654 lines
24 KiB
Matlab
654 lines
24 KiB
Matlab
function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_)
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% function [rmse_MC, ixx] = filt_mc_(OutDir)
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% inputs (from opt_gsa structure)
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% vvarvecm = options_gsa_.var_rmse;
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% loadSA = options_gsa_.load_rmse;
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% pfilt = options_gsa_.pfilt_rmse;
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% alpha = options_gsa_.alpha_rmse;
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% alpha2 = options_gsa_.alpha2_rmse;
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% istart = options_gsa_.istart_rmse;
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% alphaPC = 0.5;
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%
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% Written by Marco Ratto
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% Joint Research Centre, The European Commission,
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% (http://eemc.jrc.ec.europa.eu/),
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% marco.ratto@jrc.it
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% Reference:
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% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
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% Copyright (C) 2012-2013 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 bayestopt_ estim_params_ M_ options_ oo_
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% options_gsa_=options_.opt_gsa;
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vvarvecm = options_gsa_.var_rmse;
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loadSA = options_gsa_.load_rmse;
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pfilt = options_gsa_.pfilt_rmse;
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alpha = options_gsa_.alpha_rmse;
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% alpha2 = options_gsa_.alpha2_rmse;
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alpha2 = 0;
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pvalue = options_gsa_.alpha2_rmse;
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istart = options_gsa_.istart_rmse;
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alphaPC = 0.5;
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fname_ = M_.fname;
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lgy_ = M_.endo_names;
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dr_ = oo_.dr;
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disp(' ')
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disp(' ')
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disp('Starting sensitivity analysis')
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disp('for the fit of EACH observed series ...')
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disp(' ')
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disp('Deleting old SA figures...')
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a=dir([OutDir,filesep,'*.*']);
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tmp1='0';
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if options_.opt_gsa.ppost,
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tmp=['_rmse_post'];
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else
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if options_.opt_gsa.pprior
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tmp=['_rmse_prior'];
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else
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tmp=['_rmse_mc'];
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end
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if options_gsa_.lik_only,
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tmp1 = [tmp,'_post_SA'];
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tmp = [tmp,'_lik_SA'];
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end
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end
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for j=1:length(a),
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if strmatch([fname_,tmp],a(j).name),
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disp(a(j).name)
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delete([OutDir,filesep,a(j).name])
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end,
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if strmatch([fname_,tmp1],a(j).name),
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disp(a(j).name)
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delete([OutDir,filesep,a(j).name])
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end,
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end
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disp('done !')
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nshock=estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn;
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npar=estim_params_.np;
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if ~isempty(options_.mode_file),
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load(options_.mode_file,'xparam1'),
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end
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if options_.opt_gsa.ppost,
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c=load([fname_,'_mean.mat'],'xparam1');
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xparam1_mean=c.xparam1;
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clear c
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elseif ~isempty(options_.mode_file) && exist([fname_,'_mean.mat'])==2,
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c=load([fname_,'_mean.mat'],'xparam1');
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xparam1_mean=c.xparam1;
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clear c
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end
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if options_.opt_gsa.ppost,
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fnamtmp=[fname_,'_post'];
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DirectoryName = CheckPath('metropolis',M_.dname);
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else
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if options_.opt_gsa.pprior
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fnamtmp=[fname_,'_prior'];
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DirectoryName = CheckPath(['gsa' filesep 'prior'],M_.dname);
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else
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fnamtmp=[fname_,'_mc'];
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DirectoryName = CheckPath(['gsa' filesep 'mc'],M_.dname);
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end
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end
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if ~loadSA,
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if exist('xparam1','var')
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M_ = set_all_parameters(xparam1,estim_params_,M_);
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ys_mode=steady_(M_,options_,oo_);
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end
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if exist('xparam1_mean','var')
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M_ = set_all_parameters(xparam1_mean,estim_params_,M_);
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ys_mean=steady_(M_,options_,oo_);
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end
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% eval(options_.datafile)
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Y = dataset_.data;
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gend = dataset_.info.ntobs;
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data_index = dataset_.missing.aindex;
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missing_value = dataset_.missing.state;
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for jx=1:gend, data_indx(jx,data_index{jx})=true; end
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%stock_gend=data_info.gend;
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%stock_data = data_info.data;
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load([DirectoryName filesep M_.fname '_data.mat']);
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filfilt = dir([DirectoryName filesep M_.fname '_filter_step_ahead*.mat']);
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filsmooth = dir([DirectoryName filesep M_.fname '_smooth*.mat']);
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filupdate = dir([DirectoryName filesep M_.fname '_update*.mat']);
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filparam = dir([DirectoryName filesep M_.fname '_param*.mat']);
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x=[];
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logpo2=[];
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sto_ys=[];
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for j=1:length(filparam),
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%load([DirectoryName filesep M_.fname '_param',int2str(j),'.mat']);
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if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name))
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load([DirectoryName filesep filparam(j).name]);
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x=[x; stock];
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logpo2=[logpo2; stock_logpo];
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sto_ys=[sto_ys; stock_ys];
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clear stock stock_logpo stock_ys;
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end
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end
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nruns=size(x,1);
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nfilt=floor(pfilt*nruns);
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if options_.opt_gsa.ppost || (options_.opt_gsa.ppost==0 && options_.opt_gsa.lik_only==0)
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disp(' ')
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disp('Computing RMSE''s...')
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fobs = options_.first_obs;
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nobs=options_.nobs;
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for i=1:size(vvarvecm,1),
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vj=deblank(vvarvecm(i,:));
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jxj(i) = strmatch(vj,lgy_(dr_.order_var,:),'exact');
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js(i) = strmatch(vj,lgy_,'exact');
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yss(i,:,:)=repmat(sto_ys(:,js(i))',[nobs,1]);
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end
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if exist('xparam1','var')
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[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value);
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y0 = transpose( squeeze(aK(1,jxj,1:gend)));% + kron(ys_mode(js),ones(1,gend)));
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yobs = transpose( ahat(jxj,:));% + kron(ys_mode(js),ones(1,gend)));
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rmse_mode = sqrt(mean((yobs(istart:end,:)-y0(istart:end,:)).^2));
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r2_mode = 1-sum((yobs(istart:end,:)-y0(istart:end,:)).^2)./sum(yobs(istart:end,:).^2);
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end
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y0=yss*0;
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nbb=0;
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for j=1:length(filfilt),
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load([DirectoryName filesep M_.fname '_filter_step_ahead',num2str(j),'.mat']);
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nb = size(stock,4);
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y0(:,:,nbb+1:nbb+nb)=y0(:,:,nbb+1:nbb+nb)+squeeze(stock(1,js,1:nobs,:));
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nbb=nbb+nb;
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clear stock;
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end
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yobs=-yss;
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nbb=0;
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for j=1:length(filupdate),
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load([DirectoryName filesep M_.fname '_update',num2str(j),'.mat']);
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nb = size(stock,3);
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yobs(:,:,nbb+1:nbb+nb)=yobs(:,:,nbb+1:nbb+nb)+squeeze(stock(js,1:nobs,:));
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nbb=nbb+nb;
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clear stock;
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end
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y0M=mean(y0,2);
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rmse_MC=zeros(nruns,length(js));
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r2_MC=zeros(nruns,length(js));
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for j=1:nruns,
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rmse_MC(j,:) = sqrt(mean((yobs(:,istart:end,j)'-y0(:,istart:end,j)').^2));
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r2_MC(j,:) = 1-mean((yobs(:,istart:end,j)'-y0(:,istart:end,j)').^2)./mean((yobs(:,istart:end,j)').^2);
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end
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if exist('xparam1_mean','var')
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%eval(['rmse_pmean(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0M(istart:end-1)).^2));'])
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[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1_mean,gend,Y,data_index,missing_value);
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y0 = transpose( squeeze(aK(1,jxj,1:gend)));% + kron(ys_mean(js),ones(1,gend)));
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yobs = transpose( ahat(jxj,:));% + kron(ys_mean(js),ones(1,gend)));
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rmse_pmean = sqrt(mean((yobs(istart:end,:)-y0(istart:end,:)).^2));
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r2_pmean = 1-mean((yobs(istart:end,:)-y0(istart:end,:)).^2)./mean(yobs(istart:end,:).^2);
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end
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clear stock_filter;
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end
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for j=1:nruns,
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lnprior(j,1) = priordens(x(j,:)',bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
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end
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likelihood=logpo2(:)-lnprior(:);
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disp('... done!')
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if options_.opt_gsa.ppost
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save([OutDir,filesep,fnamtmp,'.mat'], 'x', 'logpo2', 'likelihood', 'rmse_MC', 'rmse_mode','rmse_pmean', 'r2_MC', 'r2_mode','r2_pmean')
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else
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if options_.opt_gsa.lik_only
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save([OutDir,filesep,fnamtmp, '.mat'], 'x', 'logpo2','likelihood', '-append')
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else
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save([OutDir,filesep,fnamtmp, '.mat'], 'x', 'logpo2','likelihood', 'rmse_MC', 'r2_MC','-append')
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if exist('xparam1_mean','var')
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save([OutDir,filesep,fnamtmp, '.mat'], 'rmse_pmean', 'r2_pmean','-append')
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end
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if exist('xparam1','var')
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save([OutDir,filesep,fnamtmp,'.mat'], 'rmse_mode', 'r2_mode','-append')
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end
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end
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end
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else
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if options_.opt_gsa.lik_only && options_.opt_gsa.ppost==0
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load([OutDir,filesep,fnamtmp, '.mat'],'x','logpo2','likelihood');
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else
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load([OutDir,filesep,fnamtmp, '.mat'],'x','logpo2','likelihood','rmse_MC','rmse_mode','rmse_pmean', 'r2_MC', 'r2_mode','r2_pmean');
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end
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lnprior=logpo2(:)-likelihood(:);
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nruns=size(x,1);
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nfilt=floor(pfilt*nruns);
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end
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% smirnov tests
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nfilt0=nfilt*ones(size(vvarvecm,1),1);
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logpo2=logpo2(:);
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if ~options_.opt_gsa.ppost
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[dum, ipost]=sort(-logpo2);
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[dum, ilik]=sort(-likelihood);
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end
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if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
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if options_.opt_gsa.pprior
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anam='rmse_prior_post';
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else
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anam='rmse_mc_post';
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end
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stab_map_1(x, ipost(1:nfilt), ipost(nfilt+1:end), anam, 1,[],OutDir);
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stab_map_2(x(ipost(1:nfilt),:),alpha2,pvalue,anam, OutDir);
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if options_.opt_gsa.pprior
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anam='rmse_prior_lik';
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else
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anam='rmse_mc_lik';
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end
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stab_map_1(x, ilik(1:nfilt), ilik(nfilt+1:end), anam, 1,[],OutDir);
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stab_map_2(x(ilik(1:nfilt),:),alpha2,pvalue,anam, OutDir);
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else
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for i=1:size(vvarvecm,1),
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[dum, ixx(:,i)]=sort(rmse_MC(:,i));
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if options_.opt_gsa.ppost,
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%nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
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rmse_txt=rmse_pmean;
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r2_txt=r2_pmean;
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else
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if options_.opt_gsa.pprior || ~exist('rmse_pmean'),
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if exist('rmse_mode'),
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rmse_txt=rmse_mode;
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r2_txt=r2_mode;
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else
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rmse_txt=NaN(1,size(rmse_MC,2));
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r2_txt=NaN(1,size(r2_MC,2));
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end
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else
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%nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
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rmse_txt=rmse_pmean;
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r2_txt=r2_pmean;
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end
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end
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for j=1:npar+nshock,
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[H,P,KSSTAT] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j), alpha);
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[H1,P1,KSSTAT1] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,1);
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[H2,P2,KSSTAT2] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,-1);
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if H1 & H2==0,
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SS(j,i)=1;
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elseif H1==0,
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SS(j,i)=-1;
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else
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SS(j,i)=0;
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end
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PP(j,i)=P;
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end
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end
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if ~options_.nograph,
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ifig=0;
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for i=1:size(vvarvecm,1),
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if options_.opt_gsa.ppost
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temp_name='RMSE Posterior: Log Prior';
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else
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if options_.opt_gsa.pprior
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temp_name='RMSE Prior: Log Prior';
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else
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temp_name='RMSE MC: Log Prior';
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end
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end
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if mod(i,9)==1,
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ifig=ifig+1;
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hh=dyn_figure(options_,'name',[temp_name,' ',int2str(ifig)]);
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end
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subplot(3,3,i-9*(ifig-1))
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h=cumplot(lnprior(ixx(1:nfilt0(i),i)));
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set(h,'color','red')
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hold on, cumplot(lnprior)
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h=cumplot(lnprior(ixx(nfilt0(i)+1:end,i)));
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set(h,'color','green')
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title(vvarvecm(i,:),'interpreter','none')
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if mod(i,9)==0 || i==size(vvarvecm,1)
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if options_.opt_gsa.ppost
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnprior',int2str(ifig)],options_);
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else
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if options_.opt_gsa.pprior
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_lnprior',int2str(ifig) ],options_);
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else
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_lnprior',int2str(ifig) ],options_);
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end
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end
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end
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end
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ifig=0;
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for i=1:size(vvarvecm,1),
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if options_.opt_gsa.ppost
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temp_name='RMSE Posterior: Log Likelihood';
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else
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if options_.opt_gsa.pprior
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temp_name='RMSE Prior: Log Likelihood';
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else
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temp_name='RMSE MC: Log Likelihood';
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end
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end
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if mod(i,9)==1,
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ifig=ifig+1;
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hh = dyn_figure(options_,'Name',[temp_name,' ',int2str(ifig)]);
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end
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subplot(3,3,i-9*(ifig-1))
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h=cumplot(likelihood(ixx(1:nfilt0(i),i)));
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set(h,'color','red')
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hold on, h=cumplot(likelihood);
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h=cumplot(likelihood(ixx(nfilt0(i)+1:end,i)));
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set(h,'color','green')
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title(vvarvecm(i,:),'interpreter','none')
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if options_.opt_gsa.ppost==0,
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set(gca,'xlim',[min( likelihood(ixx(1:nfilt0(i),i)) ) max( likelihood(ixx(1:nfilt0(i),i)) )])
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end
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if mod(i,9)==0 || i==size(vvarvecm,1)
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if options_.opt_gsa.ppost
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnlik',int2str(ifig) ],options_);
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else
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if options_.opt_gsa.pprior
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_lnlik',int2str(ifig)],options_);
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else
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_lnlik',int2str(ifig) ],options_);
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end
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end
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end
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end
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ifig=0;
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for i=1:size(vvarvecm,1),
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if options_.opt_gsa.ppost
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temp_name='RMSE Posterior: Log Posterior';
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else
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if options_.opt_gsa.pprior
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temp_name='RMSE Prior: Log Posterior';
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else
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temp_name='RMSE MC: Log Posterior';
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end
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end
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if mod(i,9)==1,
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ifig=ifig+1;
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hh = dyn_figure(options_,'Name',[temp_name,' ',int2str(ifig)]);
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end
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subplot(3,3,i-9*(ifig-1))
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h=cumplot(logpo2(ixx(1:nfilt0(i),i)));
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set(h,'color','red')
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hold on, h=cumplot(logpo2);
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h=cumplot(logpo2(ixx(nfilt0(i)+1:end,i)));
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set(h,'color','green')
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title(vvarvecm(i,:),'interpreter','none')
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if options_.opt_gsa.ppost==0,
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set(gca,'xlim',[min( logpo2(ixx(1:nfilt0(i),i)) ) max( logpo2(ixx(1:nfilt0(i),i)) )])
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end
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if mod(i,9)==0 || i==size(vvarvecm,1)
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if options_.opt_gsa.ppost
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnpost',int2str(ifig) ],options_);
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else
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if options_.opt_gsa.pprior
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_lnpost',int2str(ifig)],options_);
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else
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dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_lnpost',int2str(ifig)],options_);
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end
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end
|
|
end
|
|
end
|
|
end
|
|
|
|
param_names='';
|
|
for j=1:npar+nshock,
|
|
param_names=char(param_names, bayestopt_.name{j});
|
|
end
|
|
param_names=param_names(2:end,:);
|
|
|
|
disp(' ')
|
|
disp('RMSE over the MC sample:')
|
|
disp(' min yr RMSE max yr RMSE')
|
|
for j=1:size(vvarvecm,1),
|
|
disp([vvarvecm(j,:), sprintf('%15.5g',[(min(rmse_MC(:,j))) [(max(rmse_MC(:,j)))]])])
|
|
end
|
|
invar = find( std(rmse_MC)./mean(rmse_MC)<=0.0001 );
|
|
if ~isempty(invar)
|
|
disp(' ')
|
|
disp(' ')
|
|
disp('RMSE is not varying significantly over the MC sample for the following variables:')
|
|
disp(vvarvecm(invar,:))
|
|
disp('These variables are excluded from SA')
|
|
disp('[Unless you treat these series as exogenous, there is something wrong in your estimation !]')
|
|
end
|
|
ivar = find( std(rmse_MC)./mean(rmse_MC)>0.0001 );
|
|
vvarvecm=vvarvecm(ivar,:);
|
|
rmse_MC=rmse_MC(:,ivar);
|
|
|
|
disp(' ')
|
|
% if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior,
|
|
disp(['Sample filtered the ',num2str(pfilt*100),'% best RMSE''s for each observed series ...' ])
|
|
% else
|
|
% disp(['Sample filtered the best RMSE''s smaller than RMSE at the posterior mean ...' ])
|
|
% end
|
|
% figure, boxplot(rmse_MC)
|
|
% set(gca,'xticklabel',vvarvecm)
|
|
% saveas(gcf,[fname_,'_SA_RMSE'])
|
|
|
|
disp(' ')
|
|
disp(' ')
|
|
disp('RMSE ranges after filtering:')
|
|
if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior,
|
|
disp([' best ',num2str(pfilt*100),'% filtered remaining 90%'])
|
|
disp([' min max min max posterior mode'])
|
|
else
|
|
disp([' best filtered remaining '])
|
|
disp([' min max min max posterior mean'])
|
|
end
|
|
for j=1:size(vvarvecm,1),
|
|
disp([vvarvecm(j,:), sprintf('%15.5g',[min(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
|
|
max(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
|
|
min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
|
|
max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
|
|
rmse_txt(j)])])
|
|
% disp([vvarvecm(j,:), sprintf('%15.5g',[min(logpo2(ixx(1:nfilt,j))) ...
|
|
% max(logpo2(ixx(1:nfilt,j))) ...
|
|
% min(logpo2(ixx(nfilt+1:end,j))) ...
|
|
% max(logpo2(ixx(nfilt+1:end,j)))])])
|
|
end
|
|
|
|
%%%%% R2 table
|
|
|
|
disp(' ')
|
|
disp('R2 over the MC sample:')
|
|
disp(' min yr R2 max yr R2')
|
|
for j=1:size(vvarvecm,1),
|
|
disp([vvarvecm(j,:), sprintf('%15.5g',[(min(r2_MC(:,j))) [(max(r2_MC(:,j)))]])])
|
|
end
|
|
r2_MC=r2_MC(:,ivar);
|
|
|
|
disp(' ')
|
|
disp(['Sample filtered the ',num2str(pfilt*100),'% best R2''s for each observed series ...' ])
|
|
|
|
disp(' ')
|
|
disp(' ')
|
|
disp('R2 ranges after filtering:')
|
|
if options_.opt_gsa.ppost==0 && options_.opt_gsa.pprior,
|
|
disp([' best ',num2str(pfilt*100),'% filtered remaining 90%'])
|
|
disp([' min max min max posterior mode'])
|
|
else
|
|
disp([' best filtered remaining '])
|
|
disp([' min max min max posterior mean'])
|
|
end
|
|
for j=1:size(vvarvecm,1),
|
|
disp([vvarvecm(j,:), sprintf('%15.5g',[min(r2_MC(ixx(1:nfilt0(j),j),j)) ...
|
|
max(r2_MC(ixx(1:nfilt0(j),j),j)) ...
|
|
min(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
|
|
max(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
|
|
r2_txt(j)])])
|
|
end
|
|
|
|
%%%% R2 table
|
|
SP=zeros(npar+nshock,size(vvarvecm,1));
|
|
for j=1:size(vvarvecm,1),
|
|
ns=find(PP(:,j)<alpha);
|
|
SP(ns,j)=ones(size(ns));
|
|
SS(:,j)=SS(:,j).*SP(:,j);
|
|
end
|
|
|
|
for j=1:npar+nshock, %estim_params_.np,
|
|
nsp(j)=length(find(SP(j,:)));
|
|
end
|
|
snam0=param_names(find(nsp==0),:);
|
|
snam1=param_names(find(nsp==1),:);
|
|
snam2=param_names(find(nsp>1),:);
|
|
snam=param_names(find(nsp>0),:);
|
|
% snam0=bayestopt_.name(find(nsp==0));
|
|
% snam1=bayestopt_.name(find(nsp==1));
|
|
% snam2=bayestopt_.name(find(nsp>1));
|
|
% snam=bayestopt_.name(find(nsp>0));
|
|
nsnam=(find(nsp>1));
|
|
|
|
disp(' ')
|
|
disp(' ')
|
|
disp('These parameters do not affect significantly the fit of ANY observed series:')
|
|
disp(snam0)
|
|
disp(' ')
|
|
disp('These parameters affect ONE single observed series:')
|
|
disp(snam1)
|
|
disp(' ')
|
|
disp('These parameters affect MORE THAN ONE observed series: trade off exists!')
|
|
disp(snam2)
|
|
|
|
|
|
%pnam=bayestopt_.name(end-estim_params_.np+1:end);
|
|
pnam=bayestopt_.name;
|
|
|
|
% plot trade-offs
|
|
if ~options_.nograph
|
|
a00=jet(size(vvarvecm,1));
|
|
for ix=1:ceil(length(nsnam)/5),
|
|
if options_.opt_gsa.ppost
|
|
temp_name='RMSE Posterior Tradeoffs: Log Posterior';
|
|
else
|
|
if options_.opt_gsa.pprior
|
|
temp_name='RMSE Prior Tradeoffs: Log Posterior';
|
|
else
|
|
temp_name='RMSE MC Tradeoffs: Log Posterior';
|
|
end
|
|
end
|
|
hh = dyn_figure(options_,'name',[temp_name,' ',int2str(ix)]);
|
|
for j=1+5*(ix-1):min(size(snam2,1),5*ix),
|
|
subplot(2,3,j-5*(ix-1))
|
|
%h0=cumplot(x(:,nsnam(j)+nshock));
|
|
h0=cumplot(x(:,nsnam(j)));
|
|
set(h0,'color',[0 0 0])
|
|
hold on,
|
|
npx=find(SP(nsnam(j),:)==0);
|
|
%a0=jet(nsp(nsnam(j)));
|
|
% a0=a00(np,:);
|
|
for i=1:size(vvarvecm,1),
|
|
%h0=cumplot(x(ixx(1:nfilt,np(i)),nsnam(j)+nshock));
|
|
% h0=cumplot(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)));
|
|
if any(npx==i),
|
|
h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j))*NaN);
|
|
else
|
|
h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j)));
|
|
end
|
|
set(h0,'color',a00(i,:))
|
|
end
|
|
ydum=get(gca,'ylim');
|
|
%xdum=xparam1(nshock+nsnam(j));
|
|
if exist('xparam1')
|
|
xdum=xparam1(nsnam(j));
|
|
h1=plot([xdum xdum],ydum);
|
|
set(h1,'color',[0.85 0.85 0.85],'linewidth',2)
|
|
end
|
|
xlabel('')
|
|
title([pnam{nsnam(j)}],'interpreter','none')
|
|
end
|
|
%subplot(3,2,6)
|
|
if exist('OCTAVE_VERSION'),
|
|
legend(char('base',vvarvecm),'location','eastoutside');
|
|
else
|
|
h0=legend(char('base',vvarvecm),0);
|
|
set(h0,'fontsize',6,'position',[0.7 0.1 0.2 0.3],'interpreter','none');
|
|
end
|
|
%h0=legend({'base',vnam{np}}',0);
|
|
%set(findobj(get(h0,'children'),'type','text'),'interpreter','none')
|
|
if options_.opt_gsa.ppost
|
|
dyn_saveas(hh,[ OutDir filesep fname_ '_rmse_post_' int2str(ix)],options_);
|
|
else
|
|
if options_.opt_gsa.pprior
|
|
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_' int2str(ix) ],options_);
|
|
else
|
|
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_' int2str(ix)],options_);
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
for j=1:size(SP,2),
|
|
nsx(j)=length(find(SP(:,j)));
|
|
end
|
|
|
|
disp(' ')
|
|
disp(' ')
|
|
disp('Sensitivity table (significance and direction):')
|
|
vav=char(zeros(1, size(param_names,2)+3 ));
|
|
ibl = 12-size(vvarvecm,2);
|
|
for j=1:size(vvarvecm,1),
|
|
vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
|
|
end
|
|
disp(vav)
|
|
for j=1:npar+nshock, %estim_params_.np,
|
|
%disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])
|
|
disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))])
|
|
disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))])
|
|
end
|
|
|
|
|
|
disp(' ')
|
|
disp(' ')
|
|
disp('Starting bivariate analysis:')
|
|
|
|
for i=1:size(vvarvecm,1)
|
|
if options_.opt_gsa.ppost
|
|
fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
|
|
else
|
|
if options_.opt_gsa.pprior
|
|
fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
|
|
else
|
|
fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
|
|
end
|
|
end
|
|
stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir);
|
|
|
|
% [pc,latent,explained] = pcacov(c0);
|
|
% %figure, bar([explained cumsum(explained)])
|
|
% ifig=0;
|
|
% j2=0;
|
|
% for j=1:npar+nshock,
|
|
% i2=find(abs(pc(:,j))>alphaPC);
|
|
% if ~isempty(i2),
|
|
% j2=j2+1;
|
|
% if mod(j2,12)==1,
|
|
% ifig=ifig+1;
|
|
% figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
|
|
% end
|
|
% subplot(3,4,j2-(ifig-1)*12)
|
|
% bar(pc(i2,j)),
|
|
% set(gca,'xticklabel',bayestopt_.name(i2)),
|
|
% set(gca,'xtick',[1:length(i2)])
|
|
% title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
|
|
% end
|
|
% if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
|
|
% saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
|
|
% end
|
|
% end
|
|
% close all
|
|
end
|
|
|
|
end |