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Identification strength: make plotting consistent 

1. Rely on actually computed standard deviations in bayestopt_ instead of potentially unset estim_params_
2. Remove arbitrary normalizations/omitted normalization in case of division by 0 in normalization
3. Distinguish between 0 identification and division by 0 due to normalization in plots

(cherry picked from commit 7341e21a381850c47fbed018bf6a7acdda4fa92e)
time-shift
Johannes Pfeifer 2018-11-15 19:31:55 +01:00 committed by Sébastien Villemot
parent 3acdcd3657
commit 60d95b65f2
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GPG Key ID: 2CECE9350ECEBE4A
2 changed files with 55 additions and 22 deletions
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48
matlab/identification_analysis.m
View File

@ -123,24 +123,27 @@ if info(1)==0
ide_strength_J=NaN(1,nparam);
ide_strength_J_prior=NaN(1,nparam);
if init
normaliz = NaN(1,nparam);
ide_uncert_unnormaliz = NaN(1,nparam);
offset=0;
if prior_exist
if ~isempty(estim_params_.var_exo)
normaliz1 = estim_params_.var_exo(:,7)'; % normalize with prior standard deviation
normaliz_prior_std = bayestopt_.p2(1:estim_params_.nvx)'; % normalize with prior standard deviation
offset=offset+estim_params_.nvx+estim_params_.nvn;
else
normaliz1=[];
normaliz_prior_std=[];
end
if ~isempty(estim_params_.corrx)
normaliz1 = [normaliz1 estim_params_.corrx(:,8)']; % normalize with prior standard deviation
normaliz_prior_std = [normaliz_prior_std bayestopt_.p2(offset+1:offset+estim_params_.ncx)']; % normalize with prior standard deviation
offset=offset+estim_params_.ncx+estim_params_.ncn;
end
if ~isempty(estim_params_.param_vals)
normaliz1 = [normaliz1 estim_params_.param_vals(:,7)']; % normalize with prior standard deviation
normaliz_prior_std = [normaliz_prior_std bayestopt_.p2(offset+1:offset+estim_params_.np)']; % normalize with prior standard deviation
end
% normaliz = max([normaliz; normaliz1]);
normaliz1(isinf(normaliz1)) = 1;
% normaliz1(isinf(normaliz1)) = 1;
else
normaliz1 = NaN(1,nparam);
normaliz_prior_std = NaN(1,nparam);
end
try
options_.irf = 0;
@ -176,7 +179,7 @@ if info(1)==0
end
indok = find(max(ide_hess.indno,[],1)==0);
cparam(indok,indok) = inv(AHess(indok,indok));
normaliz(indok) = sqrt(diag(cparam(indok,indok)))';
ide_uncert_unnormaliz(indok) = sqrt(diag(cparam(indok,indok)))';
cmm = NaN(size(siJ,1),size(siJ,1));
ind1=find(ide_hess.ind0);
cmm = siJ(:,ind1)*((AHess(ind1,ind1))\siJ(:,ind1)');
@ -242,22 +245,23 @@ if info(1)==0
clre = siLRE(:,ind1-offset)*((MIM(ind1,ind1))\siLRE(:,ind1-offset)');
if ~isempty(indok)
rhoM(indok)=sqrt(1./diag(inv(tildaM(indok,indok))));
normaliz(indok) = (sqrt(diag(inv(tildaM(indok,indok))))./deltaM(indok))'; %sqrt(diag(inv(MIM(indok,indok))))';
ide_uncert_unnormaliz(indok) = (sqrt(diag(inv(tildaM(indok,indok))))./deltaM(indok))'; %sqrt(diag(inv(MIM(indok,indok))))';
end
% deltaM = deltaM.*abs(params')
flag_score=0;
end
ide_strength_J(indok) = (1./(normaliz(indok)'./abs(params(indok)')));
ide_strength_J_prior(indok) = (1./(normaliz(indok)'./normaliz1(indok)'));
ide_strength_J(params==0)=1./normaliz(params==0)';
deltaM_prior = deltaM.*abs(normaliz1');
end
ide_strength_J(indok) = (1./(ide_uncert_unnormaliz(indok)'./abs(params(indok)')));
ide_strength_J_prior(indok) = (1./(ide_uncert_unnormaliz(indok)'./normaliz_prior_std(indok)'));
%ide_strength_J(params==0)=1./ide_uncert_unnormaliz(params==0)';
sensitivity_zero_pos=find(isinf(deltaM));
deltaM_prior = deltaM.*abs(normaliz_prior_std');
deltaM = deltaM.*abs(params');
deltaM(params==0)=deltaM_prior(params==0);
%deltaM(params==0)=deltaM_prior(params==0);
quant = siJ./repmat(sqrt(diag(cmm)),1,nparam);
if size(quant,1)==1
siJnorm = abs(quant).*normaliz1;
siJnorm = abs(quant).*normaliz_prior_std;
else
siJnorm = vnorm(quant).*normaliz1;
siJnorm = vnorm(quant).*normaliz_prior_std;
end
% siJnorm = vnorm(siJ(inok,:)).*normaliz;
quant=[];
@ -272,9 +276,9 @@ if info(1)==0
if ~isempty(iy)
quant = siH./repmat(sqrt(diag_chh(iy)),1,nparam);
if size(quant,1)==1
siHnorm = abs(quant).*normaliz1;
siHnorm = abs(quant).*normaliz_prior_std;
else
siHnorm = vnorm(quant).*normaliz1;
siHnorm = vnorm(quant).*normaliz_prior_std;
end
else
siHnorm = [];
@ -292,9 +296,9 @@ if info(1)==0
if ~isempty(iy)
quant = siLRE./repmat(sqrt(diag_clre(iy)),1,np);
if size(quant,1)==1
siLREnorm = abs(quant).*normaliz1(offset+1:end);
siLREnorm = abs(quant).*normaliz_prior_std(offset+1:end);
else
siLREnorm = vnorm(quant).*normaliz1(offset+1:end);
siLREnorm = vnorm(quant).*normaliz_prior_std(offset+1:end);
end
else
siLREnorm=[];
@ -304,6 +308,8 @@ if info(1)==0
ide_hess.ide_strength_J_prior=ide_strength_J_prior;
ide_hess.deltaM=deltaM;
ide_hess.deltaM_prior=deltaM_prior;
ide_hess.sensitivity_zero_pos=sensitivity_zero_pos;
ide_hess.identified_parameter_indices=indok;
ide_moments.siJnorm=siJnorm;
ide_model.siHnorm=siHnorm;
ide_lre.siLREnorm=siLREnorm;

29
matlab/plot_identification.m
View File

@ -69,6 +69,19 @@ if SampleSize == 1
else
bar(log([idehess.ide_strength_J(:,is)' ]))
end
hold on
plot((1:length(idehess.ide_strength_J(:,is)))-0.15,log([idehess.ide_strength_J(:,is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.ide_strength_J_prior(:,is)))+0.15,log([idehess.ide_strength_J_prior(:,is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
if any(isinf(log(idehess.ide_strength_J(idehess.identified_parameter_indices))))
inf_indices=find(isinf(log(idehess.ide_strength_J(idehess.identified_parameter_indices))));
inf_pos=ismember(is,inf_indices);
plot(find(inf_pos)-0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
end
if any(isinf(log(idehess.ide_strength_J_prior(idehess.identified_parameter_indices))))
inf_indices=find(isinf(log(idehess.ide_strength_J_prior(idehess.identified_parameter_indices))));
inf_pos=ismember(is,inf_indices);
plot(find(inf_pos)+0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
end
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');
@ -92,7 +105,21 @@ if SampleSize == 1
else
bar(log([idehess.deltaM(is)]))
end
hold on
plot((1:length(idehess.deltaM(is)))-0.15,log([idehess.deltaM(is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
plot((1:length(idehess.deltaM_prior(is)))+0.15,log([idehess.deltaM_prior(is)']),'o','MarkerSize',7,'MarkerFaceColor',[0 0 0],'MarkerEdgeColor','none')
inf_pos=find(isinf(log(idehess.deltaM)));
if ~isempty(inf_pos)
inf_indices=~ismember(inf_pos,idehess.sensitivity_zero_pos);
inf_pos=ismember(is,inf_pos(inf_indices));
plot(find(inf_pos)-0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
end
inf_pos=find(isinf(log(idehess.deltaM_prior)));
if ~isempty(inf_pos)
inf_indices=~ismember(inf_pos,idehess.sensitivity_zero_pos);
inf_pos=ismember(is,inf_pos(inf_indices));
plot(find(inf_pos)+0.15,zeros(sum(inf_pos),1),'o','MarkerSize',7,'MarkerFaceColor',[1 1 1],'MarkerEdgeColor',[0 0 0])
end
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
dy = get(gca,'ylim');