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function McMCDiagnostics(options_, estim_params_, M_)
% function McMCDiagnostics
% Computes convergence tests
%
% INPUTS
% options_ [structure]
% estim_params_ [structure]
% M_ [structure]
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
%
% PARALLEL CONTEXT
% See the comment in random_walk_metropolis_hastings.m funtion.
% Copyright (C) 2005-2012 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
DirectoryName = CheckPath('Output',M_.dname);
MhDirectoryName = CheckPath('metropolis',M_.dname);
TeX = options_.TeX;
nblck = options_.mh_nblck;
% Brooks and Gelman tests need more than one block
if nblck == 1
return;
end
npar = estim_params_.nvx;
npar = npar + estim_params_.nvn;
npar = npar + estim_params_.ncx;
npar = npar + estim_params_.ncn;
npar = npar + estim_params_.np ;
MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
load([MhDirectoryName '/' M_.fname '_mh_history.mat'])
NumberOfMcFilesPerBlock = size(dir([MhDirectoryName ,filesep, M_.fname '_mh*_blck1.mat']),1);
for blck = 2:nblck
tmp = size(dir([MhDirectoryName ,filesep, M_.fname '_mh*_blck' int2str(blck) '.mat']),1);
if tmp~=NumberOfMcFilesPerBlock
disp(['McMCDiagnostics:: The number of mh files in chain ' int2str(blck) ' is ' int2str(tmp) ' while'])
disp([' the number of mh files in chain 1 is ' int2str(mcfiles) '!'])
error('The number of mh files has to be constant across chains!')
end
end
PastDraws = sum(record.MhDraws,1);
LastFileNumber = PastDraws(2);
LastLineNumber = record.MhDraws(end,3);
NumberOfDraws = PastDraws(1);
Origin = 1000;
StepSize = ceil((NumberOfDraws-Origin)/100);% So that the computational time does not
ALPHA = 0.2; % increase too much with the number of simulations.
time = 1:NumberOfDraws;
xx = Origin:StepSize:NumberOfDraws;
NumberOfLines = length(xx);
tmp = zeros(NumberOfDraws*nblck,3);
UDIAG = zeros(NumberOfLines,6,npar);
if NumberOfDraws < Origin
disp('MCMC Diagnostics :: The number of simulations is to small to compute the MCMC convergence diagnostics.')
return
end
if TeX
fidTeX = fopen([DirectoryName '/' M_.fname '_UnivariateDiagnostics.TeX'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by McmcDiagnostics.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
disp('MCMC Diagnostics: Univariate convergence diagnostic, Brooks and Gelman (1998):')
% The mandatory variables for local/remote parallel
% computing are stored in localVars struct.
localVars.MhDirectoryName = MhDirectoryName;
localVars.nblck = nblck;
localVars.NumberOfMcFilesPerBlock = NumberOfMcFilesPerBlock;
localVars.Origin = Origin;
localVars.StepSize = StepSize;
localVars.mh_drop = options_.mh_drop;
localVars.NumberOfDraws = NumberOfDraws;
localVars.NumberOfLines = NumberOfLines;
localVars.time = time;
localVars.M_ = M_;
% Like sequential execution!
if isnumeric(options_.parallel),
fout = McMCDiagnostics_core(localVars,1,npar,0);
UDIAG = fout.UDIAG;
clear fout
% Parallel execution!
else
ModelName = M_.fname;
if ~isempty(M_.bvar)
ModelName = [M_.fname '_bvar'];
end
NamFileInput={[M_.dname '/metropolis/'],[ModelName '_mh*_blck*.mat']};
[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, 1, npar,NamFileInput,'McMCDiagnostics_core', localVars, [], options_.parallel_info);
UDIAG = fout(1).UDIAG;
for j=2:totCPU,
UDIAG = cat(3,UDIAG ,fout(j).UDIAG);
end
end
UDIAG(:,[2 4 6],:) = UDIAG(:,[2 4 6],:)/nblck;
disp(' ')
clear pmet temp moyenne CSUP CINF csup cinf n linea iter tmp;
pages = floor(npar/3);
k = 0;
for i = 1:pages
h=dyn_figure(options_,'Name','MCMC univariate diagnostic (Brooks and Gelman,1998)');
boxplot = 1;
for j = 1:3 % Loop over parameters
k = k+1;
[nam,namtex] = get_the_name(k,TeX,M_,estim_params_,options_);
for crit = 1:3% Loop over criteria
if crit == 1
plt1 = UDIAG(:,1,k);
plt2 = UDIAG(:,2,k);
namnam = [nam , ' (Interval)'];
elseif crit == 2
plt1 = UDIAG(:,3,k);
plt2 = UDIAG(:,4,k);
namnam = [nam , ' (m2)'];
elseif crit == 3
plt1 = UDIAG(:,5,k);
plt2 = UDIAG(:,6,k);
namnam = [nam , ' (m3)'];
end
if TeX
if j==1
NAMES = deblank(namnam);
TEXNAMES = deblank(namtex);
else
NAMES = char(NAMES,deblank(namnam));
TEXNAMES = char(TEXNAMES,deblank(namtex));
end
end
subplot(3,3,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
end
dyn_saveas(h,[DirectoryName '/' M_.fname '_udiag' int2str(i)],options_);
if TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(NAMES,1)
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_udiag%s}\n',[DirectoryName '/' M_.fname],int2str(i));
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.}');
fprintf(fidTeX,'\\label{Fig:UnivariateDiagnostics:%s}\n',int2str(i));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
end
end
reste = npar-k;
if reste
if reste == 1
nr = 3;
nc = 1;
elseif reste == 2;
nr = 2;
nc = 3;
end
h = dyn_figure(options_,'Name','MCMC univariate diagnostic (Brooks and Gelman, 1998)');
boxplot = 1;
for j = 1:reste
k = k+1;
[nam,namtex] = get_the_name(k,TeX,M_,estim_params_,options_);
for crit = 1:3
if crit == 1
plt1 = UDIAG(:,1,k);
plt2 = UDIAG(:,2,k);
namnam = [nam , ' (Interval)'];
elseif crit == 2
plt1 = UDIAG(:,3,k);
plt2 = UDIAG(:,4,k);
namnam = [nam , ' (m2)'];
elseif crit == 3
plt1 = UDIAG(:,5,k);
plt2 = UDIAG(:,6,k);
namnam = [nam , ' (m3)'];
end
if TeX
if j==1
NAMES = deblank(namnam);
TEXNAMES = deblank(namtex);
else
NAMES = char(NAMES,deblank(namnam));
TEXNAMES = char(TEXNAMES,deblank(namtex));
end
end
subplot(nr,nc,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
end
dyn_saveas(h,[ DirectoryName '/' M_.fname '_udiag' int2str(pages+1)],options_);
if TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(NAMES,1);
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_udiag%s}\n',[DirectoryName '/' M_.fname],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
fidTeX = fopen([DirectoryName '/' M_.fname '_MultivariateDiagnostics.TeX'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by McmcDiagnostics.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([MhDirectoryName '/' mcfiles(n,1,b).name],'logpo2');
load([MhDirectoryName '/' M_.fname '_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
% $$$ %load([MhDirectoryName '/' mcfiles(NumberOfMcFilesPerBlock,1,b).name],'logpo2');
% $$$ load([MhDirectoryName '/' M_.fname '_mh',int2str(NumberOfMcFilesPerBlock),'_blck' int2str(b) '.mat'],'logpo2');
% $$$ tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+ MAX_nruns*(LastFileNumber-1)+LastLineNumber,1) = logpo2;
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 = round(n*ALPHA/2);
csup = round(n*(1-ALPHA/2));
CINF = 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;
h = dyn_figure(options_,'Name','Multivariate 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
if TeX
if crit == 1
NAMES = deblank(namnam);
else
NAMES = char(NAMES,deblank(namnam));
end
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(h,[ DirectoryName '/' M_.fname '_mdiag'],options_);
if TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:3
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),' ');
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_mdiag}\n',[DirectoryName '/' M_.fname]);
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
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