SVMCrossVal.git

@@ -0,0 +1,18 @@

+function [KI_u,KI_o,PROZ]=ki_bin(Z,N,alpha)

+% Berechnet Konfidenzintervall f�r Binomialverteilte Zufallsvariable

+% Aufruf: [KI_u,KI_o,PROZ]=ki_bin(Z,N,alpha);

+% Z (zB 5) von insgesamt N (zB 20) Beobachtungen mit Merkmalsauspr�gung x,

+% und damit N-Z Beobachtungen des Alternativmerkmals y.

+% alpha: Schranken des Konfidenzintervalls (z.B. 0.05 entspricht 95% Vertrauensbereich).

+% �bergibt in KI_u die untere in KI_o die obere Vertrauensgrenze,

+% PROZ ist der Anteil des Merkmals x bezogen auf alle Beobachtungen in Prozent.

+

+

+PROZ=Z/N;

+alpha=alpha/2;

+KI_u=(Z+1)*finv(alpha,2*(Z+1),2*(N-Z))/(N-Z+(Z+1)*finv(alpha,2*(Z+1),2*(N-Z)));

+KI_o=Z/(Z+(N-Z+1)*finv(alpha,2*(N-Z+1),2*Z));

+

+end

+

+

@@ -81,7 +81,7 @@ switch task

         out.header.classDef = classDef;

-        out.subjectdata  = fbs_load_mask(model.baseDir,subjects);

+        out.subjectdata  = fbs_load_mask(getBaseDir(model),subjects);

 %         if(size(subjects,2)>1)

 %             display(sprintf('No BATCH Support for Searchlight!'));

@@ -92,7 +92,7 @@ switch task

         fbsargs.timeline        = timeLine;

         fbsargs.classes         = classDef;

         fbsargs.mask            = mask;

-        fbsargs.basedir         = model.baseDir;

+        fbsargs.basedir         = getBaseDir(model);

         fbsargs.sessionList     = 1:3;

         fbsargs.eventList       = classDef.eventMatrix;

         fbsargs.psthOpts        = psthOpts;

@@ -26,52 +26,64 @@ nTrials           = getNTrials(psth);

     axis([psthStart psthEnd 0 100])

     xlabel('time [sec]');

     ylabel('decode performance [%]');

-

+plottime= tic;

     switch type

         case 'psth'

             plotPSTH(psth,psthStart,psthEnd);  

+

         case 'simple'

             plotDecodePerformanceWithSE(frameStart,frameEnd,decodePerformance)

+

+            plot([psthStart psthEnd],[chanceLevel chanceLevel],'k:');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.05,chanceLevel/100,'--');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.01,chanceLevel/100,'-.');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.001,chanceLevel/100,':');

+

         case 'class performance'

             plotClassPerformance(frameStart,frameEnd,decodePerformance,nClasses)

+

+            plot([psthStart psthEnd],[chanceLevel chanceLevel],'k:');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.05,chanceLevel/100,'--');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.01,chanceLevel/100,'-.');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.001,chanceLevel/100,':');

         case 'x-subject-val'

             for c = 1:nSubjects

                 plot(frameStart:frameEnd, decodePerformance(:,c) ,[colorChooser(mod(c,nSubjects)+3) '-']);

             end

             plotDecodePerformanceWithSE(frameStart,frameEnd,decodePerformance)

-    end

             plot([psthStart psthEnd],[chanceLevel chanceLevel],'k:');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.05,chanceLevel/100,'--');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.01,chanceLevel/100,'-.');

+            plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.001,chanceLevel/100,':');

-    plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.05,chanceLevel/100,'r');

-    plotBinConfidenceIntervall(psthStart,psthEnd,nTrials,0.01,chanceLevel/100,'g');

-    

+    end

+toc(plottime);    

     hold off;

-%     setTitle(f,header,decode,subjectData);

-    

 end

 function plotBinConfidenceIntervall(pStart,pEnd,nTrials,alpha,limit,color)

     [pLevel Z] = rev_ki_bin(nTrials,alpha,limit);

     [lower upper proz] = ki_bin(Z,nTrials,alpha);

-    plot([pStart pEnd],[pLevel*100 pLevel*100],[color ':']);

-

-    plot([pStart pEnd],[lower*100 lower*100],[color '-.']);

-    plot([pStart pEnd],[upper*100 upper*100],[color '-.']);

+    plot([pStart pEnd],[pLevel*100 pLevel*100],[color 'k']);

+%     plot([pStart pEnd],[lower*100 lower*100],[color 'k']);

+%     plot([pStart pEnd],[upper*100 upper*100],[color 'k']);

 end

 function n = getNTrials(psth)

-nSubjects = size(psth,2);

+nOverallConditions = size(psth,2);

 n = 0;

-for ns = 1:nSubjects

+for ns = 1:nOverallConditions

     nClasses = size(psth{ns},2);

     for nc = 1:nClasses

         n = n + size(psth{ns}{nc},1);

     end

 end

+n=n/(nOverallConditions/nClasses);

+

 end

 function plotClassPerformance(frameStart,frameEnd,decodePerformance,nClasses)

@@ -104,7 +116,9 @@ PSTH_AXIS_MAX = 2;

                   psthData = nanmean(psth{voxel}{label});

               end

               PSTH_AXIS_MAX = max(PSTH_AXIS_MAX,nanmax(psthData));

+              PSTH_AXIS_MIN = min(PSTH_AXIS_MIN,nanmin(psthData));

               plot(psthStart:psthEnd,psthData,[colorChooser(voxel), lineStyleChooser(label)]);

+%               plot(psthStart:psthEnd,psthData,[lineStyleChooser(voxel), colorChooser(label)]);

           end

       end

     end

@@ -0,0 +1,20 @@

+function [PROZ Z] = rev_ki_bin(N,alpha,KI_u)

+% rausbekommen ab wie vielen (hypothetischen) Beobachtungen einer Klasse 

+% (f�r ein gegebenes N, dh alle trials/Cross-Validations) das 

+% Konfidenzintervall f�r die decode performance f�r diese Klasse den 

+% 50%-Wert (also chance) gerade noch umschliesst (dh. KI_u=50%). Der 

+% zugeh�rige PROZ wert w�rde dann die Schwelle fetlegen, die geplottet 

+% werden soll. Decode werte �ber dieser Schwelle w�ren dann mit 95% 

+% Sicherheit signifikant.

+

+

+Z = 0;

+KI_low = 0;

+% tic

+while KI_low < KI_u && (Z+1)< N

+    Z = Z + 1;

+    KI_low = ki_bin(Z,N,alpha); 

+end

+% toc

+PROZ = Z/N;

+end

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