Delete 'MatlabScripts/g_Fig3FigS6Decoding.m'

MatlabScripts/g_Fig3FigS6Decoding.m

@@ -1,407 +0,0 @@
-clear all;
-load ('R_2R.mat');
-load ('RAW.mat');
-load ('PoolDec_2R.mat');
-load ('UnitDec_2R.mat');
-
-sucrose=[1  0.6  0.1];
-maltodextrin=[.9  0.3  .9];
-water=[0.00  0.75  0.75];
-total=[0.3  0.1  0.8];
-NAc=[0.5  0.1  0.8];
-VP=[0.3  0.7  0.1];
-NAcP{5,1}=[1  0.7  1];
-NAcP{4,1}=[0.85  0.3  0.9];
-NAcP{3,1}=[0.6  0.1  0.8];
-NAcP{2,1}=[0.3  0.05  0.5];
-NAcP{1,1}=[0.2  0  0.3];
-VPP{5,1}=[0.6  1  0.25];
-VPP{4,1}=[0.4  0.9  0.15];
-VPP{3,1}=[0.3  0.7  0.1];
-VPP{2,1}=[0.1  0.4  0.05];
-VPP{1,1}=[0.03  0.2  0];
-NAcShuff=[0.3  0.05  0.5];
-VPShuff=[0.05  0.4  0];
-
-%load parameters
-BinDura=R_2R.Param.BinDura;
-bins=R_2R.Param.bins;
-binint=R_2R.Param.binint;
-binstart=R_2R.Param.binstart;
-NumNeurons=R_2R.Param.NumNeurons;
-repetitions=length(PoolDec{1,1}.True{1,1}(:,1));
-
-xaxis=linspace(binstart+BinDura(2)/2,binstart+(bins-1)*binint+BinDura(2)/2,bins); %include all bins
-
-%% single unit decoding
-
-%divide neurons up by region
-NAneurons=strcmp(R_2R.Ninfo(:,3),'NA');
-VPneurons=strcmp(R_2R.Ninfo(:,3),'VP');
-
-for e=1:3 %different selections of neurons
-    
-    figure;
-    
-    %pick which set of neurons: all, reward-specific, or non-reward specific
-    if e==1 selection=R_2R.SucN<2; end %all neurons
-    if e==2 selection=R_2R.SucN | R_2R.MalN; end %reward-selective neurons
-    if e==3 selection=(R_2R.SucN | R_2R.MalN) == 0; end %reward-nonselective neurons
-
-    %get average accuracy for each bin and run stats comparing region
-    for i = 1:bins
-        AvgAccNAc(1,i)=nanmean(UnitDec.True(NAneurons&selection,i)); %average accuracy NAc
-        SEMAccNAc(1,i)=nanste(UnitDec.True(NAneurons&selection,i),1); %SEM
-        AvgAccNAcShuff(1,i)=nanmean(UnitDec.Shuff(NAneurons&selection,i)); %average accuracy NAcShuff
-        SEMAccNAcShuff(1,i)=nanste(UnitDec.Shuff(NAneurons&selection,i),1); %SEM
-        AvgAccVP(1,i)=nanmean(UnitDec.True(VPneurons&selection,i)); %average accuracy NAcShuff
-        SEMAccVP(1,i)=nanste(UnitDec.True(VPneurons&selection,i),1); %SEM
-        AvgAccVPShuff(1,i)=nanmean(UnitDec.Shuff(VPneurons&selection,i)); %average accuracy NAcShuff
-        SEMAccVPShuff(1,i)=nanste(UnitDec.Shuff(VPneurons&selection,i),1); %SEM
-      
-    end
-
-    %prepare shading
-    upSEMNAc=AvgAccNAc+SEMAccNAc;
-    downSEMNAc=AvgAccNAc-SEMAccNAc;
-    upSEMVP=AvgAccVP+SEMAccVP;
-    downSEMVP=AvgAccVP-SEMAccVP;
-    upSEMNAcShuff=AvgAccNAcShuff+SEMAccNAcShuff;
-    downSEMNAcShuff=AvgAccNAcShuff-SEMAccNAcShuff;
-    upSEMVPShuff=AvgAccVPShuff+SEMAccVPShuff;
-    downSEMVPShuff=AvgAccVPShuff-SEMAccVPShuff;
-
-    %plotting decoder accuracies over time
-    subplot(2,3,1);
-    hold on;
-    plot(xaxis,AvgAccNAc(1:bins),'Color', NAc,'linewidth',1.5); %accumbens
-    plot(xaxis,AvgAccVP(1:bins),'Color', VP,'linewidth',1.5); %vp
-    %shuffled
-    plot(xaxis,AvgAccNAcShuff(1:bins),'Color', 'k','linewidth',1.5);
-    plot(xaxis,AvgAccVPShuff(1:bins),'Color', 'k','linewidth',1.5);
-    %error
-    patch([xaxis,xaxis(end:-1:1)],[upSEMNAc,downSEMNAc(end:-1:1)],NAc,'EdgeColor','none');alpha(0.5);
-    patch([xaxis,xaxis(end:-1:1)],[upSEMNAcShuff,downSEMNAcShuff(end:-1:1)],'k','EdgeColor','none');alpha(0.5);
-    patch([xaxis,xaxis(end:-1:1)],[upSEMVP,downSEMVP(end:-1:1)],VP,'EdgeColor','none');alpha(0.5);
-    patch([xaxis,xaxis(end:-1:1)],[upSEMVPShuff,downSEMVPShuff(end:-1:1)],'k','EdgeColor','none');alpha(0.5);
-    xlabel('Seconds from reward delivery');
-    ylabel('Accuracy');
-    title('Unit decoding accuracy');
-    legend('NAc units','VP units','Shuffled','Location','northwest');
-    axis([xaxis(1) xaxis(end) 0.47 max(AvgAccVP)+0.04]);
-
-    %find and plot bins exceeding confidence interval
-    for i=1:bins
-        %confidence interval for NAc
-        x = UnitDec.Shuff(NAneurons&selection,i);                      % Create Data
-        SEM = nanstd(x)/sqrt(length(x));               % Standard Error
-        ts = tinv([0.005  0.995],length(x)-1);      % T-Score
-        CI = nanmean(x) + ts*SEM; % Confidence Intervals
-        if nanmean(UnitDec.True(NAneurons&selection,i))>CI(2) NAcConf(1,i)=1; else NAcConf(1,i)=0; end
-        
-        
-        %confidence interval for VP
-        x = UnitDec.Shuff(VPneurons&selection,i);                      % Create Data
-        SEM = nanstd(x)/sqrt(length(x));               % Standard Error
-        ts = tinv([0.005  0.995],length(x)-1);      % T-Score
-        CI = nanmean(x) + ts*SEM; % Confidence Intervals
-        if nanmean(UnitDec.True(VPneurons&selection,i))>CI(2) VPConf(1,i)=1; else VPConf(1,i)=0; end
-        
-    end
-    
-    %find consecutive bins
-    R_2R.UnitNAcComp{e,1}=zeros(1,bins);
-    R_2R.UnitVPComp{e,1}=zeros(1,bins);
-    for i=2:bins-1
-        if NAcConf(1,i)==1
-            if NAcConf(1,i-1)==1 | NAcConf(1,i+1)==1
-                R_2R.UnitNAcComp{e,1}(1,i)=1;
-            end
-        end
-        if VPConf(1,i)==1
-            if VPConf(1,i-1)==1 | VPConf(1,i+1)==1
-                R_2R.UnitVPComp{e,1}(1,i)=1;
-            end
-        end
-    end
-    
-    %check to see if the first bin above shuffled data would be different
-    %if using fewer VP neurons to match NAc
-    for j=1:20
-        for i=1:bins
-            matchedneurons=cat(1,ones(sum(NAneurons&selection),1),zeros(sum(VPneurons&selection)-sum(NAneurons&selection),1));
-            matchedneurons=(matchedneurons(randperm(length(matchedneurons)))==1);
-            %confidence interval for VP
-            VPselectionSh=UnitDec.Shuff(VPneurons&selection,i);
-            VPselection=UnitDec.True(VPneurons&selection,i);
-            x = VPselectionSh(matchedneurons,1);                      % Create Data
-            SEM = nanstd(x)/sqrt(length(x));               % Standard Error
-            ts = tinv([0.005  0.995],length(x)-1);      % T-Score
-            CI = nanmean(x) + ts*SEM; % Confidence Intervals
-            if nanmean(VPselection(matchedneurons,1))>CI(2) R_2R.VPaboveshuff{e,1}(j,i)=1; else R_2R.VPaboveshuff{e,1}(j,i)=0; end
-        end
-    end
-        
-        
-    %plot
-    plot(xaxis,R_2R.UnitVPComp{e,1}*0.48,'*','Color',VP);
-    plot(xaxis,R_2R.UnitNAcComp{e,1}*0.485,'*','Color',NAc);
-    
-    %multiple comparisons for NAc vs VP
-    
-    %make a matrix indicating which region each neuron-bin comes from
-    nbinregion=[];
-    binname=[];
-    for i=1:bins
-        nbinregion=cat(2,nbinregion,NAneurons);
-        binname=cat(2,binname,i*ones(length(NAneurons),1));
-    end
-    
-    testtrue=UnitDec.True(selection,:);
-    testshuff=UnitDec.Shuff(selection,:);
-    testregion=nbinregion(selection,:);
-    testbins=binname(selection,:);
-    
-    
-    %find effects of real vs shuffled, region, and bins on accuracy
-    [~,R_2R.UnitDecStats{e,1},R_2R.UnitDecStats{e,2}]=anovan(cat(1,testtrue(:),testshuff(:)),{cat(1,zeros(length(testtrue(:)),1),ones(length(testshuff(:)),1)),cat(1,testregion(:),testregion(:)),cat(1,testbins(:),testbins(:))},'varnames',{'real vs shuffled','region','bins'},'display','off','model','full');
-
-    %do post-hoc comparisons
-    [c,~,~,names]=multcompare(R_2R.UnitDecStats{e,2},'Dimension',[1 2 3],'CType','tukey-kramer','display','off');
-
-    %find post-hoc differences 
-    for i=1:bins
-        %NAc vs VP
-        Sel=c(:,1)==4*(i-1)+1 & c(:,2)==4*(i-1)+3;
-        if c(Sel,6)<0.05 R_2R.UnitNAcVPComp{e,1}(1,i)=1; else R_2R.UnitNAcVPComp{e,1}(1,i)=0; end
-        R_2R.UnitNAcVPComp{e,1}(2,i)=c(Sel,6);
-    end
-    
-    %add it to plot
-    plot(xaxis,R_2R.UnitNAcVPComp{e,1}(1,:)*(max(AvgAccVP)+0.015),'*','Color','k');
-    
-    %plotting CDF at peak bin
-    subplot(2,3,4)
-    hold on;
-    %NAc
-    [~,NAcbin]=max(AvgAccNAc);
-    [cdfNAc,xNAc] = ecdf(UnitDec.True(NAneurons&selection,NAcbin));
-    [cdfNAcSh,xNAcSh] = ecdf(UnitDec.Shuff(NAneurons&selection,NAcbin));
-    plot(xNAc,cdfNAc,'Color',NAc,'linewidth',1.5);
-    %VP
-    [~,VPbin]=max(AvgAccVP);
-    [cdfVP,xVP] = ecdf(UnitDec.True(VPneurons&selection,VPbin));
-    [cdfVPSh,xVPSh] = ecdf(UnitDec.Shuff(VPneurons&selection,VPbin));
-    plot(xVP,cdfVP,'Color',VP,'linewidth',1.5);
-    %shuffled
-    plot(xNAcSh,cdfNAcSh,'Color','k','linewidth',1.5);
-    xlabel('Decoding accuracy')
-    plot(xVPSh,cdfVPSh,'Color','k','linewidth',1.5);
-    axis([0 1 0 1]);
-    plot([0.5 0.5],[0 1],':','color','k','linewidth',0.75);
-    title(['Accuracy at peak bin: ' num2str(((NAcbin-1)*binint)+(binstart+BinDura(2)/2)) 's NAc, ' num2str(((VPbin-1)*binint)+(binstart+BinDura(2)/2)) 's VP']);
-    xlabel('Decoding accuracy');
-    ylabel('Cumulative fraction of population');
-    legend('NAc units','VP units','Shuffled','Location','northwest');
-
-    %stats comparing peak bins
-    [~,R_2R.UnitDecPeakBin{e,1},~]=anovan(cat(1,UnitDec.True(NAneurons&selection,NAcbin),UnitDec.Shuff(NAneurons&selection,NAcbin),UnitDec.True(VPneurons&selection,VPbin),UnitDec.Shuff(VPneurons&selection,VPbin)),...
-        {cat(1,zeros(sum(NAneurons&selection),1),ones(sum(NAneurons&selection),1),zeros(sum(VPneurons&selection),1),ones(sum(VPneurons&selection),1)),...
-        cat(1,zeros(sum(NAneurons&selection),1),zeros(sum(NAneurons&selection),1),ones(sum(VPneurons&selection),1),ones(sum(VPneurons&selection),1))},'varnames',{'real vs shuffled','region'},'display','off','model','full');
-
-
-%% pooled decoding
-
-    %reset matrices for stats analysis
-    A=[];
-    B=[];
-    C=[];
-    D=[];
-    
-     %get average accuracy for each bin
-    for v=1:length(PoolDec{e,1}.True) %each condition (number of neurons used)
-        for i = 1:bins
-            %NAc
-            if length(PoolDec{e,1}.True{v,1})>1 %in case analysis wasn't run because not enough neurons
-                PoolAccNAc{v,1}(1,i)=nanmean(PoolDec{e,1}.True{v,1}(:,i)); %average accuracy NAc
-                PoolSEMNAc{v,1}(1,i)=nanste(PoolDec{e,1}.True{v,1}(:,i),1); %SEM
-                PoolAccNAcShuff{v,1}(1,i)=nanmean(PoolDec{e,1}.Shuff{v,1}(:,i)); %average accuracy NAcShuff
-                PoolSEMNAcShuff{v,1}(1,i)=nanste(PoolDec{e,1}.Shuff{v,1}(:,i),1); %SEM
-            else
-                PoolAccNAc{v,1}(1,i)=NaN;
-                PoolSEMNAc{v,1}(1,i)=NaN;
-                PoolAccNAcShuff{v,1}(1,i)=NaN;
-                PoolSEMNAcShuff{v,1}(1,i)=NaN;
-            end
-            
-            %VP
-            if length(PoolDec{e,2}.True{v,1})>1 %in case analysis wasn't run because not enough neurons
-                PoolAccVP{v,1}(1,i)=nanmean(PoolDec{e,2}.True{v,1}(:,i)); %average accuracy VP
-                PoolSEMVP{v,1}(1,i)=nanste(PoolDec{e,2}.True{v,1}(:,i),1); %SEM
-                PoolAccVPShuff{v,1}(1,i)=nanmean(PoolDec{e,2}.Shuff{v,1}(:,i)); %average accuracy VPshuff
-                PoolSEMVPShuff{v,1}(1,i)=nanste(PoolDec{e,2}.Shuff{v,1}(:,i),1); %SEM
-            else
-                PoolAccVP{v,1}(1,i)=NaN;
-                PoolSEMVP{v,1}(1,i)=NaN;
-                PoolAccVPShuff{v,1}(1,i)=NaN;
-                PoolSEMVPShuff{v,1}(1,i)=NaN;
-            end
-            
-        end
-
-        %find the time of the most accurate bin for each of the repetitions
-        for j = 1:repetitions
-            %NAc
-            if length(PoolDec{e,1}.True{v,1})>1 %in case analysis wasn't run because not enough neurons
-                [~,PeakBinsNAc{v,1}(j,1)]=max(PoolDec{e,1}.True{v,1}(j,:));
-                PeakBinsNAc{v,1}(j,2)=binstart-binint+PeakBinsNAc{v,1}(j,1)*binint+BinDura(2)/2;
-            else
-                PeakBinsNAc{v,1}(j,1)=NaN;
-                PeakBinsNAc{v,1}(j,2)=NaN;
-            end
-            %VP
-            if length(PoolDec{e,2}.True{v,1})>1 %in case analysis wasn't run because not enough neurons
-                [~,PeakBinsVP{v,1}(j,1)]=max(PoolDec{e,2}.True{v,1}(j,:));
-                PeakBinsVP{v,1}(j,2)=binstart-binint+PeakBinsVP{v,1}(j,1)*binint+BinDura(2)/2;
-            else
-                PeakBinsVP{v,1}(j,1)=NaN;
-                PeakBinsVP{v,1}(j,2)=NaN;
-            end
-        end
-
-        %prepare shading
-        PupSEMNAc{v,1}=PoolAccNAc{v,1}+PoolSEMNAc{v,1};
-        PdownSEMNAc{v,1}=PoolAccNAc{v,1}-PoolSEMNAc{v,1};
-        PupSEMVP{v,1}=PoolAccVP{v,1}+PoolSEMVP{v,1};
-        PdownSEMVP{v,1}=PoolAccVP{v,1}-PoolSEMVP{v,1};
-        PupSEMNAcShuff{v,1}=PoolAccNAcShuff{v,1}+PoolSEMNAcShuff{v,1};
-        PdownSEMNAcShuff{v,1}=PoolAccNAcShuff{v,1}-PoolSEMNAcShuff{v,1};
-        PupSEMVPShuff{v,1}=PoolAccVPShuff{v,1}+PoolSEMVPShuff{v,1};
-        PdownSEMVPShuff{v,1}=PoolAccVPShuff{v,1}-PoolSEMVPShuff{v,1};
-
-        %here I plot the main lines for each condition that will have an entry
-        %in the legend (legend goes by order plotted)
-
-        %plotting decoder accuracy over time
-        subplot(2,3,2); %accumbens
-        hold on;
-        plot(xaxis,PoolAccNAc{v,1}(1:66),'Color', NAcP{v,1},'linewidth',1);
-        %plot(xaxis,PoolAccNAcShuff{v,1}(1:66),'Color', NAcShuff,'linewidth',3);
-        %plot(xaxis,NAcSig{v,1}-0.53,'*','Color', 'k');
-        %patch([xaxis,xaxis(end:-1:1)],[PupSEMNAc{v,1},PdownSEMNAc{v,1}(end:-1:1)],NAc{v,1},'EdgeColor','none');alpha(0.5);
-        %patch([xaxis,xaxis(end:-1:1)],[PupSEMNAcShuff{v,1},PdownSEMNAcShuff{v,1}(end:-1:1)],NAcShuff,'EdgeColor','none');alpha(0.5);
-        xlabel('Seconds post reward delivery');
-        ylabel('Accuracy');
-        title('NAc decoding accuracy');
-        axis([xaxis(1) xaxis(end) 0.4 1]);
-
-        subplot(2,3,3); %VP
-        hold on;
-        plot(xaxis,PoolAccVP{v,1}(1:66),'Color', VPP{v,1},'linewidth',1);
-        %plot(xaxis,PoolAccVPShuff{v,1}(1:66),'Color', VPShuff,'linewidth',3);
-        %plot(xaxis,VPSig{v,1}-0.53,'*','Color','k');
-        %patch([xaxis,xaxis(end:-1:1)],[PupSEMVP{v,1},PdownSEMVP{v,1}(end:-1:1)],VP{v,1},'EdgeColor','none');alpha(0.5);
-        %patch([xaxis,xaxis(end:-1:1)],[PupSEMVPShuff{v,1},PdownSEMVPShuff{v,1}(end:-1:1)],VPShuff,'EdgeColor','none');alpha(0.5);
-        xlabel('Seconds post reward delivery');
-        ylabel('Accuracy');
-        title('VP decoding accuracy');
-        axis([xaxis(1) xaxis(end) 0.4 1]);
-
-        %comparison at best bin
-        subplot(2,3,5);
-        hold on;
-        [~,NAcbin]=max(PoolAccNAc{v,1});
-        [~,VPbin]=max(PoolAccVP{v,1});
-        
-        if length(PoolDec{e,1}.True{v,1})>1 && length(PoolDec{e,2}.True{v,1})>1
-            %make matrices that will include best bin at all levels
-            A=cat(1,A,PoolDec{e,1}.True{v,1}(:,NAcbin));
-            B=cat(1,B,PoolDec{e,1}.Shuff{v,1}(:,NAcbin));
-            C=cat(1,C,PoolDec{e,2}.True{v,1}(:,VPbin));
-            D=cat(1,D,PoolDec{e,2}.Shuff{v,1}(:,VPbin));
-        end
-
-        errorbar(NumNeurons(v),PoolAccNAc{v,1}(NAcbin),PoolSEMNAc{v,1}(NAcbin),'*','Color', NAcP{v,1},'linewidth',1.5);
-        errorbar(NumNeurons(v),PoolAccVP{v,1}(VPbin),PoolSEMVP{v,1}(VPbin),'*','Color', VPP{v,1},'linewidth',1.5);
-        xlabel('Neurons used');
-        ylabel('Accuracy');
-        title('Accuracy for most predictive bin');
-        legend('NAc','VP','Location','southeast');
-        axis([0 155 0.5 1]); 
-
-
-        %plotting time of most accurate bin
-        subplot(2,3,6);
-        hold on;
-        errorbar(NumNeurons(v),nanmean(PeakBinsNAc{v,1}(:,2)),nanste(PeakBinsNAc{v,1}(:,2),1),'*','Color', NAcP{v,1},'linewidth',1.5);
-        errorbar(NumNeurons(v),nanmean(PeakBinsVP{v,1}(:,2)),nanste(PeakBinsVP{v,1}(:,2),1),'*','Color', VPP{v,1},'linewidth',1.5);
-        xlabel('Neurons used');
-        ylabel('Seconds post reward delivery')
-        title('Time of most accurate bin');
-        %legend('NAc','VP','Location','southeast');
-        axis([0 155 0 2.5]);
-        legend('NAc','VP','location','southeast')
-
-    end %conditions
-
-    %now plot the shading, significance, and the shuffled separately
-    for v=1:length(PoolDec{e,1}.True) %each condition (number of neurons used)
-        %plotting decoder accuracy over time
-        subplot(2,3,2); %accumbens
-        hold on;
-        plot(xaxis,PoolAccNAcShuff{v,1}(1:66),'Color', 'k','linewidth',1);
-        patch([xaxis,xaxis(end:-1:1)],[PupSEMNAc{v,1},PdownSEMNAc{v,1}(end:-1:1)],NAcP{v,1},'EdgeColor','none');alpha(0.5);
-        patch([xaxis,xaxis(end:-1:1)],[PupSEMNAcShuff{v,1},PdownSEMNAcShuff{v,1}(end:-1:1)],'k','EdgeColor','none');alpha(0.5);
-        xlabel('Seconds post reward delivery');
-        title('NAc decoding accuracy');
-        axis([xaxis(1) xaxis(end) 0.4 1]);
-        legend('10 units','25 units','50 units','100 units','150 units','Shuffled','location','northwest')    
-
-        subplot(2,3,3); %VP
-        hold on;
-        plot(xaxis,PoolAccVPShuff{v,1}(1:66),'Color', 'k','linewidth',1);
-        patch([xaxis,xaxis(end:-1:1)],[PupSEMVP{v,1},PdownSEMVP{v,1}(end:-1:1)],VPP{v,1},'EdgeColor','none');alpha(0.5);
-        patch([xaxis,xaxis(end:-1:1)],[PupSEMVPShuff{v,1},PdownSEMVPShuff{v,1}(end:-1:1)],'k','EdgeColor','none');alpha(0.5);
-        xlabel('Seconds post reward delivery');
-        title('VP decoding accuracy');
-        axis([xaxis(1) xaxis(end) 0.4 1]);
-        legend('10 units','25 units','50 units','100 units','150 units','Shuffled','location','northwest')
-        
-    end
-
-    %stats!
-    %ANOVA for effects of number of neurons and region on time of peak accuracy
-    %time
-    PeakTimes=[];
-    for i=1:length(NumNeurons)
-        if length(PoolDec{e,1}.True{i,1})>1 && length(PoolDec{e,2}.True{i,1})>1
-            PeakTimes(1:repetitions,i)=PeakBinsNAc{i,1}(:,2);
-            PeakTimes(1+repetitions:repetitions+repetitions,i)=PeakBinsVP{i,1}(:,2);
-        end
-    end
-    [~,R_2R.PoolDecTimeStats{e,1},R_2R.PoolDecTimeStats{e,2}]=anova2(PeakTimes,repetitions,'off'); %columns is ensemble size, rows is region
-
-    %ANOVA for effect of ensemble size, region, and shuff vs true on accuracy
-    %of peak bin
-    size=[];
-    for i=1:length(NumNeurons)
-        if length(PoolDec{e,1}.True{i,1})>1 && length(PoolDec{e,2}.True{i,1})>1
-            size=cat(1,size,i*ones(repetitions,1));
-        end
-    end
-    
-    %with shuff vs true
-%     size2=cat(1,size,size,size,size);
-%     shuffd=cat(1,ones(length(A),1),zeros(length(B),1),ones(length(C),1),zeros(length(D),1));
-%     region=cat(1,ones(length(A)+length(B),1),zeros(length(C)+length(D),1));
-%     [~,R_2R.AccAnova,~]=anovan(cat(1,A,B,C,D),{shuffd,region,size2},'varnames',{'real vs shuffled','region','ens size'},'display','off','model','full');
-
-    %without shuff vs true
-    size3=cat(1,size,size);
-    region3=cat(1,ones(length(A),1),zeros(length(C),1));
-    [~,R_2R.PoolDecAccStats{e,1},R_2R.PoolDecAccStats{e,2}]=anovan(cat(1,A,C),{region3,size3},'varnames',{'region','ens size'},'display','off','model','full');
-    
-    
-end %selections
-
-save('R_2R.mat','R_2R');