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summaryMinis.m
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cleanUp
params
testType = {'test1'; 'testSelect'; 'test2'};
%% Analyses
for iTest = 1:numel(testType)
% minis
if strcmpi(testType{iTest}, 'test1')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_minis');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_minis');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_minis');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_minis');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_minis');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_minis');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_minis');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_minis');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_minis');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_minis');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_minis');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_minis');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_minis');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_minis');
targetDir = performanceTestDir1;
elseif strcmpi(testType{iTest}, 'testSelect')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_minis_select');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_minis_select');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_minis_select');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_minis_select');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_minis_select');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_minis_select');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_minis_select');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_minis_select');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_minis_select');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_minis_select');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_minis_select');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_minis_select');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_minis_select');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_minis_select');
targetDir = performanceTestDirSelect;
elseif strcmpi(testType{iTest}, 'test2')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_minis2');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_minis2');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_minis2');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_minis2');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_minis2');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_minis2');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_minis2');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_minis2');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_minis2');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_minis2');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_minis2');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_minis2');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_minis2');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_minis2');
targetDir = performanceTestDir2;
end
[fH1, fH2, dPrimeMeanAllMinis, ~, ~, aucAllMinis, ~, ~, pHAUC(1), pHdPrime(1), simEventCount] = summaryPerformance(folder, [], [], matlabColours(8), testType{iTest});
distType = 'nearest';
[figures, legends, nHitsPDFPerRecordingMinis, nFalseAlarmsPDFPerRecordingMinis, centresMinis, centresMinisFA] = summaryDistance(folder, [], [], matlabColours(8), distType);
% Mini Analysis
if strcmpi(testType{iTest}, 'test1')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_MA');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_MA');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_MA');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_MA');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_MA');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_MA');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_MA');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_MA');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_MA');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_MA');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_MA');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_MA');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_MA');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_MA');
targetDir = performanceTestDir1;
elseif strcmpi(testType{iTest}, 'testSelect')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_MA_select');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_MA_select');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_MA_select');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_MA_select');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_MA_select');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_MA_select');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_MA_select');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_MA_select');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_MA_select');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_MA_select');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_MA_select');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_MA_select');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_MA_select');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_MA_select');
targetDir = performanceTestDirSelect;
elseif strcmpi(testType{iTest}, 'test2')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_MA2');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_MA2');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_MA2');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_MA2');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_MA2');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_MA2');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_MA2');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_MA2');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_MA2');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_MA2');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_MA2');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_MA2');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_MA2');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_MA2');
targetDir = performanceTestDir2;
end
[fH1, fH2, dPrimeMeanAllMiniAnalysis, ~, ~, aucAllMiniAnalysis, ~, ~, pHAUC(2), pHdPrime(2)] = summaryPerformance(folder, fH1, fH2, 'r', testType{iTest});
[figures, legends, nHitsPDFPerRecordingMiniAnalysis, nFalseAlarmsPDFPerRecordingMiniAnalysis, centresMiniAnalysis, centresMiniAnalysisFA] = summaryDistance(folder, figures, legends, 'r', distType);
% pClamp_raw
if strcmpi(testType{iTest}, 'test1')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_pC_raw');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_pC_raw');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_pC_raw');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_pC_raw');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_pC_raw');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_pC_raw');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_pC_raw');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_pC_raw');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_pC_raw');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_pC_raw');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_pC_raw');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_pC_raw');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_pC_raw');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_pC_raw');
targetDir = performanceTestDir1;
elseif strcmpi(testType{iTest}, 'testSelect')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_pC_raw_select');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_pC_raw_select');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_pC_raw_select');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_pC_raw_select');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_pC_raw_select');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_pC_raw_select');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_pC_raw_select');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_pC_raw_select');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_pC_raw_select');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_pC_raw_select');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_pC_raw_select');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_pC_raw_select');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_pC_raw_select');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_pC_raw_select');
targetDir = performanceTestDirSelect;
elseif strcmpi(testType{iTest}, 'test2')
folder{1} = fullfile(dataRepo,recFolder,'p103a','mat_pC_raw2');
folder{2} = fullfile(dataRepo,recFolder,'p106b','mat_pC_raw2');
folder{3} = fullfile(dataRepo,recFolder,'p108a','mat_pC_raw2');
folder{4} = fullfile(dataRepo,recFolder,'p108b','mat_pC_raw2');
folder{5} = fullfile(dataRepo,recFolder,'p108c','mat_pC_raw2');
folder{6} = fullfile(dataRepo,recFolder,'p120b','mat_pC_raw2');
folder{7} = fullfile(dataRepo,recFolder,'p122a','mat_pC_raw2');
folder{8} = fullfile(dataRepo,recFolder,'p124b','mat_pC_raw2');
folder{9} = fullfile(dataRepo,recFolder,'p125a','mat_pC_raw2');
folder{10} = fullfile(dataRepo,recFolder,'p127c','mat_pC_raw2');
folder{11} = fullfile(dataRepo,recFolder,'p128c','mat_pC_raw2');
folder{12} = fullfile(dataRepo,recFolder,'p129a','mat_pC_raw2');
folder{13} = fullfile(dataRepo,recFolder,'p131a','mat_pC_raw2');
folder{14} = fullfile(dataRepo,recFolder,'p131c','mat_pC_raw2');
targetDir = performanceTestDir2;
end
[fH1, fH2, dPrimeMeanAllpClampRaw2, ~, ~, aucAllpClampRaw2, ~, ~, pHAUC(3), pHdPrime(3)] = summaryPerformance(folder, fH1, fH2, 'm', testType{iTest});
[figures, legends, nHitsPDFPerRecordingpClampRaw2, nFalseAlarmsPDFPerRecordingpClampRaw2, centrespClampRaw2, centrespClampRaw2FA] = summaryDistance(folder, figures, legends, 'm', distType);
%% Calculate simulated event frequencies for different conditions
durations = [200 200 100 100 100 100 100 100 100 100 100 100 100 100]';
frequency = simEventCount./durations;
meanFrequency = datamean(frequency);
%% Figures
% Common parameters
figFolder = targetDir;
yTickLabels = {'minis', 'MiniAnalysis', 'Clampfit'};
legendString = {'minis', 'Mini Analysis', 'Clampfit'};
colourCodes = {matlabColours(8),'r','m'};
figSize = 17;
%% 1. ROC figure
figure(fH1);
hold on
plot([0 1], [0 1], 'k:')
hold off
legend(pHAUC, legendString, 'Location','SouthEast');
legend boxoff
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'FPR', xlim, 0:0.2:1,...
'on', 'k', 'TPR', ylim, 0:0.2:1);
set(fH1, 'color','w');
figName = 'ROC';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(fH1, gca, width, height, label, margin, 0);
if ~exist(figFolder,'dir')
mkdir(figFolder);
end
hgsave(fH1, [figFolder filesep figName '.fig']);
exportFig(fH1, [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(fH1, [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(fH1);
%% 2. d' figure
figure(fH2);
%legend(pHdPrime, legendString, 'Location','NorthEast');
%legend boxoff
if strcmpi(testType{iTest}, 'test1')
yLim = [0 3.75];
xLim = [0.5 14.5];
xTicks = xticks;
elseif strcmpi(testType{iTest}, 'testSelect')
yLim = [0.5 3];
xLim = [0.5 3.5];
xTicks = [1 2 3];
else
yLim = [0.5 1.7];
xLim = [0.5 3.5];
xTicks = [1 2 3];
end
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'ROC condition from left to right', xLim, xTicks,...
'on', 'k', 'd''', yLim, yticks);
set(fH2, 'color','w');
figName = 'dPrime';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(fH2, gca, width, height, label, margin, 0);
hgsave(fH2, [figFolder filesep figName '.fig']);
exportFig(fH2, [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(fH2, [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(fH2);
if strcmpi(distType,'nearest')
figure(figures(1));
legend(legendString);
xlabel('Time to neighbour (ms)');
ylabel('TPR');
title('PDF (nearest): Probability of mini detection as a function of time to the nearest neighbour');
figure(figures(2));
legend(legendString);
xlabel('Time to neighbour (ms)');
ylabel('Cumulative TPR');
title('CDF (nearest): Cumulative probability of mini detection as a function of time to the nearest neighbour');
% 5
figure(figures(3));
legend(legends{3},legendString, 'Location','NorthWest');
legend boxoff
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'TPR', [0 1], 0:0.2:1);
xticklabels({'0.1','1','10','100'});
%yticklabels({'0.1','0.5','1'});
set(figures(3), 'color','w');
figName = 'distancePDF';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(figures(3), gca, width, height, label, margin, 0);
hgsave(figures(3), [figFolder filesep figName '.fig']);
exportFig(figures(3), [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(figures(3), [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(figures(3));
% 6
figure(figures(4));
%legend(legends{4},legendString, 'Location','NorthWest');
%legend boxoff
if strcmpi(testType{iTest},'test1')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'Cumulative TPR', [0 0.275], 0:0.05:0.3);
else
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'Cumulative TPR', [0 0.715], 0:0.1:0.7);
end
xticklabels({'0.1','1','10','100'});
%yticklabels({'0.006','0.01','0.1','0.3'});
set(figures(4), 'color','w');
figName = 'distanceCDF';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(figures(4), gca, width, height, label, margin, 0);
hgsave(figures(4), [figFolder filesep figName '.fig']);
exportFig(figures(4), [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(figures(4), [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(figures(4));
% 7
figure(figures(5));
%legend(legends{5},legendString, 'Location','NorthWest');
%legend boxoff
if strcmpi(testType{iTest},'test1')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'FPR', [0 0.375], 0:0.1:1);
elseif strcmpi(testType{iTest},'testSelect')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'FPR', [0 0.375], 0:0.1:1);
elseif strcmpi(testType{iTest},'test2')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'FPR', [0 0.52], 0:0.1:1);
end
xticklabels({'0.1','1','10','100'});
%yticklabels({'0.1','0.5','1'});
set(figures(5), 'color','w');
figName = 'distancePDF_fa';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(figures(5), gca, width, height, label, margin, 0);
hgsave(figures(5), [figFolder filesep figName '.fig']);
exportFig(figures(5), [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(figures(5), [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(figures(5));
% 8
figure(figures(6));
%legend(legends{6},legendString, 'Location','NorthWest');
%legend boxoff
if strcmpi(testType{iTest},'test1')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'Cumulative FPR', [0 0.301], 0:0.1:0.4);
elseif strcmpi(testType{iTest},'testSelect')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'Cumulative FPR', [0 0.301], 0:0.1:0.4);
elseif strcmpi(testType{iTest},'test2')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', [0.05 100], [0.1 1 10 100],...
'on', 'k', 'Cumulative FPR', [0 0.375], 0:0.1:0.4);
end
xticklabels({'0.1','1','10','100'});
%yticklabels({'0.006','0.01','0.1','0.3'});
set(figures(6), 'color','w');
figName = 'distanceCDF_fa';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(figures(6), gca, width, height, label, margin, 0);
hgsave(figures(6), [figFolder filesep figName '.fig']);
exportFig(figures(6), [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(figures(6), [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(figures(6));
else
figure(figures(1));
legend(legendString);
xlabel('Average time to a neighbour (ms)');
ylabel('TPR');
title('PDF (average nearest): Probability of mini detection as a function of average time to a neighbour');
figure(figures(2));
legend(legendString);
xlabel('Average time to a neighbour (ms)');
ylabel('Cumulative TPR');
title('CDF (average nearest): Cumulative probability of mini detection as a function of average time to a neighbour');
figure(figures(3));
legend(legends{1},legendString);
xlabel('Average time to a neighbour (ms)');
ylabel('TPR');
title('PDF log (average nearest): Probability of mini detection as a function of average time to a neighbour');
figure(figures(4));
legend(legends{2},legendString);
xlabel('Average time to a neighbour (ms)');
ylabel('Cumulative TPR');
title('CDF log (average nearest): Cumulative probability of mini detection as a function of average time to a neighbour');
end
%% 9. dPrime
dPrimeMean = {dPrimeMeanAllMinis; dPrimeMeanAllMiniAnalysis; dPrimeMeanAllpClampRaw2};
[dPrimeMeanMean, dPrimeCI95Mean] = datamean([dPrimeMeanAllMinis dPrimeMeanAllMiniAnalysis dPrimeMeanAllpClampRaw2]);
[statsdPrime.ttest.pval, statsdPrime.ttest.area1, statsdPrime.ttest.area2, statsdPrime.ttest.tstat] = ttestGroup(yTickLabels, dPrimeMean);
[statsdPrime.signrank.pval, statsdPrime.signrank.area1, statsdPrime.signrank.area2] = signrankGroup(yTickLabels, dPrimeMean);
[statsdPrime.signtest.pval, statsdPrime.signtest.area1, statsdPrime.signtest.area2] = signtestGroup(yTickLabels, dPrimeMean);
statsdPrime.area1sem = std(dPrimeMean{1})/sqrt(numel(dPrimeMean{1}));
statsdPrime.area2sem = std(dPrimeMean{2})/sqrt(numel(dPrimeMean{2}));
statsdPrime.area3sem = std(dPrimeMean{3})/sqrt(numel(dPrimeMean{3}));
if strcmpi(testType{iTest}, 'test1')
options.yLim = [0.5 3.1];
elseif strcmpi(testType{iTest}, 'testSelect')
options.yLim = [0.5 4];
elseif strcmpi(testType{iTest}, 'test2')
options.yLim = [0.375 2];
end
options.yLabel = 'd''';
options.showNotches = false;
options.medianPlot = false;
options.colours = colourCodes;
options.showNotches = false;
options.medianPlot = false;
options.violinVisibility = false;
fH3 = multiViolinPlots(dPrimeMean, yTickLabels, dPrimeMeanMean, dPrimeCI95Mean, [], options);
xticklabels(yTickLabels);
set(fH3, 'Name','Mean dPrime across conditions');
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', {}, xlim, xticks,...
'on', 'k', options.yLabel, options.yLim, yticks);
set(fH3, 'color','w');
figName = 'dPrimeMean';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(fH3, gca, width, height, label, margin, 0);
hgsave(fH3, [figFolder filesep figName '.fig']);
exportFig(fH3, [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(fH3, [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(fH3);
%% 10. AUC
auc = {aucAllMinis; aucAllMiniAnalysis; aucAllpClampRaw2};
[aucMean, aucCI95] = datamean([aucAllMinis' aucAllMiniAnalysis' aucAllpClampRaw2']);
[statsAUC.ttest.pval, statsAUC.ttest.area1, statsAUC.ttest.area2, statsAUC.ttest.tstat] = ttestGroup(yTickLabels, auc);
[statsAUC.signrank.pval, statsAUC.signrank.area1, statsAUC.signrank.area2] = signrankGroup(yTickLabels, auc);
[statsAUC.signtest.pval, statsAUC.signtest.area1, statsAUC.signtest.area2] = signtestGroup(yTickLabels, auc);
statsAUC.area1sem = std(auc{1})/sqrt(numel(auc{1}));
statsAUC.area2sem = std(auc{2})/sqrt(numel(auc{2}));
statsAUC.area3sem = std(auc{3})/sqrt(numel(auc{3}));
options.yLim = [];
options.yLabel = 'Area under the curve';
options.showNotches = false;
options.medianPlot = false;
options.colours = colourCodes;
options.showNotches = false;
options.medianPlot = false;
options.violinVisibility = false;
fH4 = multiViolinPlots(auc, yTickLabels, aucMean, aucCI95, [], options);
xticklabels(yTickLabels);
set(fH4, 'Name','Mean AUC across conditions');
yTicks = yticks;
yTicks = yTicks(1:2:end);
if strcmpi(testType{iTest}, 'test2')
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', {}, xlim, xticks,...
'on', 'k', options.yLabel, [0.6 0.8501], 0:0.05:1);
else
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', {}, xlim, xticks,...
'on', 'k', options.yLabel, ylim, yTicks);
end
set(fH4, 'color','w');
figName = 'AUC';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(fH4, gca, width, height, label, margin, 0);
hgsave(fH4, [figFolder filesep figName '.fig']);
exportFig(fH4, [figFolder filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(fH4, [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(fH4);
%% 11. Distance d'
smoothingWindowSize = 41;
perfmormanceStruct.centresFlatD2N = centresMinis;
perfmormanceStruct.centresFlatD2N2 = centresMinisFA;
% for col = 1:size(nHitsPDFPerRecordingMinis,2)
% if sum(nHitsPDFPerRecordingMinis(:,col), 'omitnan')
% break
% else
% nHitsPDFPerRecordingMinis(:,col) = ones(size(nHitsPDFPerRecordingMinis(:,col))).*min(min(nHitsPDFPerRecordingMinis(nHitsPDFPerRecordingMinis ~= 0)));
% end
% end
perfmormanceStruct.nHitsPDFPerRecordingFlatD2N = nHitsPDFPerRecordingMinis;
% for col = 1:size(nFalseAlarmsPDFPerRecordingMinis,2)
% if sum(nFalseAlarmsPDFPerRecordingMinis(:,col), 'omitnan')
% break
% else
% nFalseAlarmsPDFPerRecordingMinis(:,col) = ones(size(nFalseAlarmsPDFPerRecordingMinis(:,col))).*min(min(nFalseAlarmsPDFPerRecordingMinis(nFalseAlarmsPDFPerRecordingMinis ~= 0)));
% end
% end
perfmormanceStruct.nFalseAlarmsPDFPerRecordingFlatD2N = nFalseAlarmsPDFPerRecordingMinis;
[dPrimeFlatMeanMinis, dPrimeFlatCI95Minis, centresMinis] = dPrimeCalcFlat(perfmormanceStruct);
[centresMinisInterp, dPrimeFlatMeanMinisInterpSmooth] = interpSmooth(centresMinis, dPrimeFlatMeanMinis, smoothingWindowSize, true);
dPrimeFlatCI95MinisInterpSmooth = zeros(1,numel(dPrimeFlatMeanMinisInterpSmooth));
[~, dPrimeFlatCI95MinisInterpSmooth(1,:)] = interpSmooth(centresMinis, dPrimeFlatCI95Minis(1,:), smoothingWindowSize, true);
[~, dPrimeFlatCI95MinisInterpSmooth(2,:)] = interpSmooth(centresMinis, dPrimeFlatCI95Minis(2,:), smoothingWindowSize, true);
perfmormanceStruct.centresFlatD2N = centresMiniAnalysis;
perfmormanceStruct.centresFlatD2N2 = centresMiniAnalysisFA;
% for col = 1:size(nHitsPDFPerRecordingMiniAnalysis,2)
% if sum(nHitsPDFPerRecordingMiniAnalysis(:,col), 'omitnan')
% break
% else
% nHitsPDFPerRecordingMiniAnalysis(:,col) = ones(size(nHitsPDFPerRecordingMiniAnalysis(:,col))).*min(min(nHitsPDFPerRecordingMiniAnalysis(nHitsPDFPerRecordingMiniAnalysis ~= 0)));
% end
% end
perfmormanceStruct.nHitsPDFPerRecordingFlatD2N = nHitsPDFPerRecordingMiniAnalysis;
% for col = 1:size(nFalseAlarmsPDFPerRecordingMiniAnalysis,2)
% if sum(nFalseAlarmsPDFPerRecordingMiniAnalysis(:,col), 'omitnan')
% break
% else
% nFalseAlarmsPDFPerRecordingMiniAnalysis(:,col) = ones(size(nFalseAlarmsPDFPerRecordingMiniAnalysis(:,col))).*min(min(nFalseAlarmsPDFPerRecordingMiniAnalysis(nFalseAlarmsPDFPerRecordingMiniAnalysis ~= 0)));
% end
% end
perfmormanceStruct.nFalseAlarmsPDFPerRecordingFlatD2N = nFalseAlarmsPDFPerRecordingMiniAnalysis;
[dPrimeFlatMeanMiniAnalysis, dPrimeFlatCI95MiniAnalysis, centresMiniAnalysis] = dPrimeCalcFlat(perfmormanceStruct);
[centresMiniAnalysisInterp, dPrimeFlatMeanMiniAnalysisInterpSmooth] = interpSmooth(centresMiniAnalysis, dPrimeFlatMeanMiniAnalysis, smoothingWindowSize, true);
dPrimeFlatCI95MiniAnalysisInterpSmooth = zeros(1,numel(dPrimeFlatMeanMiniAnalysisInterpSmooth));
[~, dPrimeFlatCI95MiniAnalysisInterpSmooth(1,:)] = interpSmooth(centresMiniAnalysis, dPrimeFlatCI95MiniAnalysis(1,:), smoothingWindowSize, true);
[~, dPrimeFlatCI95MiniAnalysisInterpSmooth(2,:)] = interpSmooth(centresMiniAnalysis, dPrimeFlatCI95MiniAnalysis(2,:), smoothingWindowSize, true);
perfmormanceStruct.centresFlatD2N = centrespClampRaw2;
perfmormanceStruct.centresFlatD2N2 = centrespClampRaw2FA;
% for col = 1:size(nHitsPDFPerRecordingpClampRaw2,2)
% if sum(nHitsPDFPerRecordingpClampRaw2(:,col), 'omitnan')
% break
% else
% nHitsPDFPerRecordingpClampRaw2(:,col) = ones(size(nHitsPDFPerRecordingpClampRaw2(:,col))).*min(min(nHitsPDFPerRecordingpClampRaw2(nHitsPDFPerRecordingpClampRaw2 ~= 0)));
% end
% end
perfmormanceStruct.nHitsPDFPerRecordingFlatD2N = nHitsPDFPerRecordingpClampRaw2;
% for col = 1:size(nFalseAlarmsPDFPerRecordingpClampRaw2,2)
% if sum(nFalseAlarmsPDFPerRecordingpClampRaw2(:,col), 'omitnan')
% break
% else
% nFalseAlarmsPDFPerRecordingpClampRaw2(:,col) = ones(size(nFalseAlarmsPDFPerRecordingpClampRaw2(:,col))).*min(min(nFalseAlarmsPDFPerRecordingpClampRaw2(nFalseAlarmsPDFPerRecordingpClampRaw2 ~= 0)));
% end
% end
perfmormanceStruct.nFalseAlarmsPDFPerRecordingFlatD2N = nFalseAlarmsPDFPerRecordingpClampRaw2;
[dPrimeFlatMeanpClampRaw2, dPrimeFlatCI95pClampRaw2, centrespClampRaw2] = dPrimeCalcFlat(perfmormanceStruct);
[centrespClampRaw2Interp, dPrimeFlatMeanpClampRaw2InterpSmooth] = interpSmooth(centrespClampRaw2, dPrimeFlatMeanpClampRaw2, smoothingWindowSize, true);
dPrimeFlatCI95pClampRaw2InterpSmooth = zeros(1,numel(dPrimeFlatMeanpClampRaw2InterpSmooth));
[~, dPrimeFlatCI95pClampRaw2InterpSmooth(1,:)] = interpSmooth(centrespClampRaw2, dPrimeFlatCI95pClampRaw2(1,:), smoothingWindowSize, true);
[~, dPrimeFlatCI95pClampRaw2InterpSmooth(2,:)] = interpSmooth(centrespClampRaw2, dPrimeFlatCI95pClampRaw2(2,:), smoothingWindowSize, true);
colours = {matlabColours(8),'r','m'};
legends_fH5 = [];
fH5 = figure;
p = semilogx(centresMinisInterp, dPrimeFlatMeanMinisInterpSmooth, 'LineWidth',1, 'Color',colours{1}); hold on
legends_fH5 = [legends_fH5 p];
ciplot(dPrimeFlatMeanMinisInterpSmooth+dPrimeFlatCI95MinisInterpSmooth(1,:),...
dPrimeFlatMeanMinisInterpSmooth+dPrimeFlatCI95MinisInterpSmooth(2,:),...
centresMinisInterp,colours{1},0.2);
p = semilogx(centresMiniAnalysisInterp, dPrimeFlatMeanMiniAnalysisInterpSmooth, 'LineWidth',1, 'Color',colours{2});
legends_fH5 = [legends_fH5 p];
ciplot(dPrimeFlatMeanMiniAnalysisInterpSmooth+dPrimeFlatCI95MiniAnalysisInterpSmooth(1,:),...
dPrimeFlatMeanMiniAnalysisInterpSmooth+dPrimeFlatCI95MiniAnalysisInterpSmooth(2,:),...
centresMiniAnalysisInterp,colours{2},0.2);
p = semilogx(centrespClampRaw2Interp, dPrimeFlatMeanpClampRaw2InterpSmooth, 'LineWidth',1, 'Color',colours{3});
legends_fH5 = [legends_fH5 p];
ciplot(dPrimeFlatMeanpClampRaw2InterpSmooth+dPrimeFlatCI95pClampRaw2InterpSmooth(1,:),...
dPrimeFlatMeanpClampRaw2InterpSmooth+dPrimeFlatCI95pClampRaw2InterpSmooth(2,:),...
centrespClampRaw2Interp,colours{3},0.2);
hold off
if strcmpi(testType{iTest}, 'test1')
xLim = [0.05 100];
xTicks = [0.1 1 10 100];
xTickLabels = {'0.1','1','10','100'};
yLim = [-1 5];
yTicks = -1:7;
legendLocation = 'NorthWest';
elseif strcmpi(testType{iTest}, 'testSelect')
xLim = [0.05 100];
xTicks = [0.1 1 10 100];
xTickLabels = {'0.1','1','10','100'};
yLim = [-0.05 5.5];
yTicks = -1:7;
legendLocation = 'NorthWest';
elseif strcmpi(testType{iTest}, 'test2')
xLim = [0.05 100];
xTicks = [0.1 1 10 100];
xTickLabels = {'0.1','1','10','100'};
yLim = [-0.5 5.5];
yTicks = -1:7;
legendLocation = 'NorthWest';
end
%legend(legends_fH5,legendString, 'Location',legendLocation);
%legend boxoff
axesProperties({}, 1, 'normal', 'off', 'w', 'Arial', 25, 4/3, 2, [0.003 0.003], 'out',...
'on', 'k', 'Time to neighbour (ms)', xLim, xTicks,...
'on', 'k', 'd''', yLim, yTicks);
xticklabels(xTickLabels);
set(fH5, 'color','w');
figName = 'distancedPrime';
label = [3.25 3];
margin = [0.75 0.5];
width = figSize-label(1)-margin(1);
height = figSize-label(2)-margin(2);
paperSize = resizeFig(fH5, gca, width, height, label, margin, 0);
hgsave(fH5, [targetDir filesep figName '.fig']);
exportFig(fH5, [targetDir filesep figName '.png'],'-dpng','-r300', paperSize);
exportFig(fH5, [figFolder filesep figName '.eps'],'-depsc','-r1200', paperSize);
%close(fH5);
%% Save statistical data
save([figFolder filesep 'stats.mat'], 'statsdPrime','dPrimeMeanMean','dPrimeCI95Mean',...
'statsAUC','aucMean','aucCI95', '-v7.3');
end
close all
%% Local functions
function [fH1, fH2, dPrimeMeanAll, dPrimeMeanMean, dPrimeMeanCI95, aucAll, aucMean, aucCI95, pH1, pH2, simEventCountAll] = summaryPerformance(folder, fH1, fH2, colour, testType)
if nargin < 4
colour = matlabColours(8);
end
roc1All = [];
roc2All = [];
dPrimeAll = [];
dPrimeMeanAll = [];
aucAll = [];
simEventCountAll = [];
for iF = 1:numel(folder)
[fh1,fh2,roc,dPrime,dPrimeMean,auc,fileOrder,simEventCount] = rocCurve(folder{iF});
close(fh1); close(fh2);
roc1All = [roc1All; roc(1,fileOrder-2)]; %#ok<*AGROW>
roc2All = [roc2All; roc(2,fileOrder-2)];
dPrimeAll = [dPrimeAll; dPrime(fileOrder-2)];
dPrimeMeanAll = [dPrimeMeanAll; dPrimeMean];
aucAll = [aucAll; auc];
simEventCountAll = [simEventCountAll; simEventCount(fileOrder-2)];
end
[roc1Mean, roc1CI95] = datamean(roc1All);
[roc2Mean, roc2CI95] = datamean(roc2All);
[dPrimeMean, dPrimeCI95] = datamean(dPrimeAll);
[dPrimeMeanMean, dPrimeMeanCI95] = datamean(dPrimeMeanAll);
[aucMean, aucCI95] = datamean(aucAll);
aucAll = aucAll';
% Interpolate ROCs
[~, iPks] = findpeaks(roc1Mean);
[~, iTroughs] = findpeaks(-roc1Mean);
iPks = sort([iPks iTroughs]);
stepSizeNormalised = 1/1000;
roc1MeanInterp = zeros(round(1/stepSizeNormalised)+1,numel(iPks)+1);
roc2MeanInterp = zeros(round(1/stepSizeNormalised)+1,numel(iPks)+1);
if isempty(iPks)
inds = 1:numel(roc1Mean);
chunkLims = sort([roc1Mean(inds(1)) roc1Mean(inds(end))]);
chunkSize = abs(diff(chunkLims));
stepSize = stepSizeNormalised*chunkSize;
roc1MeanInterp = chunkLims(1):stepSize:chunkLims(2);
roc2MeanInterp = interp1(roc1Mean(inds),roc2Mean(inds),roc1MeanInterp,'pchip');
else
for peak = 1:numel(iPks)
if peak == 1
inds = 1:iPks(peak);
else
inds = iPks(peak-1):iPks(peak);
end
chunkLims = sort([roc1Mean(inds(1)) roc1Mean(inds(end))]);
chunkSize = abs(diff(chunkLims));
stepSize = stepSizeNormalised*chunkSize;
roc1MeanInterp(:,peak) = chunkLims(1):stepSize:chunkLims(2);
roc2MeanInterp(:,peak) = interp1(roc1Mean(inds),roc2Mean(inds),roc1MeanInterp(:,peak),'pchip');
if peak == numel(iPks)
inds = iPks(peak):numel(roc1Mean);
chunkLims = sort([roc1Mean(inds(1)) roc1Mean(inds(end))]);
chunkSize = abs(diff(chunkLims));
stepSize = stepSizeNormalised*chunkSize;
roc1MeanInterp(:,peak+1) = chunkLims(1):stepSize:chunkLims(2);
roc2MeanInterp(:,peak+1) = interp1(roc1Mean(inds),roc2Mean(inds),roc1MeanInterp(:,peak+1),'pchip');
end
end
end
% Plot ROCs
if nargin < 2 || isempty(fH1)
fH1 = figure;
else
figure(fH1);
end
hold on
pH1 = plot(roc1MeanInterp, roc2MeanInterp, '-', 'Color',colour, 'Linewidth',1);
uistack(pH1, 'bottom');
if strcmpi(testType, 'test1')
p = plot(roc1Mean(4:6), roc2Mean(4:6), 'o', 'MarkerEdgeColor',colour, 'MarkerSize',20);
uistack(p, 'bottom');
end
pH1 = pH1(1);
eH = errorbar(roc1Mean, roc2Mean, abs(roc1CI95(1,:)), abs(roc1CI95(2,:)),...
abs(roc2CI95(1,:)), abs(roc2CI95(2,:)), '.', 'MarkerSize',15, 'Color',colour,...
'LineWidth',2, 'Capsize',0);
uistack(eH, 'bottom');
xlim([-0.05 1.05]);
ylim([-0.05 1.05]);
hold off
% dataOrder = [8 1 4 7 3 6 9 10 11 12 13 14 2 5];
% for iCond = 1:numel(roc1Mean)
% text(roc1Mean(iCond),roc2Mean(iCond), num2str(dataOrder(iCond)), 'Interpreter','None');
% end
% Interpolate d primes
xInterp = 1:0.001:numel(dPrimeMean);
dPrimeInterp = interp1(1:numel(dPrimeMean),dPrimeMean,xInterp,'pchip');
% Combined dPrimes
if nargin < 3 || isempty(fH2)
fH2 = figure;
else
figure(fH2);
end
hold on
pH2 = plot(xInterp,dPrimeInterp, '-', 'Color',colour);
uistack(pH2, 'bottom');
if strcmpi(testType, 'test1')
p = plot(4:6, dPrimeMean(4:6), 'o', 'MarkerEdgeColor',colour, 'MarkerSize',20);
uistack(p, 'bottom');
end
eH = errorbar(1:numel(dPrimeMean),dPrimeMean, abs(dPrimeCI95(1,:)), abs(dPrimeCI95(2,:)),...
'.', 'MarkerSize',15, 'Color',colour, 'LineWidth',2, 'Capsize',0);
uistack(eH, 'bottom');
hold off
end
function [figures, legends, nHitsPDFPerRecording, nFalseAlarmsPDFPerRecording, centresHits, centresFA] = summaryDistance(folder, figures, legends, colour, type)
if nargin < 5
type = 'nearest';
end
if nargin < 4
colour = matlabColours(8);
end
if nargin < 3 || isempty(legends)
legends{1} = [];
legends{2} = [];
legends{3} = [];
legends{4} = [];
legends{5} = [];
legends{6} = [];
end
distance2neighbourCombined = [];
distance2neighbourCombinedHits = [];
distance2neighbourPerRecording = {};
distance2neighbourPerRecordingHits = {};
distance2neighbourPerRecordingFA = {};
distance2neighbourPerRecordingCR = {};
for iF = 1:numel(folder)
distance2neighbourRecording = [];
distance2neighbourRecordingHits = [];
d = dir(folder{iF});
for iFile = 1:numel(d)
if contains(d(iFile).name, '.mat')
disp([iF iFile])
% Extract the noise scaling factor
scaleFactorIndStart = strfind(d(iFile).name,'noiseScaleFactor') + 16;
scaleFactorIndEnd = strfind(d(iFile).name,'smoothWindow') - 2;
scaleFactor = d(iFile).name(scaleFactorIndStart:scaleFactorIndEnd);
scaleFactor = strrep(scaleFactor, 'p', '.');
scaleFactor = str2double(scaleFactor);
if scaleFactor == 1
realData = load([folder{iF} filesep d(iFile).name]);
for iRun = 1:numel(realData.performance)
% Get real data for a particular simulation instance
allTrue = realData.performance{iRun}(1,:);
trueI = find(allTrue);
trueT = trueI.*realData.dt;
misses = find(realData.performance{iRun}(5,:));
[~, hitsI] = setdiff(trueI, misses);
% Calculate the time to the nearest neighbour
if strcmpi(type, 'nearest') %&& ~isfield(realData, 'distance2neighbour')
distanceToTheRight = abs([trueT(2:end) inf] - trueT);
distanceToTheLeft = abs([inf trueT(1:end-1)] - trueT);
distance2neighbour{iRun} = min([distanceToTheRight; distanceToTheLeft],[],1);
elseif strcmpi(type, 'average')
distanceToTheRight = abs([trueT(2:end) inf] - trueT);
distanceToTheLeft = abs([inf trueT(1:end-1)] - trueT);
distanceToTheRight(end) = distanceToTheLeft(end);
distanceToTheLeft(1) = distanceToTheRight(1);
distance2neighbour{iRun} = mean([distanceToTheRight; distanceToTheLeft],1);
end
% Combine data
distance2neighbourCombined = [distance2neighbourCombined distance2neighbour{iRun}];
distance2neighbourCombinedHits = [distance2neighbourCombinedHits distance2neighbour{iRun}(hitsI)];
distance2neighbourRecording = [distance2neighbourRecording distance2neighbour{iRun}];
distance2neighbourRecordingHits = [distance2neighbourRecordingHits distance2neighbour{iRun}(hitsI)];
falseAlarms = realData.performance{iRun}(6,:);
falseAlarmsFlatI = find(falseAlarms);
falseAlarmsFlatT = falseAlarmsFlatI.*realData.dt;
falseAlarmsFlatD2N = zeros(size(falseAlarmsFlatI));
for iFA = 1:numel(falseAlarmsFlatI)
falseAlarmsFlatD2N(iFA) = min(abs(trueT - falseAlarmsFlatT(iFA)));
end
correctRejections = realData.performance{iRun}(7,:);
correctRejectionsFlatI = find(correctRejections);
correctRejectionsFlatT = correctRejectionsFlatI.*realData.dt;
correctRejectionsFlatD2N = zeros(size(correctRejectionsFlatI));
for iFA = 1:numel(correctRejectionsFlatI)
correctRejectionsFlatD2N(iFA) = min(abs(trueT - correctRejectionsFlatT(iFA)));
end
end
end
end
end
distance2neighbourPerRecording = [distance2neighbourPerRecording; distance2neighbourRecording];
distance2neighbourPerRecordingHits = [distance2neighbourPerRecordingHits; distance2neighbourRecordingHits];
distance2neighbourPerRecordingFA = [distance2neighbourPerRecordingFA; falseAlarmsFlatD2N];
distance2neighbourPerRecordingCR = [distance2neighbourPerRecordingCR; correctRejectionsFlatD2N];
end
% Calculate distributions
maxDist = floor(max(distance2neighbourCombined));
binSize = 0.1;
edgesHits = 0:binSize:maxDist;
centresHits = edgesHits(1:end-1) + binSize/2;
nAllTrue = histcounts(distance2neighbourCombined, edgesHits);
nHits = histcounts(distance2neighbourCombinedHits, edgesHits);
nHitsPDF = nHits./nAllTrue;
nHitsCDF = cumsum(nHits,'omitnan')./numel(distance2neighbourCombined);
% figure; semilogx(centres, nAllTrue); hold on
% semilogx(centres, nHits); hold off
% Hit rate
nHitsPDFPerRecording = [];
nHitsCDFPerRecording = [];
for iRec = 1:numel(distance2neighbourPerRecording)
nAllTrueRecording = histcounts(distance2neighbourPerRecording{iRec}, edgesHits);
nHitsRecording = histcounts(distance2neighbourPerRecordingHits{iRec}, edgesHits);
nHitsPDFPerRecording = [nHitsPDFPerRecording; nHitsRecording./nAllTrueRecording];
nHitsCDFPerRecording = [nHitsCDFPerRecording; cumsum(nHitsRecording,'omitnan')./numel(distance2neighbourPerRecording{iRec})];
end
[nHitsPDFPerRecordingMean, nHitsPDFPerRecordingCI95] = datamean(nHitsPDFPerRecording);
[nHitsCDFPerRecordingMean, nHitsCDFPerRecordingCI95] = datamean(nHitsCDFPerRecording);
% Probability of detecting a mini as a function of time to the nearest neighbour
if nargin < 2 || isempty(figures) || numel(figures) < 1
figures(1) = figure;
else
figure(figures(1)); hold on
end
semilogx(centresHits, nHitsPDF, 'LineWidth',2, 'Color',colour);
%loglog(centres, nHitsPDF, 'LineWidth',2, 'Color',colour);
hold off
% Cumulative probability of detecting a mini as a function of time to the nearest neighbour
if nargin < 2 || isempty(figures) || numel(figures) < 2
figures(2) = figure;
else
figure(figures(2)); hold on
end
semilogx(centresHits, nHitsCDF, 'LineWidth',2, 'Color',colour);
%loglog(centres, nHitsCDF, 'LineWidth',2, 'Color',colour);
hold off
% Probability of detecting a mini as a function of time to the nearest neighbour (with 95% CI)
smoothingWindowSize = 41;
[centresInterp, nHitsPDFPerRecordingMeanInterpSmooth] = interpSmooth(centresHits, nHitsPDFPerRecordingMean, smoothingWindowSize, true);
nHitsPDFPerRecordingCI95InterpSmooth = zeros(1,numel(centresInterp));
[~, nHitsPDFPerRecordingCI95InterpSmooth(1,:)] = interpSmooth(centresHits, nHitsPDFPerRecordingCI95(1,:), smoothingWindowSize, true);
[~, nHitsPDFPerRecordingCI95InterpSmooth(2,:)] = interpSmooth(centresHits, nHitsPDFPerRecordingCI95(2,:), smoothingWindowSize, true);
if nargin < 2 || isempty(figures) || numel(figures) < 3
figures(3) = figure;
else
figure(figures(3)); hold on
end
p = semilogx(centresInterp(~isnan(nHitsPDFPerRecordingMeanInterpSmooth)), nHitsPDFPerRecordingMeanInterpSmooth(~isnan(nHitsPDFPerRecordingMeanInterpSmooth)),...
'LineWidth',1, 'Color',colour); hold on
% p = loglog(centresInterp(~isnan(nHitsPDFPerRecordingMeanInterpSmooth)), nHitsPDFPerRecordingMeanInterpSmooth(~isnan(nHitsPDFPerRecordingMeanInterpSmooth)),...
% 'LineWidth',1, 'Color',colour); hold on
legends{3} = [legends{3} p];
lowerLim = nHitsPDFPerRecordingMeanInterpSmooth(~isnan(nHitsPDFPerRecordingMeanInterpSmooth) & ~isnan(nHitsPDFPerRecordingCI95InterpSmooth(1,:))) +...
nHitsPDFPerRecordingCI95InterpSmooth(1,~isnan(nHitsPDFPerRecordingMeanInterpSmooth) & ~isnan(nHitsPDFPerRecordingCI95InterpSmooth(1,:)));
lowerLim(lowerLim <= 0) = 1e-9;
upperLim = nHitsPDFPerRecordingMeanInterpSmooth(~isnan(nHitsPDFPerRecordingMeanInterpSmooth) & ~isnan(nHitsPDFPerRecordingCI95InterpSmooth(2,:))) +...
nHitsPDFPerRecordingCI95InterpSmooth(2,~isnan(nHitsPDFPerRecordingMeanInterpSmooth) & ~isnan(nHitsPDFPerRecordingCI95InterpSmooth(2,:)));
upperLim(upperLim <= 0 | upperLim > 1.5) = 1e-9;
ciplot(lowerLim,upperLim, centresInterp(~isnan(nHitsPDFPerRecordingMeanInterpSmooth) & ~isnan(nHitsPDFPerRecordingCI95InterpSmooth(1,:))), colour, 0.2);
hold off
ylim([0 1]);
% Cumulative probability of detecting a mini as a function of time to the nearest neighbour (with 95% CI)
if nargin < 2 || isempty(figures) || numel(figures) < 4
figures(4) = figure;
else
figure(figures(4)); hold on
end
p = semilogx(centresHits, nHitsCDFPerRecordingMean, 'LineWidth',1, 'Color',colour); hold on
% p = loglog(centres, nHitsCDFPerRecordingMean, 'LineWidth',1, 'Color',colour); hold on
legends{4} = [legends{4} p];
lowerLim = nHitsCDFPerRecordingMean+nHitsCDFPerRecordingCI95(1,:);
lowerLim(lowerLim <= 0) = 1e-9;
upperLim = nHitsCDFPerRecordingMean+nHitsCDFPerRecordingCI95(2,:);
upperLim(upperLim <= 0 | upperLim > 1.5) = 1e-9;
ciplot(lowerLim,upperLim, centresHits,colour,0.2);
hold off
% False alarm rate
binSize = 0.5;
edgesFA = 0:binSize:maxDist;
centresFA = edgesFA(1:end-1) + binSize/2;
nFalseAlarmsPDFPerRecording = [];
nFalseAlarmsCDFPerRecording = [];
for iRec = 1:numel(distance2neighbourPerRecordingFA)
nAllFalseRecording = histcounts([distance2neighbourPerRecordingFA{iRec} distance2neighbourPerRecordingCR{iRec}], edgesFA);
nFalseAlarmsRecording = histcounts(distance2neighbourPerRecordingFA{iRec}, edgesFA);
nFalseAlarmsPDFPerRecording = [nFalseAlarmsPDFPerRecording; nFalseAlarmsRecording./nAllFalseRecording];
nFalseAlarmsCDFPerRecording = [nFalseAlarmsCDFPerRecording;...
cumsum(nFalseAlarmsRecording,'omitnan')./numel([distance2neighbourPerRecordingFA{iRec} distance2neighbourPerRecordingCR{iRec}])];
end
[nFalseAlarmsPDFPerRecordingMean, nFalseAlarmsPDFPerRecordingCI95] = datamean(nFalseAlarmsPDFPerRecording);
[nFalseAlarmsCDFPerRecordingMean, nFalseAlarmsCDFPerRecordingCI95] = datamean(nFalseAlarmsCDFPerRecording);
% Probability of a false alarm as a function of time to the nearest neighbour (with 95% CI)
smoothingWindowSize = 41;
[centresInterp, nFalseAlarmsPDFPerRecordingMeanInterpSmooth] = interpSmooth(centresFA, nFalseAlarmsPDFPerRecordingMean, smoothingWindowSize, true);
nFalseAlarmsPDFPerRecordingCI95InterpSmooth = zeros(1,numel(centresInterp));
[~, nFalseAlarmsPDFPerRecordingCI95InterpSmooth(1,:)] = interpSmooth(centresFA, nFalseAlarmsPDFPerRecordingCI95(1,:), smoothingWindowSize, true);
[~, nFalseAlarmsPDFPerRecordingCI95InterpSmooth(2,:)] = interpSmooth(centresFA, nFalseAlarmsPDFPerRecordingCI95(2,:), smoothingWindowSize, true);
if nargin < 2 || isempty(figures) || numel(figures) < 5
figures(5) = figure;
else
figure(figures(5)); hold on
end
p = semilogx(centresInterp(~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth)), nFalseAlarmsPDFPerRecordingMeanInterpSmooth(~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth)),...
'LineWidth',1, 'Color',colour); hold on
% p = loglog(centresInterp(~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth)), nFalseAlarmsPDFPerRecordingMeanInterpSmooth(~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth)),...
% 'LineWidth',1, 'Color',colour); hold on
legends{5} = [legends{5} p];
lowerLim = nFalseAlarmsPDFPerRecordingMeanInterpSmooth(~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth) & ~isnan(nFalseAlarmsPDFPerRecordingCI95InterpSmooth(1,:))) +...
nFalseAlarmsPDFPerRecordingCI95InterpSmooth(1,~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth) & ~isnan(nFalseAlarmsPDFPerRecordingCI95InterpSmooth(1,:)));
lowerLim(lowerLim <= 0) = 1e-9;
upperLim = nFalseAlarmsPDFPerRecordingMeanInterpSmooth(~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth) & ~isnan(nFalseAlarmsPDFPerRecordingCI95InterpSmooth(2,:))) +...
nFalseAlarmsPDFPerRecordingCI95InterpSmooth(2,~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth) & ~isnan(nFalseAlarmsPDFPerRecordingCI95InterpSmooth(2,:)));
upperLim(upperLim <= 0 | upperLim > 1.5) = 1e-9;
ciplot(lowerLim,upperLim, centresInterp(~isnan(nFalseAlarmsPDFPerRecordingMeanInterpSmooth) & ~isnan(nFalseAlarmsPDFPerRecordingCI95InterpSmooth(1,:))), colour, 0.2);
hold off
ylim([0 1]);
% Cumulative probability of detecting a mini as a function of time to the nearest neighbour (with 95% CI)
if nargin < 2 || isempty(figures) || numel(figures) < 6
figures(6) = figure;
else
figure(figures(6)); hold on
end
p = semilogx(centresFA, nFalseAlarmsCDFPerRecordingMean, 'LineWidth',1, 'Color',colour); hold on
% p = loglog(centres, nFalseAlarmsCDFPerRecordingMean, 'LineWidth',1, 'Color',colour); hold on
legends{6} = [legends{6} p];
lowerLim = nFalseAlarmsCDFPerRecordingMean+nFalseAlarmsCDFPerRecordingCI95(1,:);
lowerLim(lowerLim <= 0) = 1e-9;
upperLim = nFalseAlarmsCDFPerRecordingMean+nFalseAlarmsCDFPerRecordingCI95(2,:);
upperLim(upperLim <= 0 | upperLim > 1.5) = 1e-9;
ciplot(lowerLim,upperLim, centresFA,colour,0.2);
hold off
end
function [p, area1, area2, tstat] = ttestGroup(areaNames, data)
areaCombos = nchoosek(1:numel(areaNames), 2);
nCombos = size(areaCombos,1);
area1 = areaNames(areaCombos(:,1));
area2 = areaNames(areaCombos(:,2));
p = zeros(1,nCombos);
for iCombo = 1:nCombos
areaCode1 = areaCombos(iCombo,1);
areaCode2 = areaCombos(iCombo,2);