLRE,:), bayestopt_);
indok = find(max(idemoments.indno{iteration},[],1)==0);
+ ide_strength_J=NaN(1,nparam);
+ ide_strength_J_prior=NaN(1,nparam);
+ if iteration ==1 && advanced,
+ [pars, cosnJ] = ident_bruteforce(JJ(indJJ,:)./normJ(:,ones(nparam,1)),max_ord_bruteforce,options_.TeX,name_tex);
+ end
if iteration ==1 && ~isempty(indok),
- ide_strength_J=NaN(1,nparam);
- ide_strength_J_prior=NaN(1,nparam);
- if advanced,
- [pars, cosnJ] = ident_bruteforce(JJ(indJJ,:)./normJ(:,ones(nparam,1)),2,1,name_tex);
- end
normaliz = abs(params);
if prior_exist,
if ~isempty(estim_params_.var_exo),
@@ -338,6 +343,8 @@ if iload <=0,
normaliz1 = [normaliz1; estim_params_.param_vals(:,7)]'; % normalize with prior standard deviation
end
% normaliz = max([normaliz; normaliz1]);
+ normaliz1(isinf(normaliz1)) = 1;
+
else
normaliz1 = ones(1,nparam);
end
@@ -385,7 +392,7 @@ if iload <=0,
quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,iteration),1,np);
quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE(:,iteration))),length(inok),np);
siLREnorm(iteration,:) = vnorm(quant).*normaliz(offset+1:end);
- % siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*normaliz(offset+1:end);
+ % siLREnorm(iteration,:) = vnorm(siLRE./repmat(LRE(:,iteration),1,nparam-offset)).*normaliz(offset+1:end);
end,
if run_index==MAX_tau || iteration==SampleSize,
file_index = file_index + 1;
@@ -408,7 +415,22 @@ if iload <=0,
if SampleSize > 1,
- close(h)
+ close(h),
+ normTAU=std(TAU')';
+ normLRE=std(LRE')';
+ normGAM=std(GAM')';
+ normaliz1=std(pdraws);
+ iter=0;
+ for ifile_index = 1:file_index,
+ load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(ifile_index) '.mat'], 'stoH', 'stoJJ', 'stoLRE')
+ for irun=1:size(stoH,3),
+ iter=iter+1;
+ siJnorm(iter,:) = vnorm(stoJJ(:,:,irun)./repmat(normGAM,1,nparam)).*normaliz1;
+ siHnorm(iter,:) = vnorm(stoH(:,:,irun)./repmat(normTAU,1,nparam)).*normaliz1;
+ siLREnorm(iter,:) = vnorm(stoLRE(:,:,irun)./repmat(normLRE,1,nparam-offset)).*normaliz1(offset+1:end);
+ end
+
+ end
end
@@ -474,44 +496,44 @@ else
close(h);
end
-if SampleSize>1,
- siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
- siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
- siLREmean = siLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
-
- derJmean = derJmean.*(ones(length(indJJ),1)*std(pdraws));
- derHmean = derHmean.*(ones(length(indH),1)*std(pdraws));
- derLREmean = derLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
-
- derHmean = abs(derHmean./(max(siHmean')'*ones(1,size(pdraws,2))));
- derJmean = abs(derJmean./(max(siJmean')'*ones(1,size(pdraws,2))));
- derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,np)));
-
- siHmean = siHmean./(max(siHmean')'*ones(1,size(pdraws,2)));
- siJmean = siJmean./(max(siJmean')'*ones(1,size(pdraws,2)));
- siLREmean = siLREmean./(max(siLREmean')'*ones(1,np));
-
- tstJmean = derJmean*0;
- tstHmean = derHmean*0;
- tstLREmean = derLREmean*0;
-
- if exist('OCTAVE_VERSION')
- warning('off'),
- else
- warning off,
- end
-
- for j=1:nparam,
- indd = 1:length(siJmean(:,j));
- tstJmean(indd,j) = abs(derJmean(indd,j))./siJmean(indd,j);
- indd = 1:length(siHmean(:,j));
- tstHmean(indd,j) = abs(derHmean(indd,j))./siHmean(indd,j);
- if j>offset
- indd = 1:length(siLREmean(:,j-offset));
- tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
- end
- end
-end
+% if SampleSize>1,
+% siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
+% siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
+% siLREmean = siLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
+%
+% derJmean = derJmean.*(ones(length(indJJ),1)*std(pdraws));
+% derHmean = derHmean.*(ones(length(indH),1)*std(pdraws));
+% derLREmean = derLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
+%
+% derHmean = abs(derHmean./(max(siHmean')'*ones(1,size(pdraws,2))));
+% derJmean = abs(derJmean./(max(siJmean')'*ones(1,size(pdraws,2))));
+% derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,np)));
+%
+% siHmean = siHmean./(max(siHmean')'*ones(1,size(pdraws,2)));
+% siJmean = siJmean./(max(siJmean')'*ones(1,size(pdraws,2)));
+% siLREmean = siLREmean./(max(siLREmean')'*ones(1,np));
+%
+% tstJmean = derJmean*0;
+% tstHmean = derHmean*0;
+% tstLREmean = derLREmean*0;
+%
+% if exist('OCTAVE_VERSION')
+% warning('off'),
+% else
+% warning off,
+% end
+%
+% for j=1:nparam,
+% indd = 1:length(siJmean(:,j));
+% tstJmean(indd,j) = abs(derJmean(indd,j))./siJmean(indd,j);
+% indd = 1:length(siHmean(:,j));
+% tstHmean(indd,j) = abs(derHmean(indd,j))./siHmean(indd,j);
+% if j>offset
+% indd = 1:length(siLREmean(:,j-offset));
+% tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
+% end
+% end
+% end
if nargout>3 && iload,
@@ -530,141 +552,22 @@ end
disp_identification(pdraws, idemodel, idemoments, name, advanced)
-
-if advanced,
- figure('Name','Identification LRE model form'),
- subplot(211)
- if SampleSize > 1,
- mmm = mean(siLREnorm);
- else
- mmm = (siLREnorm);
- end
- [ss, is] = sort(mmm);
- if SampleSize ==1,
- bar(siLREnorm(:,is))
- else
- myboxplot(log(siLREnorm(:,is)))
- end
- % mmm = mean(siLREmean);
- % [ss, is] = sort(mmm);
- % myboxplot(siLREmean(:,is))
- % set(gca,'ylim',[0 1.05])
- set(gca,'xticklabel','')
- for ip=1:np,
- text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- title('Sensitivity in the LRE model')
-
- subplot(212)
- if SampleSize>1,
- mmm = mean(-idelre.Mco');
- else
- mmm = (-idelre.Mco');
- end
- [ss, is] = sort(mmm);
- if SampleSize ==1,
- bar(idelre.Mco(is,:)')
- else
- myboxplot(idelre.Mco(is,:)')
- end
- set(gca,'ylim',[0 1])
- set(gca,'xticklabel','')
- for ip=1:np,
- text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- title('Multicollinearity in the LRE model')
- saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_LRE'])
- eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
- eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
- if options_.nograph, close(gcf); end
-
- figure('Name','Identification in the model'),
- % subplot(311)
- %
- % if SampleSize>1,
- % mmm = mean(ide_strength_H);
- % else
- % mmm = (ide_strength_H);
- % end
- % [ss, is] = sort(mmm);
- % if SampleSize>1,
- % myboxplot(ide_strength_H(:,is))
- % else
- % bar(ide_strength_H(:,is))
- % end
- % % set(gca,'ylim',[0 1.05])
- % set(gca,'xticklabel','')
- % dy = get(gca,'ylim');
- % % dy=dy(2)-dy(1);
- % for ip=1:nparam,
- % text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- % end
- % title('Identification strength in the model')
-
- subplot(211)
- % mmm = mean(siHmean);
- % [ss, is] = sort(mmm);
- % myboxplot(siHmean(:,is))
- if SampleSize>1,
- mmm = mean(siHnorm);
- else
- mmm = (siHnorm);
- end
- [ss, is] = sort(mmm);
- if SampleSize>1,
- myboxplot(log(siHnorm(:,is)))
- else
- bar(siHnorm(:,is))
- end
- % set(gca,'ylim',[0 1.05])
- set(gca,'xticklabel','')
- dy = get(gca,'ylim');
- % dy=dy(2)-dy(1);
- for ip=1:nparam,
- text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- title('Sensitivity in the model')
-
- subplot(212)
- if SampleSize>1,
- mmm = mean(-idemodel.Mco');
- else
- mmm = (-idemodel.Mco');
- end
- % [ss, is] = sort(mmm);
- if SampleSize>1,
- myboxplot(idemodel.Mco(is,:)')
- else
- bar(idemodel.Mco(is,:)')
- end
- set(gca,'ylim',[0 1])
- set(gca,'xticklabel','')
- for ip=1:nparam,
- text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- title('Multicollinearity in the model')
- saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_model'])
- eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_model']);
- eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_model']);
- if options_.nograph, close(gcf); end
-end
-
figure('Name','Identification in the moments'),
subplot(211)
% mmm = mean(siHmean);
% [ss, is] = sort(mmm);
% myboxplot(siHmean(:,is))
-if SampleSize>1,
- mmm = mean(ide_strength_J);
-else
+% if SampleSize>1,
+% mmm = mean(ide_strength_J);
+% else
mmm = (ide_strength_J);
-end
+% end
[ss, is] = sort(mmm);
-if SampleSize>1,
- myboxplot(log(ide_strength_J(:,is)))
-else
+% if SampleSize>1,
+% myboxplot(log(ide_strength_J(:,is)))
+% else
bar(log([ide_strength_J(:,is)' ide_strength_J_prior(:,is)']))
-end
+% end
% set(gca,'ylim',[0 1.05])
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
@@ -686,11 +589,11 @@ else
mmm = (siJnorm);
end
% [ss, is] = sort(mmm);
-if SampleSize > 1,
- myboxplot(log(siJnorm(:,is)))
-else
- bar(siJnorm(:,is))
-end
+% if SampleSize > 1,
+% myboxplot(log(siJnorm(:,is)))
+% else
+ bar(mmm(is))
+% end
% set(gca,'ylim',[0 1.05])
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
@@ -701,32 +604,62 @@ for ip=1:nparam,
end
title('Sensitivity in the moments')
-% subplot(313)
-% if SampleSize>1,
-% mmm = mean(-idemoments.Mco');
-% else
-% mmm = (-idemoments.Mco');
-% end
-% % [ss, is] = sort(mmm);
-% if SampleSize>1,
-% myboxplot(idemoments.Mco(is,:)')
-% else
-% bar(idemoments.Mco(is,:)')
-% end
-% set(gca,'ylim',[0 1])
-% set(gca,'xticklabel','')
-% for ip=1:nparam,
-% text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% title('Multicollinearity in the moments')
-% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_moments'])
-% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
-% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
-% if options_.nograph, close(gcf); end
+if advanced,
+ figure('Name','Identification in the model'),
+ subplot(211)
+ if SampleSize > 1,
+ mmm = mean(siLREnorm);
+ else
+ mmm = (siLREnorm);
+ end
+ mmm = [NaN(1, offset), mmm];
+% [ss, is] = sort(mmm);
+% if SampleSize ==1,
+ bar(mmm(is))
+% else
+% myboxplot(log(siLREnorm(:,is)))
+% end
+ % mmm = mean(siLREmean);
+ % [ss, is] = sort(mmm);
+ % myboxplot(siLREmean(:,is))
+ % set(gca,'ylim',[0 1.05])
+ set(gca,'xticklabel','')
+% for ip=1:np,
+% text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ for ip=1:nparam,
+ text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ title('Sensitivity in the LRE model')
+
+ subplot(212)
+ % mmm = mean(siHmean);
+ % [ss, is] = sort(mmm);
+ % myboxplot(siHmean(:,is))
+ if SampleSize>1,
+ mmm = mean(siHnorm);
+ else
+ mmm = (siHnorm);
+ end
+% [ss, is] = sort(mmm);
+% if SampleSize>1,
+% myboxplot(log(siHnorm(:,is)))
+% else
+ bar(mmm(is))
+% end
+ % set(gca,'ylim',[0 1.05])
+ set(gca,'xticklabel','')
+ dy = get(gca,'ylim');
+ % dy=dy(2)-dy(1);
+ for ip=1:nparam,
+ text(ip,dy(1),name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ title('Sensitivity in the model')
+
+ if options_.nograph, close(gcf); end
-if SampleSize==1 && advanced,
% identificaton patterns
for j=1:size(cosnJ,2),
+ pax=NaN(nparam,nparam);
fprintf('\n\n')
disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
@@ -736,7 +669,24 @@ if SampleSize==1 && advanced,
namx=[namx ' ' sprintf('%-15s',name{pars{i,j}(in)})];
end
fprintf('%-15s [%s] %10.3f\n',name{i},namx,cosnJ(i,j))
+ pax(i,pars{i,j})=cosnJ(i,j);
+ end
+ figure('name',['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);
+ saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_collinearity_', int2str(j)])
+ eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
+ eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_collinearity_', int2str(j)]);
+ if options_.nograph, close(gcf); end
end
disp('')
[U,S,V]=svd(siJ./normJ(:,ones(nparam,1)),0);
@@ -762,8 +712,8 @@ if SampleSize==1 && advanced,
% xlabel('SMALLEST singular values'),
% end,
else
- bar(abs(V(:,j))),
- Stit = S(j,j);
+ bar(abs(V(:,jj))),
+ Stit = S(jj,jj);
% if j==8 || j==nparam,
% xlabel('LARGEST singular values'),
% end,
@@ -786,10 +736,9 @@ if SampleSize==1 && advanced,
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_pattern_2']);
end
-end
-
-if advanced,
- if SampleSize>1
+
+ if SampleSize>1,
+ options_.nograph=1;
figure('Name','Condition Number'),
subplot(221)
hist(log10(idemodel.cond))
@@ -804,73 +753,26 @@ if advanced,
eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_COND']);
if options_.nograph, close(gcf); end
- end
-
-
- pco = NaN(np,np);
- for j=1:np,
- if SampleSize>1
- pco(j+1:end,j) = mean(abs(squeeze(idelre.Pco(j+1:end,j,:))'));
- else
- pco(j+1:end,j) = abs(idelre.Pco(j+1:end,j))';
+ ncut=floor(SampleSize/10*9);
+ [~,is]=sort(idelre.cond);
+ [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberLRE', 1, [], IdentifDirectoryName);
+ [~,is]=sort(idemodel.cond);
+ [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberModel', 1, [], IdentifDirectoryName);
+ [~,is]=sort(idemoments.cond);
+ [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments', 1, [], IdentifDirectoryName);
+% [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(pdraws, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
+% end
+ [~,is]=sort(idemoments.Mco(j,:));
+ [proba, dproba] = stab_map_1(pdraws, is(1:ncut), is(ncut+1:end), ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
end
end
- figure('name','Pairwise correlations in the LRE model'),
- imagesc(pco',[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;
- saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_LRE'])
- eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE']);
- eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_LRE']);
- if options_.nograph, close(gcf); end
- pco = NaN(nparam,nparam);
- for j=1:nparam,
- if SampleSize>1
- pco(j+1:end,j) = mean(abs(squeeze(idemodel.Pco(j+1:end,j,:))'));
- else
- pco(j+1:end,j) = abs(idemodel.Pco(j+1:end,j))';
- end
- end
- figure('name','Pairwise correlations in the model'),
- imagesc(pco',[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;
- saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_model'])
- eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model']);
- eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_model']);
- if options_.nograph, close(gcf); end
- for j=1:nparam,
- if SampleSize>1
- pco(j+1:end,j) = mean(abs(squeeze(idemoments.Pco(j+1:end,j,:))'));
- else
- pco(j+1:end,j) = abs(idemoments.Pco(j+1:end,j))';
- end
- end
- figure('name','Pairwise correlations in the moments'),
- imagesc(pco',[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;
- saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_PCORR_moments'])
- eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments']);
- eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_PCORR_moments']);
- if options_.nograph, close(gcf); end
end