395 lines
14 KiB
Matlab
395 lines
14 KiB
Matlab
function McMCDiagnostics(options_, estim_params_, M_)
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% function McMCDiagnostics
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% Computes convergence tests
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%
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% INPUTS
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% options_ [structure]
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% estim_params_ [structure]
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% M_ [structure]
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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% none
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%
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% PARALLEL CONTEXT
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% See the comment in random_walk_metropolis_hastings.m funtion.
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% Copyright (C) 2005-2011 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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DirectoryName = CheckPath('Output');
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MhDirectoryName = CheckPath('metropolis');
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TeX = options_.TeX;
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nblck = options_.mh_nblck;
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% Brooks and Gelman tests need more than one block
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if nblck == 1
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return;
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end
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npar = estim_params_.nvx;
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npar = npar + estim_params_.nvn;
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npar = npar + estim_params_.ncx;
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npar = npar + estim_params_.ncn;
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npar = npar + estim_params_.np ;
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MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
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load([MhDirectoryName '/' M_.fname '_mh_history.mat'])
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NumberOfMcFilesPerBlock = size(dir([MhDirectoryName ,filesep, M_.fname '_mh*_blck1.mat']),1);
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for blck = 2:nblck
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tmp = size(dir([MhDirectoryName ,filesep, M_.fname '_mh*_blck' int2str(blck) '.mat']),1);
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if tmp~=NumberOfMcFilesPerBlock
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disp(['McMCDiagnostics:: The number of mh files in chain ' int2str(blck) ' is ' int2str(tmp) ' while'])
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disp([' the number of mh files in chain 1 is ' int2str(mcfiles) '!'])
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error('The number of mh files has to be constant across chains!')
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end
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end
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PastDraws = sum(record.MhDraws,1);
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LastFileNumber = PastDraws(2);
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LastLineNumber = record.MhDraws(end,3);
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NumberOfDraws = PastDraws(1);
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Origin = 1000;
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StepSize = ceil((NumberOfDraws-Origin)/100);% So that the computational time does not
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ALPHA = 0.2; % increase too much with the number of simulations.
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time = 1:NumberOfDraws;
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xx = Origin:StepSize:NumberOfDraws;
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NumberOfLines = length(xx);
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tmp = zeros(NumberOfDraws*nblck,3);
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UDIAG = zeros(NumberOfLines,6,npar);
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if NumberOfDraws < Origin
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disp('MCMC Diagnostics :: The number of simulations is to small to compute the MCMC convergence diagnostics.')
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return
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end
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if TeX
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fidTeX = fopen([DirectoryName '/' M_.fname '_UnivariateDiagnostics.TeX'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by McmcDiagnostics.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
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fprintf(fidTeX,' \n');
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end
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disp('MCMC Diagnostics: Univariate convergence diagnostic, Brooks and Gelman (1998):')
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% The mandatory variables for local/remote parallel
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% computing are stored in localVars struct.
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localVars.MhDirectoryName = MhDirectoryName;
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localVars.nblck = nblck;
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localVars.NumberOfMcFilesPerBlock = NumberOfMcFilesPerBlock;
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localVars.Origin = Origin;
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localVars.StepSize = StepSize;
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localVars.mh_drop = options_.mh_drop;
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localVars.NumberOfDraws = NumberOfDraws;
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localVars.NumberOfLines = NumberOfLines;
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localVars.time = time;
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localVars.M_ = M_;
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% Like sequential execution!
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if isnumeric(options_.parallel),
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fout = McMCDiagnostics_core(localVars,1,npar,0);
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UDIAG = fout.UDIAG;
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clear fout
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% Parallel execution!
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else
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ModelName = M_.fname;
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if ~isempty(M_.bvar)
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ModelName = [M_.fname '_bvar'];
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end
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NamFileInput={[M_.dname '/metropolis/'],[ModelName '_mh*_blck*.mat']};
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[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, 1, npar,NamFileInput,'McMCDiagnostics_core', localVars, [], options_.parallel_info);
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UDIAG = fout(1).UDIAG;
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for j=2:totCPU,
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UDIAG = cat(3,UDIAG ,fout(j).UDIAG);
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end
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end
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UDIAG(:,[2 4 6],:) = UDIAG(:,[2 4 6],:)/nblck;
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disp(' ')
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clear pmet temp moyenne CSUP CINF csup cinf n linea iter tmp;
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pages = floor(npar/3);
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k = 0;
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for i = 1:pages
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if options_.nograph
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h = figure('Name','MCMC univariate diagnostic (Brooks and Gelman,1998)','Visible','off');
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else
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h = figure('Name','MCMC univariate diagnostic (Brooks and Gelman,1998)');
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end
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boxplot = 1;
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for j = 1:3 % Loop over parameters
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k = k+1;
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[nam,namtex] = get_the_name(k,TeX,M_,estim_params_,options_);
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for crit = 1:3% Loop over criteria
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if crit == 1
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plt1 = UDIAG(:,1,k);
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plt2 = UDIAG(:,2,k);
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namnam = [nam , ' (Interval)'];
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elseif crit == 2
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plt1 = UDIAG(:,3,k);
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plt2 = UDIAG(:,4,k);
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namnam = [nam , ' (m2)'];
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elseif crit == 3
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plt1 = UDIAG(:,5,k);
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plt2 = UDIAG(:,6,k);
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namnam = [nam , ' (m3)'];
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end
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if TeX
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if j==1
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NAMES = deblank(namnam);
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TEXNAMES = deblank(namtex);
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else
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NAMES = char(NAMES,deblank(namnam));
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TEXNAMES = char(TEXNAMES,deblank(namtex));
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end
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end
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subplot(3,3,boxplot);
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plot(xx,plt1,'-b'); % Pooled
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hold on;
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plot(xx,plt2,'-r'); % Within (mean)
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hold off;
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xlim([xx(1) xx(NumberOfLines)])
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title(namnam,'Interpreter','none')
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boxplot = boxplot + 1;
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end
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end
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eval(['print -depsc2 ' DirectoryName '/' M_.fname '_udiag' int2str(i) '.eps']);
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if ~exist('OCTAVE_VERSION')
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eval(['print -dpdf ' DirectoryName '/' M_.fname '_udiag' int2str(i)]);
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end
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if options_.nograph, set(h,'visible','on'), end
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if ~exist('OCTAVE_VERSION')
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saveas(h,[DirectoryName '/' M_.fname '_udiag' int2str(i) '.fig']);
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end
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if options_.nograph, close(h), end
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if TeX
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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for jj = 1:size(NAMES,1)
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fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
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end
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_udiag%s}\n',M_.fname,int2str(i));
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fprintf(fidTeX,'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n');
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fprintf(fidTeX,'The first, second and third columns are respectively the criteria based on\n');
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fprintf(fidTeX,'the eighty percent interval, the second and third moments.}');
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fprintf(fidTeX,'\\label{Fig:UnivariateDiagnostics:%s}\n',int2str(i));
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fprintf(fidTeX,'\\end{figure}\n');
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fprintf(fidTeX,'\n');
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end
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end
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reste = npar-k;
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if reste
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if reste == 1
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nr = 3;
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nc = 1;
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elseif reste == 2;
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nr = 2;
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nc = 3;
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end
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if options_.nograph
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h = figure('Name','MCMC univariate diagnostic (Brooks and Gelman, 1998)','Visible','off');
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else
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h = figure('Name','MCMC univariate diagnostic (Brooks and Gelman, 1998)');
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end
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boxplot = 1;
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for j = 1:reste
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k = k+1;
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[nam,namtex] = get_the_name(k,TeX,M_,estim_params_,options_);
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for crit = 1:3
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if crit == 1
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plt1 = UDIAG(:,1,k);
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plt2 = UDIAG(:,2,k);
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namnam = [nam , ' (Interval)'];
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elseif crit == 2
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plt1 = UDIAG(:,3,k);
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plt2 = UDIAG(:,4,k);
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namnam = [nam , ' (m2)'];
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elseif crit == 3
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plt1 = UDIAG(:,5,k);
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plt2 = UDIAG(:,6,k);
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namnam = [nam , ' (m3)'];
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end
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if TeX
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if j==1
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NAMES = deblank(namnam);
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TEXNAMES = deblank(namtex);
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else
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NAMES = char(NAMES,deblank(namnam));
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TEXNAMES = char(TEXNAMES,deblank(namtex));
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end
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end
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subplot(nr,nc,boxplot);
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plot(xx,plt1,'-b'); % Pooled
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hold on;
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plot(xx,plt2,'-r'); % Within (mean)
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hold off;
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xlim([xx(1) xx(NumberOfLines)]);
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title(namnam,'Interpreter','none');
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boxplot = boxplot + 1;
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end
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end
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eval(['print -depsc2 ' DirectoryName '/' M_.fname '_udiag' int2str(pages+1) '.eps']);
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if ~exist('OCTAVE_VERSION')
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eval(['print -dpdf ' DirectoryName '/' M_.fname '_udiag' int2str(pages+1)]);
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end
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if options_.nograph, set(h,'visible','on'), end
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if ~exist('OCTAVE_VERSION')
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saveas(h,[DirectoryName '/' M_.fname '_udiag' int2str(pages+1) '.fig']);
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end
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if options_.nograph, close(h), end
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if TeX
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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for jj = 1:size(NAMES,1);
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fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
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end
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_udiag%s}\n',M_.fname,int2str(pages+1));
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if reste == 2
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fprintf(fidTeX,'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n');
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fprintf(fidTeX,'The first, second and third columns are respectively the criteria based on\n');
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fprintf(fidTeX,'the eighty percent interval, the second and third moments.}');
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elseif reste == 1
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fprintf(fidTeX,'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n');
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fprintf(fidTeX,'The first, second and third rows are respectively the criteria based on\n');
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fprintf(fidTeX,'the eighty percent interval, the second and third moments.}');
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end
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fprintf(fidTeX,'\\label{Fig:UnivariateDiagnostics:%s}\n',int2str(pages+1));
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fprintf(fidTeX,'\\end{figure}\n');
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fprintf(fidTeX,'\n');
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fprintf(fidTeX,'% End Of TeX file.');
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fclose(fidTeX);
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end
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end % if reste > 0
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clear UDIAG;
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%%
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%% Multivariate diagnostic.
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%%
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if TeX
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fidTeX = fopen([DirectoryName '/' M_.fname '_MultivariateDiagnostics.TeX'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by McmcDiagnostics.m (Dynare).\n');
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fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
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fprintf(fidTeX,' \n');
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end
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tmp = zeros(NumberOfDraws*nblck,3);
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MDIAG = zeros(NumberOfLines,6);
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for b = 1:nblck
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startline = 0;
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for n = 1:NumberOfMcFilesPerBlock
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%load([MhDirectoryName '/' mcfiles(n,1,b).name],'logpo2');
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load([MhDirectoryName '/' M_.fname '_mh',int2str(n),'_blck' int2str(b) '.mat'],'logpo2');
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nlogpo2 = size(logpo2,1);
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tmp((b-1)*NumberOfDraws+startline+(1:nlogpo2),1) = logpo2;
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startline = startline+nlogpo2;
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end
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% $$$ %load([MhDirectoryName '/' mcfiles(NumberOfMcFilesPerBlock,1,b).name],'logpo2');
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% $$$ load([MhDirectoryName '/' M_.fname '_mh',int2str(NumberOfMcFilesPerBlock),'_blck' int2str(b) '.mat'],'logpo2');
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% $$$ tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+ MAX_nruns*(LastFileNumber-1)+LastLineNumber,1) = logpo2;
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end
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clear logpo2;
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tmp(:,2) = kron(transpose(1:nblck),ones(NumberOfDraws,1));
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tmp(:,3) = kron(ones(nblck,1),time');
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tmp = sortrows(tmp,1);
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ligne = 0;
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for iter = Origin:StepSize:NumberOfDraws
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ligne = ligne+1;
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linea = ceil(options_.mh_drop*iter);
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n = iter-linea+1;
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cinf = round(n*ALPHA/2);
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csup = round(n*(1-ALPHA/2));
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CINF = round(nblck*n*ALPHA/2);
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CSUP = round(nblck*n*(1-ALPHA/2));
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temp = tmp(find((tmp(:,3)>=linea) & (tmp(:,3)<=iter)),1:2);
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MDIAG(ligne,1) = temp(CSUP,1)-temp(CINF,1);
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moyenne = mean(temp(:,1));%% Pooled mean.
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MDIAG(ligne,3) = sum((temp(:,1)-moyenne).^2)/(nblck*n-1);
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MDIAG(ligne,5) = sum(abs(temp(:,1)-moyenne).^3)/(nblck*n-1);
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for i=1:nblck
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pmet = temp(find(temp(:,2)==i));
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MDIAG(ligne,2) = MDIAG(ligne,2) + pmet(csup,1)-pmet(cinf,1);
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moyenne = mean(pmet,1); %% Within mean.
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MDIAG(ligne,4) = MDIAG(ligne,4) + sum((pmet(:,1)-moyenne).^2)/(n-1);
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MDIAG(ligne,6) = MDIAG(ligne,6) + sum(abs(pmet(:,1)-moyenne).^3)/(n-1);
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end
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end
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MDIAG(:,[2 4 6],:) = MDIAG(:,[2 4 6],:)/nblck;
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if options_.nograph
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h = figure('Name','Multivariate diagnostic','Visible','off');
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else
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h = figure('Name','Multivariate diagnostic');
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end
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boxplot = 1;
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for crit = 1:3
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if crit == 1
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plt1 = MDIAG(:,1);
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plt2 = MDIAG(:,2);
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namnam = 'Interval';
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elseif crit == 2
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plt1 = MDIAG(:,3);
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plt2 = MDIAG(:,4);
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namnam = 'm2';
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elseif crit == 3
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plt1 = MDIAG(:,5);
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plt2 = MDIAG(:,6);
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namnam = 'm3';
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end
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if TeX
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if crit == 1
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NAMES = deblank(namnam);
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else
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NAMES = char(NAMES,deblank(namnam));
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end
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end
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subplot(3,1,boxplot);
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plot(xx,plt1,'-b'); % Pooled
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hold on
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plot(xx,plt2,'-r'); % Within (mean)
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hold off
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xlim([xx(1) xx(NumberOfLines)])
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title(namnam,'Interpreter','none');
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boxplot = boxplot + 1;
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end
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eval(['print -depsc2 ' DirectoryName '/' M_.fname '_mdiag.eps']);
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if ~exist('OCTAVE_VERSION')
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eval(['print -dpdf ' DirectoryName '/' M_.fname '_mdiag']);
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end
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if options_.nograph, set(h,'visible','on'), end
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if ~exist('OCTAVE_VERSION')
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saveas(h,[DirectoryName '/' M_.fname '_mdiag.fig']);
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end
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if options_.nograph, close(h), end
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if TeX
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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for jj = 1:3
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fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),' ');
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end
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_mdiag}\n',M_.fname);
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fprintf(fidTeX,'\\caption{Multivariate convergence diagnostics for the Metropolis-Hastings.\n');
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fprintf(fidTeX,'The first, second and third rows are respectively the criteria based on\n');
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fprintf(fidTeX,'the eighty percent interval, the second and third moments. The different \n');
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fprintf(fidTeX,'parameters are aggregated using the posterior kernel.}');
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fprintf(fidTeX,'\\label{Fig:MultivariateDiagnostics}\n');
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fprintf(fidTeX,'\\end{figure}\n');
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fprintf(fidTeX,'\n');
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fprintf(fidTeX,'% End Of TeX file.');
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fclose(fidTeX);
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end |