diff --git a/Data/createTrueData_simulated.m b/Data/createTrueData_simulated.m
index 85e521a80c495097f9655339313fd0bca0a585fd..0dcc9a6b51e300188a7e335dbb4438ff8c20c93b 100644
--- a/Data/createTrueData_simulated.m
+++ b/Data/createTrueData_simulated.m
@@ -129,9 +129,9 @@ exp.PV.sem = exp.PV.eRand;
% Data-based parameters
exp.parameters.Caa.eRand = simulatedDataTrue.parameters.Caa.mean.*0.15; % 15% error
exp.parameters.ELCo.eRand = simulatedDataTrue.parameters.ELCo.mean.*0.12; % 12% error
-exp.parameters.Ctot.eRand = simulatedDataTrue.parameters.Ctot.mean.*0.20; % 25%, calculated from other variables
-exp.parameters.Rtot.eRand = simulatedDataTrue.parameters.Rtot.mean.*0.082; % 12%, calculated from other variables
-exp.parameters.Emax_LV.eRand = simulatedDataTrue.parameters.Emax_LV.mean.*0.077; % 10%, calculated from other variables
+exp.parameters.Ctot.eRand = simulatedDataTrue.parameters.Ctot.mean.*0.20; % calculated from other variables
+exp.parameters.Rtot.eRand = simulatedDataTrue.parameters.Rtot.mean.*0.082; % calculated from other variables
+exp.parameters.Emax_LV.eRand = simulatedDataTrue.parameters.Emax_LV.mean.*0.077; % calculated from other variables
exp.parameters.Caa.eSyst = 0; % no systematic errors
exp.parameters.ELCo.eSyst = 0; % no systematic errors
diff --git a/Data/plotMeasurementErrors.m b/Data/plotMeasurementErrors.m
index dc976ad170ceb0970795cf41588465402d717570..92286b0166b943d68c88b023813698eb5404dc95 100644
--- a/Data/plotMeasurementErrors.m
+++ b/Data/plotMeasurementErrors.m
@@ -59,16 +59,16 @@ for n = 1:length(experimentNames)
SV_PV_randonly(n) = trapz(datan.MV.time, trueData.PV.mean + e.pv.random);
%add only offset errors and calculate stroke volume
- SV_MV_offsetonly(n) = trapz(datan.MV.time, trueData.MV.mean + e.mv.systematic );%+ e.RR.MV
- SV_AV_offsetonly(n) = trapz(datan.MV.time, trueData.AV.mean + e.av.systematic);% + e.RR.AV
- SV_AA_offsetonly(n) = trapz(datan.MV.time, trueData.AA.mean + e.aa.systematic);% + e.RR.AA
- SV_PV_offsetonly(n) = trapz(datan.MV.time, trueData.PV.mean + e.pv.systematic);% + e.RR.PV
+ SV_MV_offsetonly(n) = trapz(datan.MV.time, trueData.MV.mean + e.mv.systematic );
+ SV_AV_offsetonly(n) = trapz(datan.MV.time, trueData.AV.mean + e.av.systematic);
+ SV_AA_offsetonly(n) = trapz(datan.MV.time, trueData.AA.mean + e.aa.systematic);
+ SV_PV_offsetonly(n) = trapz(datan.MV.time, trueData.PV.mean + e.pv.systematic);
%add only systematic errors and calculate stroke volume
- SV_MV_systonly(n) = trapz(datan.MV.time, trueData.MV.mean + e.mv.systematic + e.RR.MV);%
- SV_AV_systonly(n) = trapz(datan.MV.time, trueData.AV.mean + e.av.systematic + e.RR.AV);%
- SV_AA_systonly(n) = trapz(datan.MV.time, trueData.AA.mean + e.aa.systematic + e.RR.AA);%
- SV_PV_systonly(n) = trapz(datan.MV.time, trueData.PV.mean + e.pv.systematic + e.RR.PV);%
+ SV_MV_systonly(n) = trapz(datan.MV.time, trueData.MV.mean + e.mv.systematic + e.RR.MV);
+ SV_AV_systonly(n) = trapz(datan.MV.time, trueData.AV.mean + e.av.systematic + e.RR.AV);
+ SV_AA_systonly(n) = trapz(datan.MV.time, trueData.AA.mean + e.aa.systematic + e.RR.AA);
+ SV_PV_systonly(n) = trapz(datan.MV.time, trueData.PV.mean + e.pv.systematic + e.RR.PV);
end
ctot_meanerr = mean(Ctoterr);
@@ -167,7 +167,7 @@ for f = 1:2
t=title(letters(letternum),'FontSize',12);
ax = gca;
- ax.TitleHorizontalAlignment = 'left';%TitleFontWeight
+ ax.TitleHorizontalAlignment = 'left';
pos = t.Position;
pos(1) = pos(1)-0.125;
t.Position = pos;
@@ -182,9 +182,9 @@ for f = 1:2
[resolutionError,postPrError,smoothingErrorReal,offsetError,allErrors,origFlow,randerrors] = flowerror(f,origflowcurves,origflownames,realRRerrors,rrdata,origTime,trueData);
% Plot all in one
RRpos = smoothingErrorReal;
- RRpos(RRpos<0) = NaN;%0;
+ RRpos(RRpos<0) = NaN;
RRneg = smoothingErrorReal;
- RRneg(RRneg>0) = NaN;%0;
+ RRneg(RRneg>0) = NaN;
nexttile
hold on
@@ -330,8 +330,14 @@ for f = 3:4
end
%% stroke volumes
-nexttile([2 1])
+swarmwidth = 0.25;
+grey = [0.65 0.65 0.65];
+black = [0.05 0.05 0.05];
+yellow = [0.5 0.5 0.8];
+
+nexttile
hold on
+yline(0,'-');
ylabel('Stroke volume error (%)')
mverrAbs_syst = SV_MV_systonly-trapz(trueData.MV.time,trueData.MV.mean);
averrAbs_syst = SV_AV_systonly-trapz(trueData.AV.time,trueData.AV.mean);
@@ -341,25 +347,24 @@ mverrAbs_systP = 100*mverrAbs_syst./trapz(trueData.MV.time,trueData.MV.mean);
averrAbs_systP = 100*averrAbs_syst./trapz(trueData.AV.time,trueData.AV.mean);
aaerrAbs_systP = 100*aaerrAbs_syst./trapz(trueData.AA.time,trueData.AA.mean);
pverrAbs_systP = 100*pverrAbs_syst./trapz(trueData.PV.time,trueData.PV.mean);
-swarmchart(1.2.*ones(size(mverrAbs_systP)),mverrAbs_systP,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
-swarmchart(2.2.*ones(size(mverrAbs_systP)),averrAbs_systP,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
-swarmchart(3.2.*ones(size(mverrAbs_systP)),aaerrAbs_systP,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
-ss=swarmchart(4.2.*ones(size(mverrAbs_systP)),pverrAbs_systP,2,[0.5 0.5 0],'*','XJitterWidth',0.10);
+swarmchart((1+swarmwidth).*ones(size(mverrAbs_systP)),mverrAbs_systP,2,yellow,'filled','XJitterWidth',swarmwidth)
+swarmchart((2+swarmwidth).*ones(size(mverrAbs_systP)),averrAbs_systP,2,yellow,'filled','XJitterWidth',swarmwidth)
+swarmchart((3+swarmwidth).*ones(size(mverrAbs_systP)),aaerrAbs_systP,2,yellow,'filled','XJitterWidth',swarmwidth)
+ss=swarmchart((4+swarmwidth).*ones(size(mverrAbs_systP)),pverrAbs_systP,2,yellow,'filled','XJitterWidth',swarmwidth);
mverr = 100*(SV_MV-trapz(trueData.MV.time,trueData.MV.mean))./trapz(trueData.MV.time,trueData.MV.mean);
averr = 100*(SV_AV-trapz(trueData.AV.time,trueData.AV.mean))./trapz(trueData.AV.time,trueData.AV.mean);
aaerr = 100*(SV_AA-trapz(trueData.AA.time,trueData.AA.mean))./trapz(trueData.AA.time,trueData.AA.mean);
pverr = 100*(SV_PV-trapz(trueData.PV.time,trueData.PV.mean))./trapz(trueData.PV.time,trueData.PV.mean);
-s=swarmchart(1.*ones(size(mverr)),mverr,2,[0.5 0.5 1],'*','XJitterWidth',0.10);
-swarmchart(2.*ones(size(mverr)),averr,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-swarmchart(3.*ones(size(mverr)),aaerr,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-swarmchart(4.*ones(size(mverr)),pverr,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-yline(0,'--');
+s=swarmchart(1.*ones(size(mverr)),mverr,2,grey,'filled','XJitterWidth',swarmwidth);
+swarmchart(2.*ones(size(mverr)),averr,2,grey,'filled','XJitterWidth',swarmwidth)
+swarmchart(3.*ones(size(mverr)),aaerr,2,grey,'filled','XJitterWidth',swarmwidth)
+swarmchart(4.*ones(size(mverr)),pverr,2,grey,'filled','XJitterWidth',swarmwidth)
-errorbar(1,mean(mverr),std(mverr),'k*','LineWidth',1,'markersize',5)
-errorbar(2,mean(averr),std(averr),'k*','LineWidth',1,'markersize',5)
-errorbar(3,mean(aaerr),std(aaerr),'k*','LineWidth',1,'markersize',5)
-er=errorbar(4,mean(pverr),std(pverr),'k*','LineWidth',1,'markersize',5);
+errorbar(1,mean(mverr),std(mverr),'.','color',black,'LineWidth',1,'markersize',5)
+errorbar(2,mean(averr),std(averr),'.','color',black,'LineWidth',1,'markersize',5)
+errorbar(3,mean(aaerr),std(aaerr),'.','color',black,'LineWidth',1,'markersize',5)
+er=errorbar(4,mean(pverr),std(pverr),'.','color',black,'LineWidth',1,'markersize',5);
mverrAbs_rand = SV_MV_randonly-trapz(trueData.MV.time,trueData.MV.mean);
averrAbs_rand = SV_AV_randonly-trapz(trueData.AV.time,trueData.AV.mean);
@@ -369,17 +374,23 @@ mverrAbs_randP = 100*mverrAbs_rand./trapz(trueData.MV.time,trueData.MV.mean);
averrAbs_randP = 100*averrAbs_rand./trapz(trueData.AV.time,trueData.AV.mean);
aaerrAbs_randP = 100*aaerrAbs_rand./trapz(trueData.AA.time,trueData.AA.mean);
pverrAbs_randP = 100*pverrAbs_rand./trapz(trueData.PV.time,trueData.PV.mean);
-swarmchart(1.4.*ones(size(mverrAbs_randP)),mverrAbs_randP,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
-swarmchart(2.4.*ones(size(mverrAbs_randP)),averrAbs_randP,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
-swarmchart(3.4.*ones(size(mverrAbs_randP)),aaerrAbs_randP,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
-sr=swarmchart(4.4.*ones(size(mverrAbs_randP)),pverrAbs_randP,2,[0.4 0 0.2],'*','XJitterWidth',0.10);
+swarmchart((1+swarmwidth*2).*ones(size(mverrAbs_randP)),mverrAbs_randP,2,black,'filled','XJitterWidth',swarmwidth)
+swarmchart((2+swarmwidth*2).*ones(size(mverrAbs_randP)),averrAbs_randP,2,black,'filled','XJitterWidth',swarmwidth)
+swarmchart((3+swarmwidth*2).*ones(size(mverrAbs_randP)),aaerrAbs_randP,2,black,'filled','XJitterWidth',swarmwidth)
+sr=swarmchart((4+swarmwidth*2).*ones(size(mverrAbs_randP)),pverrAbs_randP,2,black,'filled','XJitterWidth',swarmwidth);
-xticks([1:4])
+xticks([1:4]+swarmwidth)
xticklabels({'MV','AV','AA','PV'})
yline(5,'--');
yline(-5,'--');
+xline(2-swarmwidth,':');
+xline(3-swarmwidth,':');
+xline(4-swarmwidth,':');
set(gca,'FontSize',9)
-xlim([0.5 4.5])
+xlim([1-swarmwidth 4+swarmwidth*3])
+yticks(-30:10:30)
+ylim([-30,30])
+
t=title('E','FontSize',12);
ax = gca;
ax.TitleHorizontalAlignment = 'left';
@@ -387,9 +398,10 @@ pos = t.Position;
pos(1) = pos(1)-0.6;
t.Position = pos;
-
-nexttile([2 1])
+%----
+nexttile%([2 1])
hold on
+yline(0,'-');
ylabel('Stroke volume difference (%)')
diffMVAV_systonly = 100*(SV_MV_systonly-SV_AV_systonly)./mean([SV_MV_systonly,SV_AV_systonly],2);
diffMVAA_systonly = 100*(SV_MV_systonly-SV_AA_systonly)./mean([SV_MV_systonly,SV_AA_systonly],2);
@@ -397,12 +409,12 @@ diffMVPV_systonly = 100*(SV_MV_systonly-SV_PV_systonly)./mean([SV_MV_systonly,SV
diffAVAA_systonly = 100*(SV_AV_systonly-SV_AA_systonly)./mean([SV_AV_systonly,SV_AA_systonly],2);
diffAVPV_systonly = 100*(SV_AV_systonly-SV_PV_systonly)./mean([SV_AV_systonly,SV_PV_systonly],2);
diffAAPV_systonly = 100*(SV_AA_systonly-SV_PV_systonly)./mean([SV_AA_systonly,SV_PV_systonly],2);
-swarmchart(1.2.*ones(size(diffMVAV_systonly)),diffMVAV_systonly,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
-swarmchart(2.2.*ones(size(diffMVAV_systonly)),diffMVAA_systonly,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
-swarmchart(3.2.*ones(size(diffMVAV_systonly)),diffMVPV_systonly,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
-ss=swarmchart(4.2.*ones(size(diffMVAV_systonly)),diffAVAA_systonly,2,[0.5 0.5 0],'*','XJitterWidth',0.10);
-swarmchart(5.2.*ones(size(diffMVAV_systonly)),diffAVPV_systonly,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
-swarmchart(6.2.*ones(size(diffMVAV_systonly)),diffAAPV_systonly,2,[0.5 0.5 0],'*','XJitterWidth',0.10)
+swarmchart((1+swarmwidth).*ones(size(diffMVAV_systonly)),diffMVAV_systonly,2,yellow,'o','filled','XJitterWidth',swarmwidth)
+swarmchart((2+swarmwidth).*ones(size(diffMVAV_systonly)),diffMVAA_systonly,2,yellow,'o','filled','XJitterWidth',swarmwidth)
+swarmchart((3+swarmwidth).*ones(size(diffMVAV_systonly)),diffMVPV_systonly,2,yellow,'o','filled','XJitterWidth',swarmwidth)
+ss=swarmchart((4+swarmwidth).*ones(size(diffMVAV_systonly)),diffAVAA_systonly,2,yellow,'o','filled','XJitterWidth',swarmwidth);
+swarmchart((5+swarmwidth).*ones(size(diffMVAV_systonly)),diffAVPV_systonly,2,yellow,'o','filled','XJitterWidth',swarmwidth)
+swarmchart((6+swarmwidth).*ones(size(diffMVAV_systonly)),diffAAPV_systonly,2,yellow,'o','filled','XJitterWidth',swarmwidth)
diffMVAV = 100*(SV_MV-SV_AV)./mean([SV_MV,SV_AV],2);
diffMVAA = 100*(SV_MV-SV_AA)./mean([SV_MV,SV_AA],2);
@@ -411,19 +423,19 @@ diffAVAA = 100*(SV_AV-SV_AA)./mean([SV_AV,SV_AA],2);
diffAVPV = 100*(SV_AV-SV_PV)./mean([SV_AV,SV_PV],2);
diffAAPV = 100*(SV_AA-SV_PV)./mean([SV_AA,SV_PV],2);
-swarmchart(1.*ones(size(SV_MV)),100*(SV_MV-SV_AV)./SV_MV,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-swarmchart(2.*ones(size(SV_MV)),100*(SV_MV-SV_AA)./SV_MV,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-swarmchart(3.*ones(size(SV_MV)),100*(SV_MV-SV_PV)./SV_MV,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-swarmchart(4.*ones(size(SV_MV)), 100*(SV_AV-SV_AA)./SV_AV,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-swarmchart(5.*ones(size(SV_MV)), 100*(SV_AV-SV_PV)./SV_AV,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
-swarmchart(6.*ones(size(SV_MV)), 100*(SV_AA-SV_PV)./SV_AA,2,[0.5 0.5 1],'*','XJitterWidth',0.10)
+swarmchart(1.*ones(size(SV_MV)),100*(SV_MV-SV_AV)./SV_MV,2,grey,'filled','XJitterWidth',swarmwidth)
+swarmchart(2.*ones(size(SV_MV)),100*(SV_MV-SV_AA)./SV_MV,2,grey,'filled','XJitterWidth',swarmwidth)
+swarmchart(3.*ones(size(SV_MV)),100*(SV_MV-SV_PV)./SV_MV,2,grey,'filled','XJitterWidth',swarmwidth)
+swarmchart(4.*ones(size(SV_MV)), 100*(SV_AV-SV_AA)./SV_AV,2,grey,'filled','XJitterWidth',swarmwidth)
+swarmchart(5.*ones(size(SV_MV)), 100*(SV_AV-SV_PV)./SV_AV,2,grey,'filled','XJitterWidth',swarmwidth)
+swarmchart(6.*ones(size(SV_MV)), 100*(SV_AA-SV_PV)./SV_AA,2,grey,'filled','XJitterWidth',swarmwidth)
-errorbar(1,mean(diffMVAV),std(diffMVAV),'k*','LineWidth',1,'markersize',5)
-errorbar(2,mean(diffMVAA),std(diffMVAA),'k*','LineWidth',1,'markersize',5)
-errorbar(3,mean(diffMVPV),std(diffMVPV),'k*','LineWidth',1,'markersize',5)
-errorbar(4,mean(diffAVAA),std(diffAVAA),'k*','LineWidth',1,'markersize',5)
-errorbar(5,mean(diffAVPV),std(diffAVPV),'k*','LineWidth',1,'markersize',5)
-errorbar(6,mean(diffAAPV),std(diffAAPV),'k*','LineWidth',1,'markersize',5)
+errorbar(1,mean(diffMVAV),std(diffMVAV),'.','color',black,'LineWidth',1,'markersize',5)
+errorbar(2,mean(diffMVAA),std(diffMVAA),'.','color',black,'LineWidth',1,'markersize',5)
+errorbar(3,mean(diffMVPV),std(diffMVPV),'.','color',black,'LineWidth',1,'markersize',5)
+errorbar(4,mean(diffAVAA),std(diffAVAA),'.','color',black,'LineWidth',1,'markersize',5)
+errorbar(5,mean(diffAVPV),std(diffAVPV),'.','color',black,'LineWidth',1,'markersize',5)
+errorbar(6,mean(diffAAPV),std(diffAAPV),'.','color',black,'LineWidth',1,'markersize',5)
diffMVAV_randonly = 100*(SV_MV_randonly-SV_AV_randonly)./mean([SV_MV_randonly,SV_AV_randonly],2);
diffMVAA_randonly = 100*(SV_MV_randonly-SV_AA_randonly)./mean([SV_MV_randonly,SV_AA_randonly],2);
@@ -431,23 +443,30 @@ diffMVPV_randonly = 100*(SV_MV_randonly-SV_PV_randonly)./mean([SV_MV_randonly,SV
diffAVAA_randonly = 100*(SV_AV_randonly-SV_AA_randonly)./mean([SV_AV_randonly,SV_AA_randonly],2);
diffAVPV_randonly = 100*(SV_AV_randonly-SV_PV_randonly)./mean([SV_AV_randonly,SV_PV_randonly],2);
diffAAPV_randonly = 100*(SV_AA_randonly-SV_PV_randonly)./mean([SV_AA_randonly,SV_PV_randonly],2);
-swarmchart(1.4.*ones(size(diffMVAV_randonly)),diffMVAV_randonly,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
-swarmchart(2.4.*ones(size(diffMVAV_randonly)),diffMVAA_randonly,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
-swarmchart(3.4.*ones(size(diffMVAV_randonly)),diffMVPV_randonly,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
-sr=swarmchart(4.4.*ones(size(diffMVAV_randonly)),diffAVAA_randonly,2,[0.4 0 0.2],'*','XJitterWidth',0.10);
-swarmchart(5.4.*ones(size(diffMVAV_randonly)),diffAVPV_randonly,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
-swarmchart(6.4.*ones(size(diffMVAV_randonly)),diffAAPV_randonly,2,[0.4 0 0.2],'*','XJitterWidth',0.10)
+swarmchart((1+swarmwidth*2).*ones(size(diffMVAV_randonly)),diffMVAV_randonly,2,black,'filled','XJitterWidth',swarmwidth)
+swarmchart((2+swarmwidth*2).*ones(size(diffMVAV_randonly)),diffMVAA_randonly,2,black,'filled','XJitterWidth',swarmwidth)
+swarmchart((3+swarmwidth*2).*ones(size(diffMVAV_randonly)),diffMVPV_randonly,2,black,'filled','XJitterWidth',swarmwidth)
+sr=swarmchart((4+swarmwidth*2).*ones(size(diffMVAV_randonly)),diffAVAA_randonly,2,black,'filled','XJitterWidth',swarmwidth);
+swarmchart((5+swarmwidth*2).*ones(size(diffMVAV_randonly)),diffAVPV_randonly,2,black,'filled','XJitterWidth',swarmwidth)
+swarmchart((6+swarmwidth*2).*ones(size(diffMVAV_randonly)),diffAAPV_randonly,2,black,'filled','XJitterWidth',swarmwidth)
yline(5,'--');
-yline(-5,'--');
-yline(0,'--');
-
-xlim([0.5 6.5])
-xticks([1:6])
+line = yline(-5,'--');
+% yline(0,'--');
+xline(2-swarmwidth,':');
+xline(3-swarmwidth,':');
+xline(4-swarmwidth,':');
+xline(5-swarmwidth,':');
+xline(6-swarmwidth,':');
+
+xlim([1-swarmwidth 6+swarmwidth*3])
+xticks([1:6]+swarmwidth)
xticklabels({'MV-AV','MV-AA','MV-PV','AV-AA','AV-PV','AA-PV'})
-legend([s(1);er;sr(1);ss(1)],{'All errors','Mean+-sd of all','Random error',...
- 'Systematic error'},'location','northoutside','Numcolumns',2,...
- 'Position',[0.0872432573356074 0.284542914516151 0.4 0.04])
+yticks(-40:10:40)
+ylim([-40,40])
+legend([s(1);er;sr(1);ss(1);line],{'All errors','Mean+-sd of all errors','Random errors',...
+ 'Systematic errors','5% error'},'location','northoutside','Numcolumns',2,...
+ 'Position',[0.0461550220414897 0.11602368374692 0.4 0.04])
set(gca,'FontSize',9)
t=title('F','FontSize',12);
ax = gca;
diff --git a/Plotting/plotPL_realdata.m b/Plotting/plotPL_realdata.m
index ce89df75973f52910b30ef9458588ff618ea4ea7..b2eb9025eacb67aa04db4a29c9a181e1374ec1f2 100644
--- a/Plotting/plotPL_realdata.m
+++ b/Plotting/plotPL_realdata.m
@@ -34,7 +34,7 @@ paramNamesPlotNice = {'Caa', 'Rao','m2_L_V','ksystLV','EmaxLA','Cpvc'};
experimentNames = {'E_dataP78','E_dataP1','E_dataP3','E_dataP24','E_dataP36','E_dataP33'};
-plotNames = {'Control 1','Control 2','Control 3','T2D+HT 1','T2D+HT 2','T2D+HT 3'};
+plotNames = {'C1','C2','C3','P1','P2','P3'};
units.param = {'ml/mmHg','mmHg*s/ml','mmHg*s/ml','mmHg*s^2/ml','mmHg*s/ml',...
'ml/mmHg','cm^2','ml/mmHg','ml/mmHg','ml/mmHg','ml/mmHg','ml/mmHg',...
@@ -156,7 +156,6 @@ for i = 1:length(paramNamesPlot)
end
if lb(p,e) > ub(p,comp) || ub(p,e) < lb(p,comp) %no overlapping confidence intervals, definetely different between these subjects
signp{e,comp} = '**';
- % sign(comp,e) = '**';
elseif middlep > ub(p,comp) || middlep < lb(p,comp) %less than half overlapping, probably different (~ p=0.05)
signp{e,comp} = '*';
else % overlapping more than half, probably not different ( ~ p > 0.05)
@@ -168,6 +167,7 @@ for i = 1:length(paramNamesPlot)
end
%% Plot results figure for publication
+linespace=0.05;
darkpurple = [0.9 0.5 0.9].*0.4;
lightpurple = [1 0.7 1];
l = length(experimentNames);
@@ -177,25 +177,35 @@ letters = 'A':'Z';
figure('Name','Fig7_ProfileLikelihood_clinicalexample')
set(gcf,'Color','white')
xdim_CM = 17;
-ydim_CM = 12+5+3;
+ydim_CM = 12+3;
set(gcf,'Units','centimeters','Position',[0 0 xdim_CM ydim_CM])
set(gcf,'PaperUnits', 'centimeters', 'PaperSize', [xdim_CM, ydim_CM])
-tiledlayout(4,6,'TileSpacing','loose','Padding','compact')
+tiledlayout(3,12,'TileSpacing','loose','Padding','compact')
+%example
+nexttile([1,3])
+axis('off')
+
+% results
for i = 1:length(paramNamesPlot)
p = find(ismember(paramNames,paramNamesPlot{i}));
+ if i == 4
+ nexttile([1,3]);
+ axis('off')
+ end
ax1=nexttile([1,3]);
hold on
ylabel([paramNamesPlotNice{i} ' (' units.param{p} ')'])
for e = 1:length(experimentNames)
+ xl=xline(e,':','Color',[0.1 0.1 0.1],'linewidth',0.8);
middlep = lb(p,e)+ ((ub(p,e)-lb(p,e))/2);
sd = (ub(p,e)-middlep) / 1.96; %95% conf interval corresponds to 1.96 sd
if e > 3 % patient
- errorbar(e,middlep,sd,'.','color',purplegradient(end,:),'linewidth',2)
- plot(e,bestparam(e,p),'o','color',purplegradient(end,:),'MarkerFaceColor',purplegradient(end,:),'MarkerSize',3)
+ pe=errorbar(e,middlep,sd,'.','color',darkpurple,'linewidth',1.1,'markersize',1);
+ pb=plot(e,bestparam(e,p),'o','color',darkpurple,'MarkerFaceColor',darkpurple,'MarkerSize',3);
else % control
- errorbar(e,middlep,sd,'.','color',purplegradient(1,:),'linewidth',2)
- plot(e,bestparam(e,p),'o','color',purplegradient(1,:),'MarkerFaceColor',purplegradient(1,:),'MarkerSize',3)
+ ce=errorbar(e,middlep,sd,'.','color',lightpurple.*0.9,'linewidth',1.1,'markersize',1);
+ cb=plot(e,bestparam(e,p),'o','color',lightpurple.*0.9,'MarkerFaceColor',lightpurple.*0.9,'MarkerSize',3);
end
end
xticks(1:length(plotNames))
@@ -215,17 +225,24 @@ for i = 1:length(paramNamesPlot)
y1 = min(lb(p,:))*0.95;
end
yticks([y1 y2]);
- yd = 0.05*(length(comps)-1) + 1;
+ s=strcmp(sign{i},'**');
+ numsign = sum(s(:));
+ yd = linespace*(numsign) + 1 + (linespace*0.9)*length(experimentNames);
ymax = max(ub(p,:))*(yd+0.02)*1.01;
ymax = max(max(ub(p,:))*1.05,ymax);
ylim([min(lb(p,:))*0.95,ymax])
%significance
+ yd=1;
+ startingc = 1;
for c = 1:length(comps)
- yd = 0.05*(c-1) + 1;
if strcmp(sign{i}(comps{c}(1),comps{c}(2)),'**')
- plot(comps{c},[max(ub(p,:))*yd,max(ub(p,:))*yd],'k-')
- plot(mean(comps{c}),max(ub(p,:))*(yd+0.02),'k*','markersize',4,'linewidth',0.5)
+ if startingc ~= comps{c}(1)
+ yd = yd+linespace;
+ end
+ startingc = comps{c}(1);
+ yd = yd+linespace;
+ psign=plot(comps{c},[max(ub(p,:))*yd,max(ub(p,:))*yd],'k-');
end
end
set(gca,'FontSize',9,'FontName','Calibri')
@@ -233,33 +250,37 @@ for i = 1:length(paramNamesPlot)
ax1.TitleHorizontalAlignment = 'left';
if i ==1
- legend({'Standard deivation (sd)','Best fit to data','No overlapping CI'},'Location','Northwest')
+ legend([xl,cb,ce,pb,pe,psign],...
+ {sprintf('C: control\nP: HT+T2D'),'Standard deivation (C)','Best fit to data (C)','Standard deivation (P)','Best fit to data (P)','No overlapping CI'},...
+ 'Position',[0.02 0.415931811460952 0.2 0.19],'box','off')
end
end
lettersend =i;
+
% percent plot
green = [0 0.55 0.35];
for i = 1:length(paramNamesPlot)
p = find(ismember(paramNames,paramNamesPlot{i}));
- ax1=nexttile;
+ ax1=nexttile([1 2]);
hold on
meanbestparam = mean(bestparam(:,p));
middlep = lb(p,:)+ ((ub(p,:)-lb(p,:))./2);
sd = (ub(p,:)-middlep) / 1.96; %95% conf interval corresponds to 1.96 sd
percP = 100* (sd./meanbestparam );
- bar([perc(i),percAll(i)],'FaceColor',green)
- errorbar([perc(i),percAll(i)],[std(percP),0],'k.')
+ b1=bar(1,perc(i),'FaceColor',[184 84 184]./255);
+ b2=bar(2,percAll(i),'FaceColor',green);
+
+ e1=errorbar([perc(i),percAll(i)],[std(percP),0],'k.');
ylabel(['% of mean ' paramNamesPlotNice{i}])
- xticks([1,2])
- xticklabels({'Individual sd','Cohort sd'})
+ xticks([])
set(gca,'FontSize',9,'FontName','Calibri')
title(letters(i+lettersend),'FontSize',11,'FontName','Calibri')
ax1.TitleHorizontalAlignment = 'left';
end
-
+legend([b1,e1,b2],{sprintf('Individual\nstandard\ndeviation\n '),sprintf('Variation\namong\nindividuals\n '),sprintf('Cohort\nstandard\ndeviation\n ')},'Position',[0.925 0.12 0.05 0.23],'box','off')
%% Save and close all figures
if saveFigs
diff --git a/Plotting/plotProfileLikelihood.m b/Plotting/plotProfileLikelihood.m
index 69eff63c725b6766b6e85eb39a625d39a926add1..a95060f304590eb8f653aa4c5288ca393964423c 100644
--- a/Plotting/plotProfileLikelihood.m
+++ b/Plotting/plotProfileLikelihood.m
@@ -222,8 +222,8 @@ darkpurple = [0.9 0.5 0.9].*0.4;
lightpurple = [1 0.7 1];
purplegradient = [linspace(lightpurple(1),darkpurple(1),l)', linspace(lightpurple(2),darkpurple(2),l)', linspace(lightpurple(3),darkpurple(3),l)'];
-selectedParamNames = {'k_diast_LA','m2_LV','Caa'};
-selectedParamNamesPlot = {'k_d_i_a_s_t__L_A','m2_L_V','Caa'};
+selectedParamNames = {'Emin_LA','m2_LV','Caa'};
+selectedParamNamesPlot = {'Emin_L_A','m2_L_V','Caa'};
figure('Name',['Fig6_methodevaluation_summary_' experimentName])
set(gcf,'Color','white')
xdim_CM = 17;
@@ -262,7 +262,7 @@ for i = 1:length(selectedParamNamesPlot)
end
end
xline(trueData.allParameters(p),'--','LineWidth',1.2,'color',[0 0.5 0]);
- xlabel([paramNamesPlot{ismember(paramNamesPlot,paramNames(p))} ' (value)'])
+ xlabel([selectedParamNamesPlot{ismember(selectedParamNames,paramNames(p))} ' (value)'])
ylabel('Sampled data n.')
% full PL
@@ -283,7 +283,7 @@ for i = 1:length(selectedParamNamesPlot)
end
end
truep = xline(trueData.allParameters(p),'--','LineWidth',1.2,'color',[0 0.5 0]);
- xlabel([paramNamesPlot{ismember(paramNamesPlot,paramNames(p))} ' (value)'])
+ xlabel([selectedParamNamesPlot{ismember(selectedParamNames,paramNames(p))} ' (value)'])
ylabel('Obj. func value')
end
legend([okparamp,notokparamp,truep],sprintf('Found true\nvalue'),sprintf('Didn''t find\n true value'),'True value',...
diff --git a/Plotting/plotSampledData.m b/Plotting/plotSampledData.m
index eba7993ff322548771756d56fa0060dea9c73604..41060488baa0c8e8ee47ae0c1e7fc62615d1b577 100644
--- a/Plotting/plotSampledData.m
+++ b/Plotting/plotSampledData.m
@@ -37,12 +37,12 @@ greenCol = [0 0 0];
figure('Name',sprintf('True simulated data + %d sampled (%s)',length(estimationData),experimentName))
set(gcf,'Color','white')
xdim_CM = 17;
-ydim_CM = 17;
+ydim_CM = 11;
set(gcf,'Units','centimeters','Position',[0 0 xdim_CM ydim_CM])
set(gcf,'PaperUnits', 'centimeters', 'PaperSize', [xdim_CM, ydim_CM])
-tiledlayout(3,6,'TileSpacing','loose','Padding','compact')
+tiledlayout(4,6,'TileSpacing','compact','Padding','compact')
-nexttile([1,3])
+nexttile([2,2])
hold on
for i = 1:length(estimationData)
plot(estimationData{i}.MV.time,estimationData{i}.MV.mean,'.-','color',colors(i,:),'Markersize',12,'LineWidth',1)
@@ -52,7 +52,7 @@ ylabel('Blood flow in MV (mL/s)')
xlabel('Time (s)')
tend = trueData.MV.time(end);
xlim([0 tend])
-set(gca,'FontSize',10)
+set(gca,'FontSize',8)
t=title('A','FontSize',12);
ax = gca;
ax.TitleHorizontalAlignment = 'left';
@@ -60,7 +60,7 @@ pos = t.Position;
pos(1) = pos(1)-0.16;
t.Position = pos;
-nexttile([1,3])
+nexttile([2,2])
hold on
for i = 1:length(estimationData)
est(i)=plot(estimationData{i}.AV.time,estimationData{i}.AV.mean,'.-','color',colors(i,:),'Markersize',12,'LineWidth',1);
@@ -70,10 +70,10 @@ ylabel('Blood flow in AV (mL/s)')
xlabel('Time (s)')
n = split(sprintf('Sampled data %dx',1:length(estimationData)),'x');
legend([est,trueplot],[n(1:end-1);{'True data'}], ...
- 'Position',[0.75395269610321 0.754276960784314 0.22 0.23])
+ 'box','off','NumColumns',2,'Position',[0.65 0.798 0.33 0.2])
xlim([0 tend])
-set(gca,'FontSize',10)
+set(gca,'FontSize',8)
t=title('B','FontSize',12);
ax = gca;
ax.TitleHorizontalAlignment = 'left';
@@ -81,16 +81,63 @@ pos = t.Position;
pos(1) = pos(1)-0.16;
t.Position = pos;
-nexttile([1,3])
+nexttile
+axis off
+nexttile
+axis off
+%---
+
+letters = {'i','ii','iii','iv','v','vi'};
+nexttile
+hold on
+for i = 1:step:length(estimationData)
+ plot(1,estimationData{i}.SBP.mean,'.','color',colors(i,:),'linewidth',0.9,'Markersize',14)
+ plot(2,estimationData{i}.DBP.mean,'.','color',colors(i,:),'linewidth',0.9,'Markersize',14)
+end
+plot(1,trueData.SBP.mean,'o','color',greenCol,'linewidth',2,'Markersize',5)
+plot(2,trueData.DBP.mean,'o','color',greenCol,'linewidth',2,'Markersize',5)
+ylabel(sprintf('Blood pressure\n (mmHg)'))
+xticks([1,2])
+xticklabels({'SBP','DBP'})
+xlim([0 3])
+set(gca,'FontSize',8)
+t=title(letters{1},'FontSize',12);
+ax = gca;
+ax.TitleHorizontalAlignment = 'left';
+pos = t.Position;
+pos(1) = pos(1)-0.31;
+t.Position = pos;
+
+pnames = fieldnames(trueData.parameters);
+p = 1;
+nexttile
+hold on
+for i = 1:length(estimationData)
+ plot(p,estimationData{i}.parameters.(pnames{p}).mean,'.','color',colors(i,:),'linewidth',1,'Markersize',14)
+end
+plot(p,trueData.parameters.(pnames{p}).mean,'o','color',greenCol,'linewidth',2,'Markersize',5)
+ylabel(pnames{p})
+xticks([])
+set(gca,'FontSize',8)
+t=title(letters(p+1),'FontSize',12);
+ax = gca;
+ax.TitleHorizontalAlignment = 'left';
+pos = t.Position;
+pos(1) = pos(1)-1.5;
+t.Position = pos;
+
+%---
+
+nexttile([2,2])
hold on
for i = 1:length(estimationData)
- plot(estimationData{i}.AA.time,estimationData{i}.AA.mean,'.-','color',colors(i,:),'Markersize',12,'LineWidth',1)
+ plot(estimationData{i}.AA.time,estimationData{i}.AA.mean,'.-','color',colors(i,:),'Markersize',12,'LineWidth',1)
end
plot(trueData.AA.time,trueData.AA.mean,'o','color',greenCol,'linewidth',2,'Markersize',5)
ylabel('Blood flow in AA (mL/s)')
xlabel('Time (s)')
xlim([0 tend])
-set(gca,'FontSize',10)
+set(gca,'FontSize',8)
t=title('C','FontSize',12);
ax = gca;
ax.TitleHorizontalAlignment = 'left';
@@ -98,7 +145,7 @@ pos = t.Position;
pos(1) = pos(1)-0.16;
t.Position = pos;
-nexttile([1,3])
+nexttile([2,2])
hold on
for i = 1:length(estimationData)
plot(estimationData{i}.PV.time,estimationData{i}.PV.mean,'.-','color',colors(i,:),'Markersize',12,'LineWidth',1)
@@ -107,7 +154,7 @@ plot(trueData.PV.time,trueData.PV.mean,'o','color',greenCol,'linewidth',2,'Marke
ylabel('Blood flow in PV (mL/s)')
xlabel('Time (s)')
xlim([0 tend])
-set(gca,'FontSize',10)
+set(gca,'FontSize',8)
t=title('D','FontSize',12);
ax = gca;
ax.TitleHorizontalAlignment = 'left';
@@ -116,29 +163,9 @@ pos(1) = pos(1)-0.16;
t.Position = pos;
-nexttile
-hold on
-for i = 1:step:length(estimationData)
- plot(1,estimationData{i}.SBP.mean,'.','color',colors(i,:),'linewidth',0.9,'Markersize',14)
- plot(2,estimationData{i}.DBP.mean,'.','color',colors(i,:),'linewidth',0.9,'Markersize',14)
-end
-plot(1,trueData.SBP.mean,'o','color',greenCol,'linewidth',2,'Markersize',5)
-plot(2,trueData.DBP.mean,'o','color',greenCol,'linewidth',2,'Markersize',5)
-ylabel('Blood pressure (mmHg)')
-xticks([1,2])
-xticklabels({'SBP','DBP'})
-xlim([0 3])
-set(gca,'FontSize',10)
-t=title('E','FontSize',12);
-ax = gca;
-ax.TitleHorizontalAlignment = 'left';
-pos = t.Position;
-pos(1) = pos(1)-0.31;
-t.Position = pos;
+%----
-pnames = fieldnames(trueData.parameters);
-letters = 'F':'Z';
-for p = 1:length(pnames)
+for p = 2:length(pnames)
nexttile
hold on
for i = 1:length(estimationData)
@@ -147,8 +174,8 @@ for p = 1:length(pnames)
plot(p,trueData.parameters.(pnames{p}).mean,'o','color',greenCol,'linewidth',2,'Markersize',5)
ylabel(pnames{p})
xticks([])
- set(gca,'FontSize',10)
- t=title(letters(p),'FontSize',12);
+ set(gca,'FontSize',8)
+ t=title(letters(p+1),'FontSize',12);
ax = gca;
ax.TitleHorizontalAlignment = 'left';
pos = t.Position;