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cpc_summary.m
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cpc_summary.m
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function [] = cpc_summary(ID)
global dataPath Msamples burn Nchains datachoice filename multipleconst
global simu MinNtimesw inc_int rep model estAlpha inc_ints
id = str2double(num2str(ID)); % 1-90
rng('default'); rng(id*241);
rep = ceil(id/3);
model = id - (rep-1)*3;
datachoice = 1;
simu = 1;
inc_int = 0; inc_ints = 1;
if simu == 1
Msamples = 1e3; burn = 0; Nchains = 3;
else
Msamples = 1e3; burn = 0; Nchains = 4;
end
estAlpha = 1;
multipleconst = 1;
dataPath = './';
if datachoice == 1
MinNtimesw = 7;
elseif datachoice == 3
MinNtimesw = 3;
end
filename = strcat(dataPath,'figuremodel',num2str(model),'_',num2str(rep),'.ps');
ConvergenceDiagnostics(model, rep)
checkmodelpro(model, rep)
end
function [] = ConvergenceDiagnostics(model, rep)
%function to check the convergence of MC samples
global dataPath Msamples burn Nchains filename datachoice inc_int simu timesize
if simu == 1
load(strcat('simulateddata',num2str(datachoice),'_',num2str(rep),'.mat'))
timesize = size(data,1)-1;
end
if datachoice == 1
%set monitoring sites
monitorsite=[1 12 18 27 37 46];
%set monitoring times
monitortime=[1 14 24 35];
elseif datachoice == 2
monitorsite=[57 12 87];
monitortime=[8 20 35];
elseif datachoice == 3
monitorsite=[12 16 24 52 61 67];
monitortime=[1 4 8 12 17];
end
nsite = numel(monitorsite); ntime = numel(monitortime);
%D is number of parameters
%M is number of MCMC samples
%N is number of chains
%NxDxM matrix to put in psrf.m function
stlen = numel(monitorsite)*numel(monitortime);
Dparameters = 2+nsite*2+stlen;
if inc_int == 1
Dparameters = Dparameters + numel(monitorsite)*numel(monitortime);
end
MCSamples = zeros(Nchains,Dparameters+2,Msamples);
tmp = cell(Msamples,Nchains);
ds = zeros(Msamples+burn,Nchains);
n2loglik = zeros(Msamples+burn,Nchains);
for inits = 1:Nchains
load(strcat(dataPath,'MCMCoutputCh',num2str(model),'_ini',num2str(inits),'_',num2str(rep),'.mat'))
for iter = 1:length(xall) %(length(xall) - Msamples + 1)
if iter > burn
tmp{iter-burn, inits} = xall{iter};
end
Nstar = length(xall{iter});
ds(iter, inits) = Nstar;
loglik = 0;
for r = 1:Nstar
nr = length(xall{iter}{r}.Siteincluster);
x = xall{iter}{r};
obj = computefromInt(x.NpointinTimeIn);
for s = 1:nr
obsY = data(2:end, x.Siteincluster(s));
tmpDelta = obsY - obj.X1*x.parameter' - obj.X0*x.beta0{s}';
loglik = loglik - 0.5*length(obsY)*log(2*pi*x.sigma2(s)) - 0.5/x.sigma2(s)*(sum(tmpDelta.^2));
end
end
n2loglik(iter, inits) = -2*loglik;
end
end
xall = tmp; clear tmp;
plot(n2loglik)
title('-2loglikelihood statistic for monitoring convergence');
legend('chain 1','chain 2','chain 3','Location','Best')
orient landscape
print('-painters', '-dpsc2', '-r600', filename)
for iter = 1:Msamples
for nch = 1:Nchains
MCSamples(nch,1:2,iter) = [xall{iter,nch}{1,1}.tau2, xall{iter,nch}{1,1}.gamma];
MCSamples(nch,Dparameters+(1:2),iter) = [xall{iter,nch}{1,1}.lambda, xall{iter,nch}{1,1}.alpha];
Nstar = size(xall{iter,nch},1);% define N*: the number of cluster
for st = 1:nsite
onesite = monitorsite(st);
for ncl = 1:Nstar
marks = abs(xall{iter,nch}{ncl,1}.Siteincluster-onesite);
if ~min(marks)
sind = find(~marks);
MCSamples(nch,2+st,iter) = xall{iter,nch}{ncl,1}.sigma2(sind); %sigma2
MCSamples(nch,2+st+nsite,iter) = xall{iter,nch}{ncl,1}.u(sind); %u
for tm = 1:ntime
indx = tm+(st-1)*ntime;
onetime = monitortime(tm);
TmpTimeInt = [0 xall{iter,nch}{ncl,1}.NpointinTimeIn];
SizeInt = numel(TmpTimeInt);
for titer=2:SizeInt
if and(sum(TmpTimeInt(1:(titer-1)))<onetime,sum(TmpTimeInt(1:titer))>=onetime)
timeindx=titer-1;
end
end
MCSamples(nch,2+nsite*2+indx,iter) = xall{iter,nch}{ncl,1}.parameter(timeindx);
if inc_int == 1
MCSamples(nch,2+nsite*2+stlen+indx,iter) = xall{iter,nch}{ncl,1}.beta0(timeindx);
end
end
end
end
end
end
end
R = psrf(MCSamples);
display(R)
plot(reshape(MCSamples(:,1,:),[Nchains,Msamples])')
series = reshape(MCSamples(:,1,:),[Nchains*Msamples,1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('\tau^2 with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
legend('chain 1','chain 2','chain 3','chain 4','Location','Best')
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
hist(series); title('histogram of \tau^2 for pooled chains');
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
plot(reshape(MCSamples(:,2,:),[Nchains,Msamples])')
series = reshape(MCSamples(:,2,:),[Nchains*Msamples 1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('\gamma with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
hist(series); title('histogram of \gamma for pooled chains');
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
plot(reshape(MCSamples(:,Dparameters+1,:),[Nchains,Msamples])')
series = reshape(MCSamples(:,Dparameters+1,:),[Nchains*Msamples,1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('\lambda with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
hist(series); title('histogram of \lambda for pooled chains');
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
plot(reshape(MCSamples(:,Dparameters+2,:),[Nchains,Msamples])')
series = reshape(MCSamples(:,Dparameters+2,:),[Nchains*Msamples,1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('\alpha_0 with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
hist(series); title('histogram of \alpha_0 for pooled chains');
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
for j = 1:nsite
plot(reshape(MCSamples(:,2+j,:), [Nchains Msamples])')
series = reshape(MCSamples(:,2+j,:),[Nchains*Msamples 1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('\sigma^2 for site ',num2str(monitorsite(j)),' with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
end
for j = 1:nsite
plot(reshape(MCSamples(:,2+j+nsite,:), [Nchains Msamples])')
series = reshape(MCSamples(:,2+j+nsite,:),[Nchains*Msamples 1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('u for site ',num2str(monitorsite(j)),' with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
end
for j = 1:nsite
for k = 1:ntime
indx = k+(j-1)*ntime;
plot(reshape(MCSamples(:,2+nsite*2+indx,:), [Nchains Msamples])')
series = reshape(MCSamples(:,2+nsite*2+indx,:),[Nchains*Msamples 1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('\beta for site ',num2str(monitorsite(j)),' time ',num2str(monitortime(k)),'with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
end
end
if inc_int
for j = 1:nsite
for k = 1:ntime
indx = k+(j-1)*ntime;
plot(reshape(MCSamples(:,2+nsite*2+stlen+indx,:), [Nchains Msamples])')
series = reshape(MCSamples(:,2+nsite*2+stlen+indx,:),[Nchains*Msamples 1]);
[l,u] = FindHPDset(series,0.95,[]);
title({strcat('intercept for site ',num2str(monitorsite(j)),' time ',num2str(monitortime(k)),'with mean, median, 95% HPD set: ',num2str([mean(series), median(series)]));strcat('[',num2str([l u]),']')});
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
end
end
end
save(strcat(dataPath,'MCMCoutputModel',num2str(model),'_',num2str(rep),'.mat'),'xall', 'R')
display('done')
end
function [] = checkmodelpro(model, rep)
global t timesize simu inc_int MinNtimesw inc_ints
global sitesize dataPath Msamples Nchains datachoice filename true_bvec Clustergroup0 true_cpt
if datachoice == 1
load('neighbor.txt')
else
load('iowa_neighbor.mat'); neighbor = NB; %larger
end
if simu == 1
load(strcat('simulateddata',num2str(datachoice),'_',num2str(rep),'.mat')) % get true_bvec
data = data(2:end,:); %#ok<NODEF>
else
if datachoice == 1
load('data.txt') % for real data
data = log(data(2:end,:)); %#ok<NODEF>
u0 = zeros(1,size(data,2));
if inc_ints ~= 1
for j = 1:size(data,2)
ay = data(1:MinNtimesw,j); ax = 1:MinNtimesw;
betas =regress(ay, [ones(length(ax),1), ax']);
u0(j) = betas(1); % mean vector of random effects
end
data = data - repmat(u0, [size(data,1) 1]);
end
elseif datachoice == 2
data = load('iowa09-12.txt'); data = data'; data = data(5:35,:);
elseif datachoice == 3
data = load('yeast cell data.txt');
end
end
t = cat(1,1:size(data,2),data);
if inc_int ~= 1 && inc_ints ~= 1
GenerateRSSandbetamodel1(t, model, rep);
end
data = t(2:end,:);
site = t(1,:);
sitesize = size(site,2);
% timesize = size(data,1);
HPDp = 0.95;
pp = 0.05;
load(strcat(dataPath,'MCMCoutputModel',num2str(model),'_',num2str(rep),'.mat'))
for j = 1:Msamples
for chs = 1:Nchains
xallIf{j+Msamples*(chs-1),1} = xall{j,chs};
end
end
clear xall
ClusteIndex = zeros(sitesize,1);
TClustersize = size(xallIf,1);
for i = 1:TClustersize,
ClusteIndex(size(xallIf{i,1},1)) = ClusteIndex(size(xallIf{i,1},1))+1;
end
[val,c] = max(ClusteIndex);
afteriteration=1;
totIter = size(xallIf,1);
tau2 = zeros(1, totIter); gamma = zeros(1,totIter);
lambda = zeros(1, totIter); alpha = zeros(1,totIter);
for loop = 1:totIter
tau2(loop) = xallIf{loop,1}{1}.tau2;
gamma(loop) = xallIf{loop,1}{1}.gamma;
lambda(loop) = xallIf{loop,1}{1}.lambda;
alpha(loop) = xallIf{loop,1}{1}.alpha;
end
[l,u] = FindHPDset(gamma',HPDp,[]); HPD.gamma = [l mean(gamma) median(gamma) u];
[l,u] = FindHPDset(tau2',HPDp,[]); HPD.tau2 = [l mean(tau2) median(tau2) u];
[l,u] = FindHPDset(lambda',HPDp,[]); HPD.lambda = [l mean(lambda) median(lambda) u];
[l,u] = FindHPDset(alpha',HPDp,[]); HPD.alpha = [l mean(alpha) median(alpha) u];
l = quantile(gamma,(1-HPDp)/2); u = quantile(gamma,1-(1-HPDp)/2);
HPD.gammaCI = [l mean(gamma) u];
l = quantile(tau2,(1-HPDp)/2); u = quantile(tau2,1-(1-HPDp)/2);
HPD.tau2CI = [l mean(tau2) u];
l = quantile(lambda,(1-HPDp)/2); u = quantile(lambda,1-(1-HPDp)/2);
HPD.lambdaCI = [l mean(lambda) u];
l = quantile(alpha,(1-HPDp)/2); u = quantile(alpha,1-(1-HPDp)/2);
HPD.alphaCI = [l mean(alpha) u];
% cluster information
[Clustergroup,SS,SS2] = makelinkage(xallIf,c);
if simu == 1 % create the 2 by 2 cross-classification table and measures
clusterMeasure = computefromTable(Clustergroup, Clustergroup0);
display('cross-classification results:')
display(clusterMeasure.tab)
display(clusterMeasure)
outH = getDistance2(data, Clustergroup0, Clustergroup);
scatter(clusterMeasure.sensitivity, clusterMeasure.specificity, 60,'r','filled');
hold off;
else
hold on; outH = getDistance2(data, [], Clustergroup); hold off;
end
if simu == 0
save('outH.mat','outH')
else
save(strcat('outH_',num2str(rep),'.mat'),'outH')
end
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
PsOutput = cell(c,1);
fitMeasure.Pred = zeros(timesize, sitesize);
fitMeasure.Drep = zeros(totIter-afteriteration+1, sitesize);
fitMeasure.Dhat = zeros(1, sitesize);
fitMeasure.SSE = zeros(1, sitesize);
fitMeasure.MSE = zeros(1, sitesize);
fitMeasure.BIC = zeros(1, sitesize);
fitMeasure.DIC = zeros(1, sitesize);
cptMeasure.distcpt = zeros(timesize,sitesize); % mean posterior distribution of changepoint at each time, each site
cptMeasure.HPD = cell(1,sitesize);
cptMeasure.HPDp = cell(1,sitesize);
cptMeasure.KL = zeros(1,sitesize); % KL divergence between posterior distribution of changepoint and the true mass
cptMeasure.entropy = zeros(1,sitesize); % entropy of posterior
cptMeasure.relentropy = zeros(1,sitesize); % relative entropy of posterior
cptMeasure.accuracy = zeros(1,sitesize); % mean accuracy rate for detection of true changepoints
cptMeasure.betadiff = zeros(1,sitesize); % difference of beta
cptMeasure.betabwHPD = zeros(1,sitesize); % HPD band width of difference
cptMeasure.betabwCI = zeros(1,sitesize); % CI band width of difference
% randn('state',267);
Rdata = cell(1,c);
for i = 1:c,
monitorsite = Clustergroup{i,1};
[PsOutput{i,1},fitMeasure,cptMeasure,Rdata{i}] = getPSOutput(model, xallIf,afteriteration,t,monitorsite,totIter,HPDp,pp,...
fitMeasure, cptMeasure);
orient landscape
% print('-painters', '-depsc2', '-r600', strcat(num2str(i),filename))
print('-painters', '-dpsc2', '-r600', '-append', filename)
end
fitMeasure.Dave = mean(-2*fitMeasure.Drep, 1); fitMeasure.Dhat = -2*fitMeasure.Dhat;
fitMeasure.DIC = 2*fitMeasure.Dave - fitMeasure.Dhat;
display('DIC:')
display(fitMeasure.DIC)
fitMeasure.Drep = []; % no need to store
% Name = char('Alabama','Arizona','Arkansas','California','Colorado','Connecticut','Delaware','Washington DC','Florida','Georgia','Idaho','Illinois','Indiana','Iowa','Kansas','Kentucky',...
% 'Louisiana','Maine','Maryland','Massachusetts','Michigan','Minnesota','Mississippi','Missouri','Montana','Nebraska','Nevada','New Hampshire ',...
% 'New Jersey','New Mexico','New York','North Carolina','North Dakota','Ohio','Oklahoma','Oregon','Pennsylvania','Rhode Island','South Carolina',...
% 'South Dakota','Tennessee','Texas','Utah','Vermont','Virginia','Washington','West Virginia','Wisconsin','Wyoming');
%%% probability of number of clusters
postCluster = [(1:11)' ClusteIndex(1:11)/(Msamples * Nchains)];
display('posterior distribution of clusters:')
display(postCluster)
tmpvec = postCluster(postCluster(:,2) ~=0, 2);
clusterMeasure.SS = SS;
clusterMeasure.SS2 = SS2;
clusterMeasure.postCluster = postCluster;
clusterMeasure.entropy = - sum(tmpvec.*log(tmpvec));
if length(tmpvec) == 1
clusterMeasure.relentropy = 1;
else
clusterMeasure.relentropy = 1 - clusterMeasure.entropy/log(length(tmpvec));
end
display('entropy:')
display(clusterMeasure.entropy)
save(strcat(dataPath,'Rdata',num2str(model),'_',num2str(rep),'.mat'),'Rdata')
save(strcat(dataPath,'PsOutputModel',num2str(model),'_',num2str(rep),'.mat'),...
'PsOutput','fitMeasure','HPD','R','clusterMeasure','cptMeasure','Clustergroup','outH')
close all
end
function [psoutput,fitMeasure,cptMeasure,Rdata] = ...
getPSOutput(model, xalliter,afteriter,alldata,allmonitorsite,totIter,HPDp,pp,fitMeasure,cptMeasure)
global timesize dataPath datachoice rep sitesize MinNtimesw simu
global multipleconst inc_int inc_ints true_bvec Clustergroup0 estAlpha true_cpt
if inc_int ~= 1 && inc_ints ~= 1
load(strcat(dataPath,'Ressandbeta',num2str(model),'_',num2str(rep),'.mat')) % beta0s
end
monitorparameterindex=[1]; thetatemp=[];
beta1temp=cell(size(allmonitorsite,2),1); beta0temp=cell(size(allmonitorsite,2),1);
sigmavaltemp=cell(size(allmonitorsite,2),1); uvaltemp=cell(size(allmonitorsite,2),1); c=[];
Name = cell(1, sitesize);
for inds = 1:sitesize
Name{inds} = num2str(inds);
end
if datachoice == 1
Name=char('Alabama','Arizona','Arkansas','California','Colorado','Connecticut','Delaware','Washington DC','Florida','Georgia','Idaho','Illinois','Indiana','Iowa','Kansas','Kentucky',...
'Louisiana','Maine','Maryland','Massachusetts','Michigan','Minnesota','Mississippi','Missouri','Montana','Nebraska','Nevada','New Hampshire ',...
'New Jersey','New Mexico','New York','North Carolina','North Dakota','Ohio','Oklahoma','Oregon','Pennsylvania','Rhode Island','South Carolina',...
'South Dakota','Tennessee','Texas','Utah','Vermont','Virginia','Washington','West Virginia','Wisconsin','Wyoming');
end
figure; hold on;
miny=((1./multipleconst).*(min(min(alldata(2:end,allmonitorsite)))-5));
maxy=((1./multipleconst).*(max(max(alldata(2:end,allmonitorsite)))+5));
psoutput.sigma=zeros(size(allmonitorsite,2),1);
psoutput.theta=cell(size(allmonitorsite,2),1);
psoutput.changetime=cell(size(allmonitorsite,2),1);
psoutput.NTimeInterals=zeros(size(allmonitorsite,2),1);
psoutput.NpointinTimeIn=cell(size(allmonitorsite,2),1);
psoutput.Siteincluster=allmonitorsite;
psoutput.HPDsetdata=cell(size(allmonitorsite,2),1);
psoutput.yvalue=cell(size(allmonitorsite,2),1);
psoutput.sepredT=cell(size(allmonitorsite,2),1);
psoutput.betadifference=cell(size(allmonitorsite,2),1);
psoutput.betadifferenceHPD=cell(size(allmonitorsite,2),2);
psoutput.betadifferenceCI=cell(size(allmonitorsite,2),2);
psoutput.numTCounts=cell(size(allmonitorsite,2),1);
psoutput.numTintervalCounts=cell(size(allmonitorsite,2),1);
psoutput.thetaCI = cell(size(allmonitorsite,2),2);
yy = zeros(totIter-afteriter+1, size(allmonitorsite,2));
nr = size(allmonitorsite,2);
Rdata.sites = allmonitorsite;
Rdata.ObsY = zeros(timesize,nr);
Rdata.miny = zeros(1,nr);
Rdata.maxy = zeros(1,nr);
% Rdata.Title = zeros(1,nr);
Rdata.sigma2 = zeros(1,nr);
Rdata.yvalueupperCI = zeros(timesize,nr);
Rdata.yvaluelowerCI = zeros(timesize,nr);
Rdata.cpt = cell(1,nr);
for monidex = 1:size(allmonitorsite,2)
if length(allmonitorsite) == 8
if monidex ~= 8
subplot(3,4,monidex);
else
subplot(3,4,monidex+1);
end
elseif length(allmonitorsite) == 16
subplot(4,4,monidex);
else
subplot(floor(sqrt(size(allmonitorsite,2)))+1,floor(sqrt(size(allmonitorsite,2)))+1,monidex);
end
obsY = ((1./multipleconst).*alldata(2:end,allmonitorsite(monidex)));
plot(1:timesize,obsY,...
'o','MarkerSize',2,'MarkerEdgeColor','b','MarkerFaceColor','b');
hold on;
axis([0 timesize+1 miny maxy]);
Rdata.ObsY(:,monidex) = obsY;
if datachoice ~= 1
title(Name{allmonitorsite(monidex)}, 'Fontsize', 16)
else
title(Name(allmonitorsite(monidex),:), 'Fontsize', 16)
end
% Rdata.Title(monidex) = Name(allmonitorsite(monidex),:);
AllHPDsetdata = zeros(timesize,(totIter-afteriter+1));
totalTinterval = [(1:1:timesize)',zeros(timesize,1)];
carddiff = 0;
for ss = afteriter:totIter,
Nstar = size(xalliter{ss,:},1);% define N*: the number of cluster
HPDsetdata=[];
for j = 1:Nstar,
if (min(abs(xalliter{ss,1}{j,1}.Siteincluster-allmonitorsite(monidex)))==0)
% allmonitorsite(monidex) lies in jth cluster at ssth iteration
c(ss,monidex) = j;
tmp0 = cumsum([1 xalliter{ss,1}{j,1}.NpointinTimeIn]);
obs_cpt = tmp0(2:(length(tmp0)-1)); % observed changepoint
if simu == 1
for i0 = 1:size(Clustergroup0)
if any(Clustergroup0{i0} == allmonitorsite(monidex))
k0 = i0;
end
end
if length(obs_cpt)>length(true_cpt{k0})
carddiff = carddiff + length(obs_cpt)-length(true_cpt{k0});
end
end
cptMeasure.distcpt(obs_cpt, allmonitorsite(monidex)) = cptMeasure.distcpt(obs_cpt, allmonitorsite(monidex)) + 1;
[xx,yy(ss,monidex)] = min(abs(xalliter{ss,1}{j,1}.Siteincluster-allmonitorsite(monidex)));
% yy: the position of the site allmonitorsite(monidex) in the cluster j
TimeIntervalHPD = [0 cumsum(xalliter{ss,1}{j,1}.NpointinTimeIn,2)];
BHPD = Combination(xalliter{ss,1}{j,1}.NTimeIntervals,timesize-xalliter{ss,1}{j,1}.NTimeIntervals*MinNtimesw);
[xxxtmep,yyytemp] = min(sum((BHPD-ones(size(BHPD,1),1)*xalliter{ss,1}{j,1}.NpointinTimeIn).^2,2));
obj = computefromInt(xalliter{ss,1}{j,1}.NpointinTimeIn);
tmpSigma2 = (1./multipleconst.^2).*xalliter{ss,1}{j,1}.sigma2(yy(ss,monidex));
tmpU = (1./multipleconst).*xalliter{ss,1}{j,1}.u(yy(ss,monidex));
tmpDelta = obsY - tmpU - ...
(1./multipleconst).*obj.X1*xalliter{ss,1}{j,1}.parameter';
% Drep(ss) = Drep(ss) - 0.5*(timesize - length(TimeIntervalHPD) + 1)*log(2*pi*tmpSigma2)...
% - 0.5*log(det(obj.X0' * obj.X0)) - 0.5/tmpSigma2*tmpDelta'*obj.Sigma*tmpDelta;
if inc_int ~= 1 && inc_ints ~= 1
Betazeros = beta0s{xalliter{ss,1}{j,1}.NTimeIntervals,allmonitorsite(monidex)}(yyytemp,:);
if estAlpha == 1 && obj.n1 < timesize
Betazeros(2:end) = mvnrnd(inv(obj.X0' * obj.X0)*obj.X0'*tmpDelta((obj.n1+1):end), tmpSigma2*inv(obj.X0' * obj.X0));
end
elseif inc_int == 1
Betazeros = xalliter{ss,1}{j,1}.beta0;
elseif inc_ints == 1
Betazeros = xalliter{ss,1}{j,1}.beta0{yy(ss,monidex)};
end
if inc_ints ~= 1
fitMeasure.Drep(ss,allmonitorsite(monidex)) = fitMeasure.Drep(ss,allmonitorsite(monidex)) - 0.5*timesize*log(2*pi*tmpSigma2) - 0.5/tmpSigma2*(sum(tmpDelta(1:obj.n1).^2) + ...
sum((tmpDelta((obj.n1+1):end) - obj.X0*Betazeros(2:end)').^2));
else
fitMeasure.Drep(ss,allmonitorsite(monidex)) = fitMeasure.Drep(ss,allmonitorsite(monidex)) - 0.5*timesize*log(2*pi*tmpSigma2) - 0.5/tmpSigma2*(sum(tmpDelta(1:obj.n1).^2) + ...
sum((tmpDelta - obj.X0*Betazeros').^2));
end
for i = 1:size(TimeIntervalHPD,2)-1,
HPDsettemp = [];
timeaxisHPD = [TimeIntervalHPD(i)+1:1:TimeIntervalHPD(i+1)];
tmpmean = timeaxisHPD.*xalliter{ss,1}{j,1}.parameter(i) + xalliter{ss,1}{j,1}.u(yy(ss,monidex)) + Betazeros(i);
for tm = 1:numel(tmpmean)
HPDsettemp(tm)=(1./multipleconst).*(normrnd(tmpmean(tm),sqrt(xalliter{ss,1}{j,1}.sigma2(yy(ss,monidex)))));
end
HPDsetdata = [HPDsetdata HPDsettemp];
end
AllHPDsetdata(:,ss)= HPDsetdata';
utemp(ss,monidex) = tmpU;
sigmatemp(ss,monidex) = tmpSigma2;
thetatemp(ss,monidex) = (1./multipleconst).*(xalliter{ss,1}{j,1}.parameter(monitorparameterindex));
timeintervaltemp = size(xalliter{ss,1}{j,1}.NpointinTimeIn, 2); % # of time intervals
totalTinterval(timeintervaltemp,2) = totalTinterval(timeintervaltemp,2) + 1;
else
end
end
end
carddiff = carddiff/(totIter-afteriter+1);
tmpdistcpt = cptMeasure.distcpt(:, allmonitorsite(monidex))/(totIter-afteriter+1);
cptMeasure.distcpt(:, allmonitorsite(monidex)) = tmpdistcpt/sum(tmpdistcpt); % make it a probability
[tmpdistcptS, I] = sort(cptMeasure.distcpt(:, allmonitorsite(monidex)), 'descend');
ind = find(cumsum(tmpdistcptS) >= HPDp); ind = ind(1);
cptMeasure.HPD{allmonitorsite(monidex)} = I(1:ind);
cptMeasure.HPDp{allmonitorsite(monidex)} = cumsum(tmpdistcptS(1:ind));
distQ = cptMeasure.distcpt(:, allmonitorsite(monidex)); distQ = distQ(distQ>0);
cptMeasure.entropy(allmonitorsite(monidex)) = - sum(distQ.*log(distQ));
if length(distQ) == 1
cptMeasure.relentropy(allmonitorsite(monidex)) = 1;
else
cptMeasure.relentropy(allmonitorsite(monidex)) = 1 + sum(distQ.*log(distQ))/log(length(distQ));
end
if simu == 1
distP0 = ones(1,length(true_cpt{k0}))/length(true_cpt{k0}); distQ0 = cptMeasure.distcpt(true_cpt{k0}, allmonitorsite(monidex)) + 1e-50;
cptMeasure.KL(allmonitorsite(monidex)) = sum(distP0.*log(distP0./distQ0'));
cptMeasure.accuracy(allmonitorsite(monidex)) = mean(tmpdistcpt(true_cpt{k0})) - carddiff/length(true_cpt{k0}); %+tmpdistcpt(true_cpt{k0}+1));
end
yvalueupperHPD = zeros(timesize,1);
yvaluelowerHPD = zeros(timesize,1);
yvaluelowerCI = zeros(timesize,1);
yvalueupperCI = zeros(timesize,1);
tmpYs2 = [];
for sss = 1:timesize,
[a,b] = FindHPDset(AllHPDsetdata(sss,:)',HPDp,[]);
yvaluelowerCI(sss) = quantile(AllHPDsetdata(sss,:)',(1-HPDp)/2);
yvalueupperCI(sss) = quantile(AllHPDsetdata(sss,:)',1-(1-HPDp)/2);
if (size(a,2)==1)
yvaluelowerHPD(sss) = a(1);
yvalueupperHPD(sss) = b(1);
else
tempLength = zeros(size(a,2),1);
for hindx = 1:size(a,2)
tempLength(hindx) = b(hindx)-a(hindx);
end
[maxv,maxindx] = max(tempLength);
yvaluelowerHPD(sss) = a(maxindx);
yvalueupperHPD(sss) = b(maxindx);
end
% for hpdi=1:size(a,2)
% plot(sss,(a(hpdi)),'^k'); hold on;
% plot(sss,(b(hpdi)),'vk'); hold on;
% tmpYs2 = [tmpYs2, a(hpdi), b(hpdi)];
% end
tmpYs2 = [tmpYs2, yvaluelowerCI(sss), yvalueupperCI(sss)];
end
[test,maxtimeIn] = max(totalTinterval(:,2));
psoutput.numTCounts{monidex} = totalTinterval; %total counts of MCsamples for each number of intervals
psoutput.NTimeInterals(monidex) = maxtimeIn;
B = Combination(maxtimeIn,timesize-maxtimeIn*MinNtimesw);
sigmatempvalue = cell(size(B,1),1); utempvalue=cell(size(B,1),1);
testIndex = zeros(size(B,1),1);
Beta1parameter = cell(size(B,1),1);
Beta0parameter = cell(size(B,1),1);
for ss = afteriter:totIter,
if (size(xalliter{ss,1}{c(ss,monidex),1}.NpointinTimeIn,2) == maxtimeIn),
% for those iterations with posterior mode of # of changepoints only
[xxxtmep,yyytemp] = min(sum((B-ones(size(B,1),1)*xalliter{ss,1}{c(ss,monidex),1}.NpointinTimeIn).^2,2));
sigmatempvalue{yyytemp,1} = [sigmatempvalue{yyytemp,1}; sigmatemp(ss,monidex)];
utempvalue{yyytemp,1} = [utempvalue{yyytemp,1}; (utemp(ss,monidex))];
testIndex(yyytemp) = testIndex(yyytemp)+1;
Beta1parameter{yyytemp,1} = [Beta1parameter{yyytemp,1}; (1./multipleconst).*(xalliter{ss,1}{c(ss,monidex),1}.parameter)];
if inc_int ~= 1 && inc_ints ~= 1
tmpBeta0parameter = (1./multipleconst).*(beta0s{maxtimeIn,allmonitorsite(monidex)}(yyytemp,:));
if estAlpha == 1
obj = computefromInt(xalliter{ss,1}{c(ss,monidex),1}.NpointinTimeIn);
if obj.n1 < timesize
tmpDelta = obsY - utemp(ss,monidex) - ...
(1./multipleconst).*obj.X1*xalliter{ss,1}{c(ss,monidex),1}.parameter';
tmpBeta0parameter(2:end) = mvnrnd(inv(obj.X0' * obj.X0)*obj.X0'*tmpDelta((obj.n1+1):end), sigmatemp(ss,monidex)*inv(obj.X0' * obj.X0));
end
end
Beta0parameter{yyytemp,1}=[Beta0parameter{yyytemp,1}; tmpBeta0parameter];
elseif inc_int == 1
Beta0parameter{yyytemp,1}=[Beta0parameter{yyytemp,1}; (1./multipleconst).*(xalliter{ss,1}{c(ss,monidex),1}.beta0)];
elseif inc_ints == 1
Beta0parameter{yyytemp,1}=[Beta0parameter{yyytemp,1}; (1./multipleconst).*(xalliter{ss,1}{c(ss,monidex),1}.beta0{yy(ss,monidex)})];
end
else
end
end
[val,ComIndex] = max(testIndex);
psoutput.numTintervalCounts{monidex} = [B testIndex];
psoutput.NpointinTimeIn{monidex,1} = B(ComIndex,:);
TimeInterval = [0 cumsum(B(ComIndex,:))];
%v1temp{monidex,1}=vls{maxtimeIn,allmonitorsite(monidex)}(ComIndex,:);
sigmavaltemp{monidex,1} = mean(sigmatempvalue{ComIndex,1});
uvaltemp{monidex,1} = mean(utempvalue{ComIndex,1});
beta1temp{monidex,1} = mean(Beta1parameter{ComIndex,1},1);
beta0temp{monidex,1} = mean(Beta0parameter{ComIndex,1},1);
psoutput.sigma2(monidex) = sigmavaltemp{monidex,1};
psoutput.u(monidex) = uvaltemp{monidex,1};
psoutput.theta{monidex,1} = beta1temp{monidex,1};
obj = computefromInt(B(ComIndex,:));
tmpSigma2 = mean(sigmatempvalue{ComIndex,1});
tmpDelta = obsY - mean(utempvalue{ComIndex,1}) - obj.X1*mean(Beta1parameter{ComIndex,1},1)';
% Dhat = Dhat - 0.5*(timesize - length(B(ComIndex,:)) + 1)*log(2*pi*tmpSigma2)...
% - 0.5*log(det(obj.X0' * obj.X0)) - 0.5/tmpSigma2*tmpDelta'*obj.Sigma*tmpDelta;
if inc_ints ~= 1
fitMeasure.Dhat(allmonitorsite(monidex)) = fitMeasure.Dhat(allmonitorsite(monidex)) - 0.5*timesize*log(2*pi*tmpSigma2) - 0.5/tmpSigma2*(sum(tmpDelta(1:obj.n1).^2) + ...
sum((tmpDelta((obj.n1+1):end) - obj.X0*beta0temp{monidex,1}(2:end)').^2));
else
fitMeasure.Dhat(allmonitorsite(monidex)) = fitMeasure.Dhat(allmonitorsite(monidex)) - 0.5*timesize*log(2*pi*tmpSigma2) - 0.5/tmpSigma2*(sum(tmpDelta(1:obj.n1).^2) + ...
sum((tmpDelta - obj.X0*beta0temp{monidex,1}').^2));
end
for j0 = 1:(numel( beta1temp{monidex,1}));
psoutput.thetaCI{monidex,1}{j0} = quantile(Beta1parameter{ComIndex,1}(:,j0),(1-HPDp)/2);
psoutput.thetaCI{monidex,2}{j0} = quantile(Beta1parameter{ComIndex,1}(:,j0),1-(1-HPDp)/2);
end
if (numel( beta1temp{monidex,1}) >1)
tempbeta = Beta1parameter{ComIndex,1};
for j0 = 1:(numel( beta1temp{monidex,1})- 1)
tempbetadiff = tempbeta(:,j0+1) - tempbeta(:,j0);
psoutput.betadifference{monidex,1}{j0} = mean(tempbetadiff);
[La,Lb] = FindHPDset(tempbetadiff,HPDp,[]);
psoutput.betadifferenceHPD{monidex,1}{j0} = La;
psoutput.betadifferenceHPD{monidex,2}{j0} = Lb;
psoutput.betadifferenceCI{monidex,1}{j0} = quantile(tempbetadiff,(1-HPDp)/2);
psoutput.betadifferenceCI{monidex,2}{j0} = quantile(tempbetadiff,1-(1-HPDp)/2);
if j0 == 1
cptMeasure.betadiff(allmonitorsite(monidex)) = mean(tempbetadiff);
cptMeasure.betabwCI(allmonitorsite(monidex)) = mean(psoutput.betadifferenceCI{monidex,2}{j0} - psoutput.betadifferenceCI{monidex,1}{j0});
cptMeasure.betabwHPD(allmonitorsite(monidex)) = mean(Lb - La);
end
end
end
% xlabel(['\sigma^2= ', num2str(sigmavaltemp{monidex,1}(1)),',time= ',num2str(TimeInterval(2:end-1)+1)])
xlabel(['(\sigma_s^2= ', num2str(sigmavaltemp{monidex,1}(1)),')'], 'Fontsize', 16)
% plot(1:timesize,yvalueupperHPD,'--r'); hold on;
% plot(1:timesize,yvaluelowerHPD,'--r');hold on;
plot(1:timesize,yvalueupperCI,'--r'); hold on;
plot(1:timesize,yvaluelowerCI,'--r');hold on;
Rdata.sigma2(monidex) = sigmavaltemp{monidex,1}(1);
Rdata.yvalueupperCI(:,monidex) = yvalueupperCI';
Rdata.yvaluelowerCI(:,monidex) = yvaluelowerCI';
%yvalue=[];
%yvaluec1=[];
%yvaluec2=[];
changetime = zeros(size(TimeInterval,2)-1,1);
tmpyvalue = [];
tmpsepredT = [];
tmpYs = []; tmpPred = [];
Rdata.cpt{monidex} = zeros(1,size(TimeInterval,2) - 1);
for i = 1:size(TimeInterval,2) - 1,
%timeaxis=[];
timeaxis = [TimeInterval(i)+1:1:TimeInterval(i+1)];
changetime(i) = TimeInterval(i+1);
yvalue = [timeaxis.*beta1temp{monidex,1}(i) + uvaltemp{monidex,1}+ beta0temp{monidex,1}(i)];
Xmat = [ones(size(timeaxis,2),1) timeaxis'];
sefit = sqrt(sigmavaltemp{monidex,1}).*sqrt(diag(Xmat*inv(Xmat'*Xmat)*Xmat'))'; % note sigmavaltemp = sigma^2
sepred = sqrt(sigmavaltemp{monidex,1}).*sqrt(1+diag(Xmat*inv(Xmat'*Xmat)*Xmat'))';
yvalueprec1 = yvalue-sepred.*tinv(pp/2,size(timeaxis,2)-2);
yvalueprec2 = yvalue+sepred.*tinv(pp/2,size(timeaxis,2)-2);
yvaluec1 = yvalue-sefit.*tinv(pp/2,size(timeaxis,2)-2);
yvaluec2 = yvalue+sefit.*tinv(pp/2,size(timeaxis,2)-2);
tmpyvalue = [tmpyvalue yvalue];
tmpsepredT = [tmpsepredT sepred.*tinv(pp/2,size(timeaxis,2)-2)];
if (1<i && i<=size(TimeInterval,2)-1),
plot([TimeInterval(i)+1, TimeInterval(i)+1],[-90, 90]); hold on;
else
end
Rdata.cpt{monidex}(i) = TimeInterval(i)+1;
%xlabel(['sigma= ', num2str(sigmavaltemp{monidex,1}(1)),',time= ',num2str(TimeInterval(2:end-1))])
% plot(timeaxis,(yvalue),'r'); % plot the predictive interval
% hold on;
% plot(timeaxis,(yvalueprec1),'g');
% hold on;
% plot(timeaxis,(yvalueprec2),'g');
% hold on;
tmpYs = [tmpYs, yvalue,yvalueprec1,yvalueprec2];
tmpPred = [tmpPred, yvalue];
%plot(timeaxis,yvaluec1,'g');
%hold on;
%plot(timeaxis,yvaluec2,'g');
end
tmpYs = [tmpYs, ((1./multipleconst).*alldata(2:end,allmonitorsite(monidex)))', tmpYs2];
ylim([min(tmpYs), max(tmpYs)+0.06]);
Rdata.miny(monidex) = min(tmpYs); Rdata.maxy(monidex) = max(tmpYs)+0.06;
tmpPred = tmpPred';
fitMeasure.Pred(:,allmonitorsite(monidex)) = tmpPred;
fitMeasure.SSE(allmonitorsite(monidex)) = sum((tmpPred - obsY).^2);
fitMeasure.MSE(allmonitorsite(monidex)) = sum((tmpPred - obsY).^2)/(timesize - 2*maxtimeIn);
fitMeasure.BIC(allmonitorsite(monidex)) = log(sum((tmpPred - obsY).^2)/timesize) + 2*maxtimeIn/timesize*log(timesize);
psoutput.yvalue{monidex,1}=tmpyvalue;
psoutput.sepredT{monidex,1}=tmpsepredT;
psoutput.changetime{monidex,1}=changetime;
psoutput.HPDsetdata{monidex,1}=AllHPDsetdata;
end
end
function [Clustergroup,SS,SS2] = makelinkage(xallIf,c)
global sitesize datachoice filename simu Clustergroup0 model rep
modellist = {'SRE (model 1)', 'NSE (model 2)', 'NRE (model 3)'};
if datachoice == 1
Name=char('Alabama','Arizona','Arkansas','California','Colorado','Connecticut','Delaware','Washington DC','Florida','Georgia','Idaho','Illinois','Indiana','Iowa','Kansas','Kentucky',...
'Louisiana','Maine','Maryland','Massachusetts','Michigan','Minnesota','Mississippi','Missouri','Montana','Nebraska','Nevada','New Hampshire ',...
'New Jersey','New Mexico','New York','North Carolina','North Dakota','Ohio','Oklahoma','Oregon','Pennsylvania','Rhode Island','South Carolina',...
'South Dakota','Tennessee','Texas','Utah','Vermont','Virginia','Washington','West Virginia','Wisconsin','Wyoming');
end
Niteration = size(xallIf,1);
PCount = zeros(sitesize,sitesize);
for k = 1:Niteration,
for j = 1:sitesize,
jsite = j;
Nstar = size(xallIf{k,1},1);
N = [];
for i = 1:Nstar,
N(i) = size(xallIf{k,1}{i,1}.Siteincluster,2);% N(i): the number of sites in cluster i.
if (min(abs(xallIf{k,1}{i,1}.Siteincluster-j))==0)
for ss=1:size(xallIf{k,1}{i,1}.Siteincluster,2),
PCount(j,xallIf{k,1}{i,1}.Siteincluster(ss))=PCount(j,xallIf{k,1}{i,1}.Siteincluster(ss))+1;
end
else
end
end
end
end
DissMat= 1-PCount./Niteration;
pdistvec=[];
for i=1:sitesize-1,
pdistvec = [pdistvec DissMat(i,i+1:end)];
end
clusterRe = linkage(pdistvec,'ward');
% dendrogram(clusterRe,sitesize, 'LABELS', Name);
if model ~= 3
[H,T] = dendrogram(clusterRe,sitesize, 'LABELS', Name,'orient','left','colorthreshold',1.5);set(H,'LineWidth',2);axis tight;
else
[H,T] = dendrogram(clusterRe,sitesize, 'LABELS', Name,'orient','left','colorthreshold',1.3);set(H,'LineWidth',2);axis tight;
end
title(strcat('Model=', modellist(model)),'Fontsize',15)
orient landscape
print('-painters', '-dpsc2', '-r600', '-append', filename)
% get the figure
[H,T] = dendrogram(clusterRe,sitesize, 'LABELS', Name,'orient','left','colorthreshold',1.3);
set(H,'LineWidth',2);axis tight;
title('')
set(gca,'FontSize',13)
orient landscape
print('-painters', '-dpsc2', '-r600', strcat('DendrogramM_',num2str(rep),'.eps'))
ClusterIndex = cluster(clusterRe,'maxclust',c);
Clustergroup = cell(c,1);
for i = 1:sitesize,
for j = 1:c
if ClusterIndex(i)==j,
Clustergroup{j,1} = [Clustergroup{j,1} i];
end
end
end
labs0 = zeros(1,sitesize);
for i = 1:size(Clustergroup);
labs0(Clustergroup{i}) = i;
end
n1p = 0; n2p = 0;
for i = 1:sitesize
for j = (i+1):sitesize
if labs0(i)==labs0(j)
n1p = n1p+1;
elseif labs0(i)~=labs0(j)
n2p = n2p+1;
end
end
end
Sensitivity = zeros(Niteration,1); Specificity = zeros(Niteration,1);
for k = 1:Niteration
labs = zeros(1,sitesize); Nstar = size(xallIf{k,1},1);
for i = 1:Nstar
labs(xallIf{k,1}{i,1}.Siteincluster) = i;
end
n11 = 0; n22 = 0;
for i = 1:sitesize
for j = (i+1):sitesize
if (labs(i)==labs(j)) && (labs0(i)==labs0(j))
n11 = n11+1;
elseif (labs(i)~=labs(j)) && (labs0(i)~=labs0(j))
n22 = n22+1;
end
end
end
Sensitivity(k) = n11/n1p; Specificity(k) = n22/n2p;
end
labsC = labs0;
SS = mean(2-Sensitivity-Specificity); % central
SS2 = []; % true
if simu == 1
subplot(1,2,1); % iteraton versus central
scatter(Sensitivity,Specificity,36,'k','filled'); xlim([0,1]); ylim([0,1]); grid on
xlabel('Sensitivity','FontSize',18); ylabel('Specificity','FontSize',18);
SensitivityC = Sensitivity; SpecificityC = Specificity;
% iteraton versus true
labs0 = zeros(1,sitesize);
for i = 1:size(Clustergroup0);
labs0(Clustergroup0{i}) = i;
end
n1p = 0; n2p = 0;
for i = 1:sitesize
for j = (i+1):sitesize
if labs0(i)==labs0(j)
n1p = n1p+1;
elseif labs0(i)~=labs0(j)
n2p = n2p+1;
end
end
end
Sensitivity = zeros(Niteration,1); Specificity = zeros(Niteration,1);
for k = 1:Niteration
labs = zeros(1,sitesize); Nstar = size(xallIf{k,1},1);
for i = 1:Nstar
labs(xallIf{k,1}{i,1}.Siteincluster) = i;
end
n11 = 0; n22 = 0;
for i = 1:sitesize
for j = (i+1):sitesize
if (labs(i)==labs(j)) && (labs0(i)==labs0(j))
n11 = n11+1;
elseif (labs(i)~=labs(j)) && (labs0(i)~=labs0(j))
n22 = n22+1;
end
end
end
Sensitivity(k) = n11/n1p; Specificity(k) = n22/n2p;
end
SS2 = mean(2-Sensitivity-Specificity);
subplot(1,2,2);
scatter(Sensitivity,Specificity,36,'k','filled'); xlim([0,1]); ylim([0,1]); grid on
hold on;
% get SS for C versus T
n11 = 0; n22 = 0;
for i = 1:sitesize
for j = (i+1):sitesize
if (labsC(i)==labsC(j)) && (labs0(i)==labs0(j))
n11 = n11+1;
elseif (labsC(i)~=labsC(j)) && (labs0(i)~=labs0(j))
n22 = n22+1;
end
end
end
SensitivityCT = n11/n1p; SpecificityCT = n22/n2p;
labsT = labs0;
else
scatter(Sensitivity,Specificity,36,'ko'); xlim([0,1]); ylim([0,1]); grid on % central
%title(strcat('Model=', modellist(model)),'Fontsize',15)
SS2 = []; %mean(2-Sensitivity-Specificity);
end
if simu == 0
save('SS.mat','Sensitivity','Specificity') % central
else
save(strcat('SS_',num2str(rep),'.mat'),'SensitivityC','SpecificityC','Sensitivity',...
'Specificity','labsC','labsT','SensitivityCT','SpecificityCT') % central, true
end
xlabel('Sensitivity','FontSize',18); ylabel('Specificity','FontSize',18);
title(strcat('max d = ',num2str(c)),'FontSize',18)
set(gca,'FontSize',16)
orient landscape
print('-painters', '-dpsc2', '-r600', 'SSc_real.eps')
end
function [out] = getDistance2(data, Clustergroup0, Clustergroup1)
global dataPath rep model MinNtimesw timesize sitesize simu
N = floor(timesize/MinNtimesw);
Name=char('Alabama','Arizona','Arkansas','California','Colorado','Connecticut','Delaware','Washington DC','Florida','Georgia','Idaho','Illinois','Indiana','Iowa','Kansas','Kentucky',...
'Louisiana','Maine','Maryland','Massachusetts','Michigan','Minnesota','Mississippi','Missouri','Montana','Nebraska','Nevada','New Hampshire ',...
'New Jersey','New Mexico','New York','North Carolina','North Dakota','Ohio','Oklahoma','Oregon','Pennsylvania','Rhode Island','South Carolina',...
'South Dakota','Tennessee','Texas','Utah','Vermont','Virginia','Washington','West Virginia','Wisconsin','Wyoming');
computP = 1;
if computP == 1
if simu == 0
% load(strcat(dataPath,'Ressandbeta',num2str(model),'_',num2str(rep),'.mat'))
load('Ressandbeta.mat')
else % for simulation I am going to calculate it here
Ressval = cell(N,sitesize);
beta0s = cell(N,sitesize);
beta1s = cell(N,sitesize);
for j = 1:sitesize
for i=1:N % the number of time intervals.
if i==1 %only one time inveral in the site j
sitedata = data(:,j); times = 1:timesize;
[Ressval{i,j},beta1s{i,j},beta0s{i,j}] = Ress(sitedata',times);
else
sitedata = data(:,j);
B = Combination(i,timesize-i*MinNtimesw);
m = size(B,1); k = size(B,2);
times = 1:timesize;
celly = cell(m,k);
timesinterval=cell(m,k);
for l=1:m
timesinterval(l,:) = mat2cell(times,1,B(l,:));
celly(l,:)= mat2cell(sitedata',1,B(l,:));
end
[Ressval{i,j},beta1s{i,j},beta0s{i,j}] = cellfun(@Ress,celly,timesinterval);
end
end
end
end
Dist = zeros(sitesize); count = 1; times = 1:timesize;
Info = cell(sitesize);
Hcps = cell(1,sitesize); Hbeta0s = cell(1,sitesize); Hbeta1s = cell(1,sitesize); HRa = zeros(1,sitesize);
for s1 = 1:sitesize % find changepoint measures
D0 = 1e4;
for i = 1:N
if i == 1
D = timesize*log(Ressval{i,s1}) + (2*i+1)*log(timesize);
if D<D0
D0 = D; Hcps{s1} = timesize; Hbeta0s{s1} = beta0s{i,s1}; Hbeta1s{s1} = beta1s{i,s1};
end
else
B = Combination(i,timesize-i*MinNtimesw);
m = size(B,1); k = size(B,2); D = 0;
for l = 1:m
D = timesize*log(sum(Ressval{i,s1}(l,:))) + (2*i+1)*log(timesize);
if D<D0
D0 = D; Hcps{s1} = B(l,:); Hbeta0s{s1} = beta0s{i,s1}(l,:); Hbeta1s{s1} = beta1s{i,s1}(l,:);
end
end
end
end
if simu == 1 % detect accuracy
load('true_cpt.mat')
for r = 1:length(Clustergroup0)
if any(Clustergroup0{r} == s1)
HRa(s1) = 0;
if length(Hcps{s1}) > 1
tmp0 = cumsum([1 Hcps{s1}]);
obs_cpt = tmp0(2:(length(tmp0)-1)); lens = length(obs_cpt);
for k = 1:lens