NHP-pRF/Toolboxes/chronux/spectral_analysis/statistical_tests/two_group_test_spectrum.m

function [dz,vdz,Adz]=two_group_test_spectrum(J1,J2,p,plt,f)
% function [dz,vdz,Adz]=two_group_test_spectrum(J1,J2,p,plt,f)
% Test the null hypothesis (H0) that data sets J1, J2 in 
% two conditions c1,c2 have equal population spectrum
%
% Usage:
% [dz,vdz,Adz]=two_sample_test_spectrum(J1,J2,p)
%
% Inputs:
% J1   tapered fourier transform in condition 1
% J2   tapered fourier transform in condition 2
% p      p value for test (default: 0.05)
% plt    'y' for plot and 'n' for no plot
% f      frequencies (useful for plotting)
%
%
% Dimensions: J1: frequencies x number of samples in condition 1
%             J2: frequencies x number of samples in condition 2
%              number of samples = number of trials x number of tapers
% Outputs:
% dz    test statistic (will be distributed as N(0,1) under H0
% vdz   Arvesen estimate of the variance of dz
% Adz   1/0 for accept/reject null hypothesis of equal population
%       coherences based dz ~ N(0,1)
% 
% 
% Note: all outputs are functions of frequency
%
% References: Arvesen, Jackkknifing U-statistics, Annals of Mathematical
% Statisitics, vol 40, no. 6, pg 2076-2100 (1969)

if nargin < 2; error('Need four sets of Fourier transforms'); end;
if nargin < 4 || isempty(plt); plt='n'; end;
% 
% Test for matching dimensionalities
%
m1=size(J1,2); % number of samples, condition 1
m2=size(J2,2); % number of samples, condition 2
dof1=m1; % degrees of freedom, condition 1
dof2=m2; % degrees of freedom, condition 2
if nargin < 5 || isempty(f); f=size(J1,1); end;
if nargin < 3 || isempty(p); p=0.05; end; % set the default p value

%
% Compute the individual condition spectra, coherences
%
S1=conj(J1).*J1; % spectrum, condition 1
S2=conj(J2).*J2; % spectrum, condition 2

Sm1=squeeze(mean(S1,2)); % mean spectrum, condition 1
Sm2=squeeze(mean(S2,2)); % mean spectrum, condition 2
%
% Compute the statistic dz, and the probability of observing the value dz
% given an N(0,1) distribution i.e. under the null hypothesis
%
bias1=psi(dof1)-log(dof1); bias2=psi(dof2)-log(dof2); % bias from Thomson & Chave
var1=psi(1,dof1); var2=psi(1,dof2); % variance from Thomson & Chave
z1=log(Sm1)-bias1; % Bias-corrected Fisher z, condition 1
z2=log(Sm2)-bias2; % Bias-corrected Fisher z, condition 2
dz=(z1-z2)/sqrt(var1+var2); % z statistic

% Bug fix 
% pdz=normpdf(dz,0,1); % probability of observing value dz
pdz = 2*normcdf(-abs(dz),0,1); % probability of observing value dz

%
% The remaining portion of the program computes Jackknife estimates of the mean (mdz) and variance (vdz) of dz
% 
samples1=[1:m1];
samples2=[1:m2];
%
% Leave one out of one sample
%
bias11=psi(dof1-1)-log(dof1-1); var11=psi(1,dof1-1);
for i=1:m1;
    ikeep=setdiff(samples1,i); % all samples except i
    Sm1=squeeze(mean(S1(:,ikeep),2)); % 1 drop mean spectrum, data 1, condition 1
    z1i(:,i)=log(Sm1)-bias11; % 1 drop, bias-corrected Fisher z, condition 1
    dz1i(:,i)=(z1i(:,i)-z2)/sqrt(var11+var2); % 1 drop, z statistic, condition 1
    ps1(:,i)=m1*dz-(m1-1)*dz1i(:,i);
end; 
ps1m=mean(ps1,2);
bias21=psi(dof2-1)-log(dof2-1); var21=psi(1,dof2-1);
for j=1:m2;
    jkeep=setdiff(samples2,j); % all samples except j
    Sm2=squeeze(mean(S2(:,jkeep),2)); % 1 drop mean spectrum, data 2, condition 2
    z2j(:,j)=log(Sm2)-bias21; % 1 drop, bias-corrected Fisher z, condition 2
    dz2j(:,j)=(z1-z2j(:,j))/sqrt(var1+var21); % 1 drop, z statistic, condition 2
    ps2(:,j)=m2*dz-(m2-1)*dz2j(:,j);
end;
%
% Leave one out, both samples
% and pseudo values
% for i=1:m1;
%     for j=1:m2;
%         dzij(:,i,j)=(z1i(:,i)-z2j(:,j))/sqrt(var11+var21);
%         dzpseudoval(:,i,j)=m1*m2*dz-(m1-1)*m2*dz1i(:,i)-m1*(m2-1)*dz2j(:,j)+(m1-1)*(m2-1)*dzij(:,i,j);
%     end;
% end;
%
% Jackknife mean and variance
%
% dzah=sum(sum(dzpseudoval,3),2)/(m1*m2);
ps2m=mean(ps2,2);
% dzar=(sum(ps1,2)+sum(ps2,2))/(m1+m2);
vdz=sum((ps1-ps1m(:,ones(1,m1))).*(ps1-ps1m(:,ones(1,m1))),2)/(m1*(m1-1))+sum((ps2-ps2m(:,ones(1,m2))).*(ps2-ps2m(:,ones(1,m2))),2)/(m2*(m2-1));
% vdzah=sum(sum((dzpseudoval-dzah(:,ones(1,m1),ones(1,m2))).*(dzpseudoval-dzah(:,ones(1,m1),ones(1,m2))),3),2)/(m1*m2);
%
% Test whether H0 is accepted at the specified p value
%
Adz=zeros(size(dz));
x=norminv([p/2 1-p/2],0,1);
indx=find(dz>=x(1) & dz<=x(2)); 
Adz(indx)=1;

% Adzar=zeros(size(dzar));
% indx=find(dzar>=x(1) & dzar<=x(2)); 
% Adzar(indx)=1;
% 
% Adzah=zeros(size(dzah));
% indx=find(dzah>=x(1) & dzah<=x(2)); 
% Adzah(indx)=1;
if strcmp(plt,'y');
    if isempty(f) || nargin < 5;
        f=linspace(0,1,length(dz));
    end;
    %
    % Compute the coherences
    %
    S1=squeeze(mean(conj(J1).*J1,2));
    S2=squeeze(mean(conj(J2).*J2,2));
    %
    % Plot the coherence
    %
    subplot(311); 
    plot(f,S1,f,S2); legend('Data 1','Data 2');
    set(gca,'FontName','Times New Roman','Fontsize', 16);
    ylabel('Spectra');
    title('Two group test for spectrum');
    subplot(312);
    plot(f,dz);
    set(gca,'FontName','Times New Roman','Fontsize', 16);
    ylabel('Test statistic');
    conf=norminv(1-p/2,0,1);
    line(get(gca,'xlim'),[conf conf]);
    line(get(gca,'xlim'),[-conf -conf]);
    subplot(313);
    plot(f,vdz);
    set(gca,'FontName','Times New Roman','Fontsize', 16);
    xlabel('frequency'); ylabel('Jackknifed variance');
end;