Merged from 4.2 branch commits:
9747cb78940f41ba3d6c262b2e77a0aa01576d3d 730100282f17974d86510a9ebbaad735f27c46ba * 1) bug fixes: non-initialized ide_strength when no param is identified; normaliz1 set to one when prior std is inf; HIGHEST SVD bar fixed (wrong singluar values were plotted). 2) Simplified advanced output (no more multicollinearity and pair-wise correlations!). 3) Beautified output of collinear groups of variables. 4) use of sensitivities normalized with std errors.time-shift
Marco Ratto
parent
5e230e41b4
commit
d0d04065e6
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@ -78,7 +78,7 @@ options_.Schur_vec_tol = 1.e-8;
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options_ = set_default_option(options_,'datafile',[]);
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options_.mode_compute = 0;
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options_.plot_priors = 0;
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[data,rawdata,xparam1,data_info]=dynare_estimation_init([],fname_,1);
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[data,rawdata,xparam1,data_info]=dynare_estimation_init(M_.endo_names,fname_,1);
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if isempty(data_info),
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data_info.gend = periods;
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data_info.data = [];
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@ -138,6 +138,10 @@ else
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name_tex = [cellstr(M_.exo_names_tex); cellstr(M_.param_names_tex)];
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end
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options_ident = set_default_option(options_ident,'max_ord_bruteforce',min([2,nparam-1]));
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max_ord_bruteforce = options_ident.max_ord_bruteforce;
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MaxNumberOfBytes=options_.MaxNumberOfBytes;
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disp(' ')
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@ -324,12 +328,14 @@ if iload <=0,
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identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
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% identification_checks(H(indH,:),JJ(indJJ,:), gp(indLRE,:), bayestopt_);
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indok = find(max(idemoments.indno{iteration},[],1)==0);
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if iteration ==1 && ~isempty(indok),
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if iteration ==1,
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ide_strength_J=NaN(1,nparam);
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ide_strength_J_prior=NaN(1,nparam);
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if advanced,
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[pars, cosnJ] = ident_bruteforce(JJ(indJJ,:)./normJ(:,ones(nparam,1)),2,1,name_tex);
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end
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end
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if iteration ==1 && advanced,
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[pars, cosnJ] = ident_bruteforce(JJ(indJJ,:)./normJ(:,ones(nparam,1)),max_ord_bruteforce,options_.TeX,name_tex);
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end
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if iteration ==1 && ~isempty(indok),
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normaliz = abs(params);
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if prior_exist,
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if ~isempty(estim_params_.var_exo),
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@ -341,6 +347,8 @@ if iload <=0,
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normaliz1 = [normaliz1; estim_params_.param_vals(:,7)]'; % normalize with prior standard deviation
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end
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% normaliz = max([normaliz; normaliz1]);
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normaliz1(isinf(normaliz1)) = 1;
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else
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normaliz1 = ones(1,nparam);
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end
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@ -425,10 +433,24 @@ if iload <=0,
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if SampleSize > 1,
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close(h)
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close(h),
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normTAU=std(TAU')';
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normLRE=std(LRE')';
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normGAM=std(GAM')';
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normaliz1=std(pdraws);
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iter=0;
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for ifile_index = 1:file_index,
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load([IdentifDirectoryName '/' M_.fname '_identif_' int2str(ifile_index) '.mat'], 'stoH', 'stoJJ', 'stoLRE')
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for irun=1:size(stoH,3),
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iter=iter+1;
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siJnorm(iter,:) = vnorm(stoJJ(:,:,irun)./repmat(normGAM,1,nparam)).*normaliz1;
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siHnorm(iter,:) = vnorm(stoH(:,:,irun)./repmat(normTAU,1,nparam)).*normaliz1;
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siLREnorm(iter,:) = vnorm(stoLRE(:,:,irun)./repmat(normLRE,1,nparam-offset)).*normaliz1(offset+1:end);
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end
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end
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end
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save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'pdraws', 'idemodel', 'idemoments', 'idelre', 'indJJ', 'indH', 'indLRE', ...
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'siHmean', 'siJmean', 'siLREmean', 'derHmean', 'derJmean', 'derLREmean', 'TAU', 'GAM', 'LRE')
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else
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@ -491,44 +513,44 @@ else
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close(h);
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end
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if SampleSize>1,
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siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
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siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
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siLREmean = siLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
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derJmean = derJmean.*(ones(length(indJJ),1)*std(pdraws));
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derHmean = derHmean.*(ones(length(indH),1)*std(pdraws));
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derLREmean = derLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
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derHmean = abs(derHmean./(max(siHmean')'*ones(1,size(pdraws,2))));
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derJmean = abs(derJmean./(max(siJmean')'*ones(1,size(pdraws,2))));
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derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,np)));
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siHmean = siHmean./(max(siHmean')'*ones(1,size(pdraws,2)));
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siJmean = siJmean./(max(siJmean')'*ones(1,size(pdraws,2)));
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siLREmean = siLREmean./(max(siLREmean')'*ones(1,np));
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tstJmean = derJmean*0;
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tstHmean = derHmean*0;
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tstLREmean = derLREmean*0;
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if exist('OCTAVE_VERSION')
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warning('off'),
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else
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warning off,
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end
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for j=1:nparam,
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indd = 1:length(siJmean(:,j));
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tstJmean(indd,j) = abs(derJmean(indd,j))./siJmean(indd,j);
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indd = 1:length(siHmean(:,j));
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tstHmean(indd,j) = abs(derHmean(indd,j))./siHmean(indd,j);
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if j>offset
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indd = 1:length(siLREmean(:,j-offset));
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tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
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end
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end
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end
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% if SampleSize>1,
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% siJmean = siJmean.*(ones(length(indJJ),1)*std(pdraws));
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% siHmean = siHmean.*(ones(length(indH),1)*std(pdraws));
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% siLREmean = siLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
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%
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% derJmean = derJmean.*(ones(length(indJJ),1)*std(pdraws));
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% derHmean = derHmean.*(ones(length(indH),1)*std(pdraws));
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% derLREmean = derLREmean.*(ones(length(indLRE),1)*std(pdraws(:, offset+1:end )));
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%
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% derHmean = abs(derHmean./(max(siHmean')'*ones(1,size(pdraws,2))));
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% derJmean = abs(derJmean./(max(siJmean')'*ones(1,size(pdraws,2))));
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% derLREmean = abs(derLREmean./(max(siLREmean')'*ones(1,np)));
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%
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% siHmean = siHmean./(max(siHmean')'*ones(1,size(pdraws,2)));
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% siJmean = siJmean./(max(siJmean')'*ones(1,size(pdraws,2)));
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% siLREmean = siLREmean./(max(siLREmean')'*ones(1,np));
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%
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% tstJmean = derJmean*0;
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% tstHmean = derHmean*0;
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% tstLREmean = derLREmean*0;
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%
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% if exist('OCTAVE_VERSION')
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% warning('off'),
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% else
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% warning off,
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% end
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%
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% for j=1:nparam,
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% indd = 1:length(siJmean(:,j));
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% tstJmean(indd,j) = abs(derJmean(indd,j))./siJmean(indd,j);
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% indd = 1:length(siHmean(:,j));
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% tstHmean(indd,j) = abs(derHmean(indd,j))./siHmean(indd,j);
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% if j>offset
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% indd = 1:length(siLREmean(:,j-offset));
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% tstLREmean(indd,j-offset) = abs(derLREmean(indd,j-offset))./siLREmean(indd,j-offset);
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% end
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% end
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% end
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if nargout>3 && iload,
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@ -547,141 +569,22 @@ end
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disp_identification(pdraws, idemodel, idemoments, name, advanced)
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if advanced,
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figure('Name','Identification LRE model form'),
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subplot(211)
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if SampleSize > 1,
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mmm = mean(siLREnorm);
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else
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mmm = (siLREnorm);
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end
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[ss, is] = sort(mmm);
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if SampleSize ==1,
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bar(siLREnorm(:,is))
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else
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myboxplot(log(siLREnorm(:,is)))
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end
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% mmm = mean(siLREmean);
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% [ss, is] = sort(mmm);
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% myboxplot(siLREmean(:,is))
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% set(gca,'ylim',[0 1.05])
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set(gca,'xticklabel','')
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for ip=1:np,
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text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Sensitivity in the LRE model')
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subplot(212)
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if SampleSize>1,
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mmm = mean(-idelre.Mco');
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else
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mmm = (-idelre.Mco');
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end
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[ss, is] = sort(mmm);
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if SampleSize ==1,
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bar(idelre.Mco(is,:)')
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else
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myboxplot(idelre.Mco(is,:)')
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end
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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for ip=1:np,
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text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Multicollinearity in the LRE model')
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saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_LRE'])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_LRE']);
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if options_.nograph, close(gcf); end
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figure('Name','Identification in the model'),
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% subplot(311)
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%
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% if SampleSize>1,
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% mmm = mean(ide_strength_H);
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% else
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% mmm = (ide_strength_H);
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% end
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% [ss, is] = sort(mmm);
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% if SampleSize>1,
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% myboxplot(ide_strength_H(:,is))
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% else
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% bar(ide_strength_H(:,is))
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% end
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% % set(gca,'ylim',[0 1.05])
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% set(gca,'xticklabel','')
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% dy = get(gca,'ylim');
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% % dy=dy(2)-dy(1);
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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('Identification strength in the model')
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subplot(211)
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% mmm = mean(siHmean);
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% [ss, is] = sort(mmm);
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% myboxplot(siHmean(:,is))
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if SampleSize>1,
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mmm = mean(siHnorm);
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else
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mmm = (siHnorm);
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end
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[ss, is] = sort(mmm);
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if SampleSize>1,
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myboxplot(log(siHnorm(:,is)))
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else
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bar(siHnorm(:,is))
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end
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% set(gca,'ylim',[0 1.05])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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% dy=dy(2)-dy(1);
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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 in the model')
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subplot(212)
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if SampleSize>1,
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mmm = mean(-idemodel.Mco');
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else
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mmm = (-idemodel.Mco');
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end
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% [ss, is] = sort(mmm);
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if SampleSize>1,
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myboxplot(idemodel.Mco(is,:)')
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else
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bar(idemodel.Mco(is,:)')
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end
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set(gca,'ylim',[0 1])
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set(gca,'xticklabel','')
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for ip=1:nparam,
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text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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end
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title('Multicollinearity in the model')
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saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_model'])
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eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_model']);
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eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_model']);
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if options_.nograph, close(gcf); end
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end
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figure('Name','Identification in the moments'),
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subplot(211)
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% mmm = mean(siHmean);
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% [ss, is] = sort(mmm);
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% myboxplot(siHmean(:,is))
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if SampleSize>1,
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mmm = mean(ide_strength_J);
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else
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% if SampleSize>1,
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% mmm = mean(ide_strength_J);
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% else
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mmm = (ide_strength_J);
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end
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% end
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[ss, is] = sort(mmm);
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if SampleSize>1,
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myboxplot(log(ide_strength_J(:,is)))
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else
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% if SampleSize>1,
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% myboxplot(log(ide_strength_J(:,is)))
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% else
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bar(log([ide_strength_J(:,is)' ide_strength_J_prior(:,is)']))
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end
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% end
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% set(gca,'ylim',[0 1.05])
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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@ -703,11 +606,11 @@ else
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mmm = (siJnorm);
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end
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% [ss, is] = sort(mmm);
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if SampleSize > 1,
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myboxplot(log(siJnorm(:,is)))
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else
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bar(siJnorm(:,is))
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end
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% if SampleSize > 1,
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% myboxplot(log(siJnorm(:,is)))
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% else
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bar(mmm(is))
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% end
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% set(gca,'ylim',[0 1.05])
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set(gca,'xlim',[0 nparam+1])
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set(gca,'xticklabel','')
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@ -718,32 +621,62 @@ for ip=1:nparam,
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end
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title('Sensitivity in the moments')
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% subplot(313)
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% if SampleSize>1,
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% mmm = mean(-idemoments.Mco');
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% else
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% mmm = (-idemoments.Mco');
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% end
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% % [ss, is] = sort(mmm);
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% if SampleSize>1,
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% myboxplot(idemoments.Mco(is,:)')
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% else
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% bar(idemoments.Mco(is,:)')
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% end
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% set(gca,'ylim',[0 1])
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% set(gca,'xticklabel','')
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% for ip=1:nparam,
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% text(ip,-0.02,name{is(ip)},'rotation',90,'HorizontalAlignment','right','interpreter','none')
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% end
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% title('Multicollinearity in the moments')
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% saveas(gcf,[IdentifDirectoryName,'/',M_.fname,'_ident_moments'])
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% eval(['print -depsc2 ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
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% eval(['print -dpdf ' IdentifDirectoryName '/' M_.fname '_ident_moments']);
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% if options_.nograph, close(gcf); end
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if advanced,
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figure('Name','Identification in the model'),
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subplot(211)
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if SampleSize > 1,
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mmm = mean(siLREnorm);
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else
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mmm = (siLREnorm);
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end
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mmm = [NaN(1, offset), mmm];
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% [ss, is] = sort(mmm);
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% if SampleSize ==1,
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bar(mmm(is))
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% else
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% myboxplot(log(siLREnorm(:,is)))
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% end
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% mmm = mean(siLREmean);
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% [ss, is] = sort(mmm);
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% myboxplot(siLREmean(:,is))
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% set(gca,'ylim',[0 1.05])
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set(gca,'xticklabel','')
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% for ip=1:np,
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% text(ip,-0.02,deblank(M_.param_names(indx(is(ip)),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
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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 in the LRE model')
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subplot(212)
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% mmm = mean(siHmean);
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% [ss, is] = sort(mmm);
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% myboxplot(siHmean(:,is))
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if SampleSize>1,
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mmm = mean(siHnorm);
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else
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mmm = (siHnorm);
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end
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% [ss, is] = sort(mmm);
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% if SampleSize>1,
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% myboxplot(log(siHnorm(:,is)))
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% else
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bar(mmm(is))
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% end
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% set(gca,'ylim',[0 1.05])
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set(gca,'xticklabel','')
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dy = get(gca,'ylim');
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% dy=dy(2)-dy(1);
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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')
|
||||
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')
|
||||
|
|
@ -753,7 +686,24 @@ 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);
|
||||
|
|
@ -779,8 +729,8 @@ 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,
|
||||
|
|
@ -803,10 +753,9 @@ 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))
|
||||
|
|
@ -821,73 +770,26 @@ 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
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue