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Pearson.m
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Pearson.m
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function [r,t,pval,hboot,CI,pboot] = Pearson(X,Y,fig_flag,level)
% compute the Pearson correlation along with the bootstrap CI
%
% FORMAT: [r,t,p] = Pearson(X,Y)
% [r,t,p] = Pearson(X,Y,fig_flag,level)
% [r,t,p,hboot,CI] = Pearson(X,Y,fig_flag,level)
%
% INPUTS: X and Y are 2 vectors or matrices, in the latter case,
% correlations are computed column-wise
% fig_flag indicates to plot (default = 1) the data or not (0)
% level is the desired alpha level (default = 5%)
%
% OUTPUTS: r is the Pearson correlation
% t is the associated t value
% pval is the corresponding p value
%
% optional:
%
% hboot 1/0 declares the test significant based on CI
% CI is the percentile bootstrap confidence interval
%
% If X and Y are matrices of size [n p], p correlations are computed
% consequently, the CI are adjusted at a level alpha/p (Bonferonni
% correction) and hboot is based on these adjusted CI (pval remain
% uncorrected)
% Cyril Pernet v1
% ---------------------------------
% Copyright (C) Corr_toolbox 2012
%% data check
% if X a vector and Y a matrix,
% repmat X to perform multiple tests on Y (or the other around)
if size(X,1) == 1 && size(X,2) > 1; X = X'; end
if size(Y,1) == 1 && size(Y,2) > 1; Y = Y'; end
if size(X,2) == 1 && size(Y,2) > 1
X = repmat(X,1,size(Y,2));
elseif size(Y,2) == 1 && size(X,2) > 1
Y = repmat(Y,1,size(X,2));
end
if sum(size(X)~=size(Y)) ~= 0
error('X and Y must have the same size')
end
%% parameters
if nargin < 2
error('two inputs requested');
elseif nargin == 2
fig_flag = 1;
level = 5/100;
elseif nargin == 3
level = 5/100;
end
[n p] = size(X);
%% basic Pearson
% compute r
r = sum(detrend(X,'constant').*detrend(Y,'constant')) ./ ...
(sum(detrend(X,'constant').^2).*sum(detrend(Y,'constant').^2)).^(1/2);
t = r.*sqrt((n-2)./(1-r.^2));
pval = 2*tcdf(-abs(t),n-2);
if nargout > 3
% adjust boot parameters
if p == 1
nboot = 599;
% adjust percentiles following Wilcox
if n < 40
low = 7 ; high = 593;
elseif n >= 40 && n < 80
low = 8 ; high = 592;
elseif n >= 80 && n < 180
low = 11 ; high = 588;
elseif n >= 180 && n < 250
low = 14 ; high = 585;
elseif n >= 250
low = 15 ; high = 584;
end
else
nboot = 1000;
level = level / p;
% Bonferonni correction
low = round((level*nboot)/2);
if low == 0
error('adjusted CI cannot be computed, too many tests for the number of observations')
else
high = nboot - low;
end
end
% compute hboot and CI
table = randi(n,n,nboot);
for B=1:nboot
rb(B,:) = sum(detrend(X(table(:,B),:),'constant').*detrend(Y(table(:,B),:),'constant')) ./ ...
(sum(detrend(X(table(:,B),:),'constant').^2).*sum(detrend(Y(table(:,B),:),'constant').^2)).^(1/2);
for c=1:size(X,2)
b = pinv([X(table(:,B),c) ones(n,1)])*Y(table(:,B),c);
slope(B,c) = b(1);
intercept(B,c) = b(2,:);
end
end
rb = sort(rb,1);
[slope,index] = sort(slope,1);
% in theory we keep the slope/intercept pair, thus:
% intercept = intercept(index); % but doesn't work?
intercept = sort(intercept,1);
% CI and h
adj_nboot = nboot - sum(isnan(rb));
adj_low = round((level*adj_nboot)/2);
adj_high = adj_nboot - adj_low;
for c=1:size(X,2)
CI(:,c) = [rb(adj_low(c),c) ; rb(adj_high(c),c)];
hboot(c) = (rb(adj_low(c),c) > 0) + (rb(adj_high(c),c) < 0);
CIslope(:,c) = [slope(adj_low(c),c) ; slope(adj_high(c),c)];
CIintercept(:,c) = [intercept(adj_low(c),c) ; intercept(adj_high(c),c)];
end
end
%% plots
if fig_flag ~= 0
answer = [];
if p > 1
answer = questdlg(['plots all ' num2str(p) ' correlations'],'Plotting option','yes','no','yes');
else
if fig_flag == 1
figure('Name','Pearson correlation');
set(gcf,'Color','w');
end
if nargout>3
subplot(1,2,1);
M = sprintf('Pearson corr r=%g \n %g%%CI [%g %g]',r,(1-level)*100,CI(1),CI(2));
else
M = sprintf('Pearson corr r=%g \n p=%g',r,pval);
end
scatter(X,Y,100,'filled'); grid on
xlabel('X','FontSize',14); ylabel('Y','FontSize',14);
title(M,'FontSize',16);
h=lsline; set(h,'Color','r','LineWidth',4);
box on;set(gca,'Fontsize',14)
if nargout>3 % if bootstrap done plot CI
y1 = refline(CIslope(1),CIintercept(1)); set(y1,'Color','r');
y2 = refline(CIslope(2),CIintercept(2)); set(y2,'Color','r');
y1 = get(y1); y2 = get(y2);
xpoints=[[y1.XData(1):y1.XData(2)],[y2.XData(2):-1:y2.XData(1)]];
step1 = y1.YData(2)-y1.YData(1); step1 = step1 / (y1.XData(2)-y1.XData(1));
step2 = y2.YData(2)-y2.YData(1); step2 = step2 / (y2.XData(2)-y2.XData(1));
filled=[[y1.YData(1):step1:y1.YData(2)],[y2.YData(2):-step2:y2.YData(1)]];
hold on; fillhandle=fill(xpoints,filled,[1 0 0]);
set(fillhandle,'EdgeColor',[1 0 0],'FaceAlpha',0.2,'EdgeAlpha',0.8);%set edge color
subplot(1,2,2); k = round(1 + log2(length(rb))); hist(rb,k); grid on;
title({'Bootstrapped correlations';['h=',num2str(hboot)]},'FontSize',16); hold on
xlabel('boot correlations','FontSize',14);ylabel('frequency','FontSize',14)
plot(repmat(CI(1),max(hist(rb,k)),1),[1:max(hist(rb,k))],'r','LineWidth',4);
plot(repmat(CI(2),max(hist(rb,k)),1),[1:max(hist(rb,k))],'r','LineWidth',4);
axis tight; colormap([.4 .4 1])
box on;set(gca,'Fontsize',14)
end
end
if strcmp(answer,'yes')
for f = 1:p
if fig_flag == 1
figure('Name',[num2str(f) ' boostrapped Pearson correlation'])
set(gcf,'Color','w');
end
if nargout>3
subplot(1,2,1);
M = sprintf('Pearson corr r=%g \n %g%%CI [%g %g]',r(f),(1-level)*100,CI(1,f),CI(2,f));
else
M = sprintf('Pearson corr r=%g p=%g',r(f),pval(f));
end
scatter(X(:,f),Y(:,f),100,'filled'); grid on
xlabel('X','FontSize',14); ylabel('Y','FontSize',14);
title(M,'FontSize',16);
h=lsline; set(h,'Color','r','LineWidth',4);
box on;set(gca,'Fontsize',14)
if nargout>3
y1 = refline(CIslope(1,f),CIintercept(1,f)); set(y1,'Color','r');
y2 = refline(CIslope(2,f),CIintercept(2,f)); set(y2,'Color','r');
y1 = get(y1); y2 = get(y2);
xpoints=[[y1.XData(1):y1.XData(2)],[y2.XData(2):-1:y2.XData(1)]];
step1 = y1.YData(2)-y1.YData(1); step1 = step1 / (y1.XData(2)-y1.XData(1));
step2 = y2.YData(2)-y2.YData(1); step2 = step2 / (y2.XData(2)-y2.XData(1));
filled=[[y1.YData(1):step1:y1.YData(2)],[y2.YData(2):-step2:y2.YData(1)]];
hold on; fillhandle=fill(xpoints,filled,[1 0 0]);
set(fillhandle,'EdgeColor',[1 0 0],'FaceAlpha',0.2,'EdgeAlpha',0.8);%set edge color
subplot(1,2,2); k = round(1 + log2(length(rb(:,f)))); hist(rb(:,f),k); grid on;
title({'Bootstrapped correlations';['h=',num2str(hboot(f))]},'FontSize',16); hold on
xlabel('boot correlations','FontSize',14);ylabel('frequency','FontSize',14)
plot(repmat(CI(1,f),max(hist(rb(:,f),k)),1),[1:max(hist(rb(:,f),k))],'r','LineWidth',4);
plot(repmat(CI(2,f),max(hist(rb(:,f),k)),1),[1:max(hist(rb(:,f),k))],'r','LineWidth',4);
axis tight; colormap([.4 .4 1])
box on;set(gca,'Fontsize',14)
end
end
end
end