498 lines
22 KiB
Matlab
498 lines
22 KiB
Matlab
function oo_ = mcmc_diagnostics(options_, estim_params_, M_, oo_)
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% function oo_ = mcmc_diagnostics(options_, estim_params_, M_, oo_)
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% Computes convergence tests
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%
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% INPUTS
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% options_ [structure] Dynare options structure
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% estim_params_ [structure] Dynare estimation parameter structure
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% M_ [structure] Dynare model structure
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% oo_ [structure] Dynare results structure
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%
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% OUTPUTS
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% oo_ [structure]
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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 posterior_sampler.m funtion.
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% Copyright © 2005-2023 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 <https://www.gnu.org/licenses/>.
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graphFolder = CheckPath('graphs',M_.dname);
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latexFolder = CheckPath('latex',M_.dname);
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MetropolisFolder = CheckPath('metropolis',M_.dname);
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ModelName = M_.fname;
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TeX = options_.TeX;
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record=load_last_mh_history_file(MetropolisFolder, ModelName);
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NumberOfMcFilesPerBlock = record.LastFileNumber;
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[nblck, npar] = size(record.LastParameters);
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npardisp = options_.convergence.brooksgelman.plotrows;
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% check if all the mh files are available.
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issue_an_error_message = 0;
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for b = 1:nblck
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nfiles = length(dir([MetropolisFolder ,filesep, ModelName '_mh*_blck' num2str(b) '.mat']));
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if ~isequal(NumberOfMcFilesPerBlock,nfiles)
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issue_an_error_message = 1;
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fprintf('The number of MCMC files in chain %u is %u while the mh-history files indicate that we should have %u MCMC files per chain!\n',b, nfiles, NumberOfMcFilesPerBlock);
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end
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end
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if issue_an_error_message
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error('mcmc_diagnostics: I cannot proceed because some MCMC files are missing. Check your MCMC files...!');
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end
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% compute inefficiency factor
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FirstLine = record.KeepedDraws.FirstLine;
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TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
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TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
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FirstMhFile = record.KeepedDraws.FirstMhFile;
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NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
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param_name = {};
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param_name_tex = {};
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Ifac=NaN(nblck,npar);
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for jj = 1:npar
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if options_.TeX
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[par_name_temp, par_name_tex_temp] = get_the_name(jj, options_.TeX, M_,estim_params_, options_.varobs);
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param_name = vertcat(param_name, par_name_temp);
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par_name_tex_temp = strrep(par_name_tex_temp,'$','');
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param_name_tex = vertcat(param_name_tex, par_name_tex_temp);
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else
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par_name_temp = get_the_name(jj, options_.TeX, M_, estim_params_, options_.varobs);
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param_name = vertcat(param_name, par_name_temp);
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end
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Draws = GetAllPosteriorDraws(options_, M_.dname, M_.fname, jj, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
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Draws = reshape(Draws, [NumberOfDraws nblck]);
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Nc = min(1000, NumberOfDraws/2);
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for ll = 1:nblck
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Ifac(ll,jj) = mcmc_ifac(Draws(:,ll), Nc);
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end
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tmp = num2cell(Ifac(:,jj));
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end
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my_title='MCMC Inefficiency factors per block';
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IFAC_header = {'Parameter'};
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IFAC_header_tex = {'Parameter'};
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for j = 1:nblck
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IFAC_header = vertcat(IFAC_header, ['Block ' int2str(j)]);
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IFAC_header_tex = vertcat(IFAC_header_tex, ['Block~' int2str(j)]);
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end
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lh = cellofchararraymaxlength(param_name)+2;
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dyntable(options_, my_title, IFAC_header, param_name, Ifac', lh, 12, 3);
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skipline()
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if options_.TeX
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dyn_latex_table(M_, options_, my_title, 'MCMC_inefficiency_factors', IFAC_header_tex, param_name_tex, Ifac', lh, 12, 3);
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end
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record.InefficiencyFactorsPerBlock = Ifac;
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update_last_mh_history_file(MetropolisFolder, ModelName, record);
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PastDraws = sum(record.MhDraws,1);
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NumberOfDraws = PastDraws(1);
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if ~strcmp(options_.posterior_sampler_options.posterior_sampling_method,'slice') && NumberOfDraws<=2000
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warning('MCMC convergence diagnostics are not computed because the total number of iterations is not bigger than 2000!');
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return
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end
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convergence_diagnostics_geweke=zeros(npar,4+2*length(options_.convergence.geweke.taper_steps));
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if any(options_.convergence.geweke.geweke_interval<0) || any(options_.convergence.geweke.geweke_interval>1) || length(options_.convergence.geweke.geweke_interval)~=2 ...
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|| (options_.convergence.geweke.geweke_interval(2)-options_.convergence.geweke.geweke_interval(1)<0)
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fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for geweke_interval. Using the default of [0.2 0.5].\n')
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options_.convergence.geweke.geweke_interval=[0.2 0.5];
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end
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first_obs_begin_sample = max(1,ceil(options_.mh_drop*NumberOfDraws));
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last_obs_begin_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(1)*NumberOfDraws*(1-options_.mh_drop));
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first_obs_end_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(2)*NumberOfDraws*(1-options_.mh_drop));
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param_name = {};
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if options_.TeX
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param_name_tex = {};
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end
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for jj=1:npar
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if options_.TeX
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[param_name_temp, param_name_tex_temp] = get_the_name(jj, options_.TeX, M_, estim_params_, options_.varobs);
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param_name_tex = vertcat(param_name_tex, strrep(param_name_tex_temp, '$',''));
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param_name = vertcat(param_name, param_name_temp);
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else
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param_name_temp = get_the_name(jj, options_.TeX, M_,estim_params_, options_.varobs);
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param_name = vertcat(param_name, param_name_temp);
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end
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end
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datamat=NaN(npar,3+length(options_.convergence.geweke.taper_steps),nblck);
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%remove stale results as it will cause assignment problems
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if options_.convergence.rafterylewis.indicator && isfield(oo_,'convergence') && isfield(oo_.convergence,'raftery_lewis')
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oo_.convergence=rmfield(oo_.convergence,'raftery_lewis');
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end
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for block_iter=1:nblck
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fprintf('\n\nConvergence diagnostics results for chain %u.\n',block_iter);
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fprintf('\nGeweke (1992) Convergence Tests, based on means of draws %d to %d vs %d to %d for chain %u.\n',first_obs_begin_sample,last_obs_begin_sample,first_obs_end_sample,NumberOfDraws,block_iter);
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fprintf('p-values are for Chi2-test for equality of means.\n');
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Geweke_header = {'Parameter'; 'Post. Mean'; 'Post. Std'; 'p-val No Taper'};
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for ii = 1:length(options_.convergence.geweke.taper_steps)
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Geweke_header = vertcat(Geweke_header, ['p-val ' num2str(options_.convergence.geweke.taper_steps(ii)),'% Taper']);
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end
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for jj=1:npar
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startline=0;
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for n = 1:NumberOfMcFilesPerBlock
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load([MetropolisFolder '/' ModelName '_mh',int2str(n),'_blck',num2str(block_iter),'.mat'],'x2');
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nx2 = size(x2,1);
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param_draws(startline+(1:nx2),1) = x2(:,jj);
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startline = startline + nx2;
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end
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[results_vec, results_struct] = geweke_moments(param_draws,options_);
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convergence_diagnostics_geweke(jj,:)=results_vec;
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param_draws1 = param_draws(first_obs_begin_sample:last_obs_begin_sample,:);
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param_draws2 = param_draws(first_obs_end_sample:end,:);
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[results_vec1] = geweke_moments(param_draws1,options_);
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[results_vec2] = geweke_moments(param_draws2,options_);
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results_struct = geweke_chi2_test(results_vec1,results_vec2,results_struct,options_);
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oo_.convergence.geweke(block_iter).(param_name{jj}) = results_struct;
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datamat(jj,:,block_iter)=[results_struct.posteriormean,results_struct.posteriorstd,results_struct.prob_chi2_test];
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end
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lh = size(param_name,2)+2;
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dyntable(options_, '', Geweke_header, param_name, datamat(:,:,block_iter), lh, 12, 3);
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if options_.TeX
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Geweke_tex_header = {'Parameter'; 'Mean'; 'Std'; 'No\ Taper'};
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additional_header = {[' & \multicolumn{2}{c}{Posterior} & \multicolumn{',num2str(1+length(options_.convergence.geweke.taper_steps)),'}{c}{p-values} \\'],
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['\cmidrule(r{.75em}){2-3} \cmidrule(r{.75em}){4-',num2str(4+length(options_.convergence.geweke.taper_steps)),'}']};
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for ii=1:length(options_.convergence.geweke.taper_steps)
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Geweke_tex_header = vertcat(Geweke_tex_header, [num2str(options_.convergence.geweke.taper_steps(ii)),'\%%\ Taper']);
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end
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headers = Geweke_tex_header;
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lh = cellofchararraymaxlength(param_name_tex)+2;
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my_title=sprintf('Geweke (1992) Convergence Tests, based on means of draws %d to %d vs %d to %d for chain %u. p-values are for $\\\\chi^2$-test for equality of means.',first_obs_begin_sample,last_obs_begin_sample,first_obs_end_sample,NumberOfDraws,block_iter);
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dyn_latex_table(M_, options_, my_title, ['geweke_block_' num2str(block_iter)], headers, param_name_tex, datamat(:,:,block_iter), lh, 12, 4, additional_header);
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end
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skipline(2);
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if options_.convergence.rafterylewis.indicator
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if any(options_.convergence.rafterylewis.qrs<0) || any(options_.convergence.rafterylewis.qrs>1) || length(options_.convergence.rafterylewis.qrs)~=3 ...
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|| (options_.convergence.rafterylewis.qrs(1)-options_.convergence.rafterylewis.qrs(2)<=0)
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fprintf('\nInvalid option for raftery_lewis_qrs. Using the default of [0.025 0.005 0.95].\n');
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options_.convergence.rafterylewis.qrs=[0.025 0.005 0.95];
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end
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Raftery_Lewis_q=options_.convergence.rafterylewis.qrs(1);
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Raftery_Lewis_r=options_.convergence.rafterylewis.qrs(2);
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Raftery_Lewis_s=options_.convergence.rafterylewis.qrs(3);
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oo_.convergence.raftery_lewis(block_iter) = raftery_lewis(x2,Raftery_Lewis_q,Raftery_Lewis_r,Raftery_Lewis_s);
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my_title=sprintf('Raftery/Lewis (1992) Convergence Diagnostics, based on quantile q=%4.3f with precision r=%4.3f with probability s=%4.3f for chain %u.',Raftery_Lewis_q,Raftery_Lewis_r,Raftery_Lewis_s,block_iter);
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headers = {'Variables'; 'M (burn-in)'; 'N (req. draws)'; 'N+M (total draws)'; 'k (thinning)'};
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raftery_data_mat=[oo_.convergence.raftery_lewis(block_iter).M_burn,oo_.convergence.raftery_lewis(block_iter).N_prec,oo_.convergence.raftery_lewis(block_iter).N_total,oo_.convergence.raftery_lewis(block_iter).k_thin];
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raftery_data_mat=[raftery_data_mat; max(raftery_data_mat,[],1)];
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labels_Raftery_Lewis = vertcat(param_name, 'Maximum');
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lh = cellofchararraymaxlength(labels_Raftery_Lewis)+2;
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dyntable(options_, my_title, headers, labels_Raftery_Lewis, raftery_data_mat, lh, 10, 0);
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if options_.TeX
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labels_Raftery_Lewis_tex = vertcat(param_name_tex, 'Maximum');
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lh = cellofchararraymaxlength(labels_Raftery_Lewis_tex)+2;
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dyn_latex_table(M_, options_, my_title, ['raftery_lewis_' num2str(block_iter)], headers, labels_Raftery_Lewis_tex, raftery_data_mat, lh, 10, 0);
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end
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end
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end
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for block_iter=1:nblck
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oo_.convergence.raftery_lewis(block_iter).parameter_names=param_name;
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end
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if nblck==1
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return
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end
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if strcmp(options_.posterior_sampler_options.posterior_sampling_method,'slice') && NumberOfDraws<2000
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Origin = 1;
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StepSize = 1;
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else
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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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end
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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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if NumberOfDraws < Origin
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fprintf('The number of simulations is too small to compute the MCMC convergence diagnostics.\n');
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return
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end
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if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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fidTeX = fopen([latexFolder '/' ModelName '_UnivariateDiagnostics.tex'],'w');
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fprintf(fidTeX,'%% TeX eps-loader file generated by mcmc_diagnostics.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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fprintf('Univariate convergence diagnostic, Brooks and Gelman (1998):\n');
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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.MetropolisFolder = MetropolisFolder;
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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 = mcmc_diagnostics_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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if ~isempty(M_.bvar)
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ModelName = [ModelName '_bvar'];
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end
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NamFileInput={[M_.dname '/metropolis/'],[ModelName '_mh*_blck*.mat']};
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[fout, ~, totCPU] = masterParallel(options_.parallel, 1, npar,NamFileInput,'mcmc_diagnostics_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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skipline()
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clear pmet temp moyenne CSUP CINF csup cinf n linea iter tmp;
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pages = floor(npar/npardisp); % changed from 3 to npardisp
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k = 0;
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for i = 1:pages
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hh_fig = dyn_figure(options_.nodisplay,'Name','MCMC univariate convergence diagnostic (Brooks and Gelman,1998)');
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boxplot = 1;
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for j = 1:npardisp % Loop over parameters %npardisp instead of 3
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k = k+1;
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[nam,namtex] = get_the_name(k,TeX,M_,estim_params_,options_.varobs);
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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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subplot(npardisp,3,boxplot); %Added more rows to display more variables
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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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if TeX
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title(namtex,'interpreter','latex')
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else
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title(namnam,'Interpreter','none')
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end
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boxplot = boxplot + 1;
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end
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end
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dyn_saveas(hh_fig,[graphFolder '/' ModelName '_udiag' int2str(i)],options_.nodisplay,options_.graph_format);
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if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
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fprintf(fidTeX,'\\begin{figure}[H]\n');
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fprintf(fidTeX,'\\centering \n');
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fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_udiag%s}\n',options_.figures.textwidth*min((boxplot-1)/3,1),[graphFolder '/' ModelName],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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else % Conditional for additional rows (variables) when not towards the end of the loop
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nr = npardisp;
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nc = 3;
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end
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hh_fig = dyn_figure(options_.nodisplay,'Name','MCMC univariate convergence diagnostic (Brooks and Gelman, 1998)');
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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_.varobs);
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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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if TeX
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namnamtex = [namtex , ' (Interval)'];
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end
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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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if TeX
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namnamtex = [namtex , ' (m2)'];
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end
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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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if TeX
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namnamtex = [namtex , ' (m3)'];
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end
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end
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subplot(nr,nc,boxplot);
|
|
plot(xx,plt1,'-b'); % Pooled
|
|
hold on;
|
|
plot(xx,plt2,'-r'); % Within (mean)
|
|
hold off;
|
|
xlim([xx(1) xx(NumberOfLines)]);
|
|
if TeX
|
|
title(namnamtex,'Interpreter','latex');
|
|
else
|
|
title(namnam,'Interpreter','none');
|
|
end
|
|
boxplot = boxplot + 1;
|
|
end
|
|
end
|
|
dyn_saveas(hh_fig,[ graphFolder '/' ModelName '_udiag' int2str(pages+1)],options_.nodisplay,options_.graph_format);
|
|
if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_udiag%s}\n',options_.figures.textwidth*min((boxplot-1)/nc,1),[graphFolder '/' ModelName],int2str(pages+1));
|
|
if reste == 2
|
|
fprintf(fidTeX,'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n');
|
|
fprintf(fidTeX,'The first, second and third columns are respectively the criteria based on\n');
|
|
fprintf(fidTeX,'the eighty percent interval, the second and third moments.}');
|
|
elseif reste == 1
|
|
fprintf(fidTeX,'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n');
|
|
fprintf(fidTeX,'The first, second and third rows are respectively the criteria based on\n');
|
|
fprintf(fidTeX,'the eighty percent interval, the second and third moments.}');
|
|
end
|
|
fprintf(fidTeX,'\\label{Fig:UnivariateDiagnostics:%s}\n',int2str(pages+1));
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
fprintf(fidTeX,'%% End Of TeX file.');
|
|
fclose(fidTeX);
|
|
end
|
|
end % if reste > 0
|
|
clear UDIAG;
|
|
%
|
|
% Multivariate diagnostic.
|
|
%
|
|
if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
|
fidTeX = fopen([latexFolder '/' ModelName '_MultivariateDiagnostics.tex'],'w');
|
|
fprintf(fidTeX,'%% TeX eps-loader file generated by mcmc_diagnostics.m (Dynare).\n');
|
|
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
|
|
fprintf(fidTeX,' \n');
|
|
end
|
|
tmp = zeros(NumberOfDraws*nblck,3);
|
|
MDIAG = zeros(NumberOfLines,6);
|
|
for b = 1:nblck
|
|
startline = 0;
|
|
for n = 1:NumberOfMcFilesPerBlock
|
|
load([MetropolisFolder '/' ModelName '_mh',int2str(n),'_blck' int2str(b) '.mat'],'logpo2');
|
|
nlogpo2 = size(logpo2,1);
|
|
tmp((b-1)*NumberOfDraws+startline+(1:nlogpo2),1) = logpo2;
|
|
startline = startline+nlogpo2;
|
|
end
|
|
end
|
|
clear logpo2;
|
|
tmp(:,2) = kron(transpose(1:nblck),ones(NumberOfDraws,1));
|
|
tmp(:,3) = kron(ones(nblck,1),time');
|
|
tmp = sortrows(tmp,1);
|
|
ligne = 0;
|
|
for iter = Origin:StepSize:NumberOfDraws
|
|
ligne = ligne+1;
|
|
linea = ceil(options_.mh_drop*iter);
|
|
n = iter-linea+1;
|
|
cinf = max(1,round(n*ALPHA/2));
|
|
csup = round(n*(1-ALPHA/2));
|
|
CINF = max(1,round(nblck*n*ALPHA/2));
|
|
CSUP = round(nblck*n*(1-ALPHA/2));
|
|
temp = tmp(find((tmp(:,3)>=linea) & (tmp(:,3)<=iter)),1:2);
|
|
MDIAG(ligne,1) = temp(CSUP,1)-temp(CINF,1);
|
|
moyenne = mean(temp(:,1));%% Pooled mean.
|
|
MDIAG(ligne,3) = sum((temp(:,1)-moyenne).^2)/(nblck*n-1);
|
|
MDIAG(ligne,5) = sum(abs(temp(:,1)-moyenne).^3)/(nblck*n-1);
|
|
for i=1:nblck
|
|
pmet = temp(find(temp(:,2)==i));
|
|
MDIAG(ligne,2) = MDIAG(ligne,2) + pmet(csup,1)-pmet(cinf,1);
|
|
moyenne = mean(pmet,1); %% Within mean.
|
|
MDIAG(ligne,4) = MDIAG(ligne,4) + sum((pmet(:,1)-moyenne).^2)/(n-1);
|
|
MDIAG(ligne,6) = MDIAG(ligne,6) + sum(abs(pmet(:,1)-moyenne).^3)/(n-1);
|
|
end
|
|
end
|
|
MDIAG(:,[2 4 6],:) = MDIAG(:,[2 4 6],:)/nblck;
|
|
|
|
hh_fig = dyn_figure(options_.nodisplay,'Name','Multivariate convergence diagnostic');
|
|
boxplot = 1;
|
|
for crit = 1:3
|
|
if crit == 1
|
|
plt1 = MDIAG(:,1);
|
|
plt2 = MDIAG(:,2);
|
|
namnam = 'Interval';
|
|
elseif crit == 2
|
|
plt1 = MDIAG(:,3);
|
|
plt2 = MDIAG(:,4);
|
|
namnam = 'm2';
|
|
elseif crit == 3
|
|
plt1 = MDIAG(:,5);
|
|
plt2 = MDIAG(:,6);
|
|
namnam = 'm3';
|
|
end
|
|
subplot(3,1,boxplot);
|
|
plot(xx,plt1,'-b'); % Pooled
|
|
hold on
|
|
plot(xx,plt2,'-r'); % Within (mean)
|
|
hold off
|
|
xlim([xx(1) xx(NumberOfLines)])
|
|
title(namnam,'Interpreter','none');
|
|
boxplot = boxplot + 1;
|
|
end
|
|
dyn_saveas(hh_fig,[ graphFolder '/' ModelName '_mdiag'],options_.nodisplay,options_.graph_format);
|
|
|
|
if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s_mdiag}\n',[graphFolder '/' ModelName]);
|
|
fprintf(fidTeX,'\\caption{Multivariate convergence diagnostics for the Metropolis-Hastings.\n');
|
|
fprintf(fidTeX,'The first, second and third rows are respectively the criteria based on\n');
|
|
fprintf(fidTeX,'the eighty percent interval, the second and third moments. The different \n');
|
|
fprintf(fidTeX,'parameters are aggregated using the posterior kernel.}');
|
|
fprintf(fidTeX,'\\label{Fig:MultivariateDiagnostics}\n');
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
fprintf(fidTeX,'%% End Of TeX file.');
|
|
fclose(fidTeX);
|
|
end
|