% function [dbar,tval,P,MLE,likelihood]=


M

Martin

% phyl_paired_ttest(C, x1, SEx1, x2, SEx2,MinMaxeps,MinMaxlam)
% Takes C (tree matrix), x1 (n x 1 vector), SEx1 (n x 1 vector)
% x2, SEx2, [MIN MAX] values for grid search on epsilon (eps) and
lambda
% (lam).
% Returns dbar, tval, P (P-value), MLEs for epsilon, sigma^2, and
lambda,
% and the log-likelihood.
% Optimization is performed on a 100 x 100 grid.
function [dbar,tval,P,MLE,likelihood]=phyl_paired_ttest(C, x1, SEx1,
x2,
SEx2,MinMaxeps,MinMaxlam)
% set grid search criterion
STEP=0.01; % *100%
% number of parameters
np=3;
% number of taxa
n=max(size(C));
% compute differences
d=x1-x2;
% compute a matrix of estimated sampling variances
E=diag(SEx1.^2+SEx2.^2);
% create column vector of 1.0s
one=ones(n,1);
% set grid search for epsilon & lambda
lambda=MinMaxlam(1):STEP*(MinMaxlam(2)-MinMaxlam(1)):MinMaxlam(2);
epsilon=MinMaxeps(1):STEP*(MinMaxeps(2)-MinMaxeps(1)):MinMaxeps(2);
% determine if the user has fixed epsilon or lambda
if (MinMaxeps(2)-MinMaxeps(1))==0
epsilon=MinMaxeps;
np=np-1;
end
if (MinMaxlam(2)-MinMaxlam(1))==0
lamda=MinMaxlam;
np=np-1;
end
% OR uncomment to fix lambda at one or zero or epsilon at zero
% lambda=[0 0]; np=np-1;
% lambda=[1 1]; np=np-1;
% epsilon=[0 0]; np=np-1;
% ok, now estimate lambda & epsilon by maximizing the likelihood
maxL=-Inf; minL=Inf;
logL=ones(max(size(lambda)),max(size(epsilon)))*(-Inf);
for i=1:max(size(lambda))
for j=1:max(size(epsilon))
% transform by lambda
Cl=lambda_transform(lambda(i),C);
% add sampling error
V=Cl+epsilon(j)*E;
% check positive definiteness
[R p]=chol(V);
if(p==0)
% compute parameter estimates and likelihood
dbar=(one'*V^-1*one)^-1*(one'*V^-1*d);
sigma2=(d-dbar*one)'*V^-1*(d-dbar*one)*n^-1;
logL(i,j)=-(1/2)*(d-dbar*one)'*(sigma2*V)^-1*(d-dbar*one)...
-(1/2)*log(det(sigma2*V))-(n/2)*log(2*pi);
if logL(i,j)>maxL
MLpos=[i j];
maxL=logL(i,j);
MLE.sigma2=sigma2;
end
if logL(i,j)<minL
minL=logL(i,j);
minLpos=[i j];
end
end
end
end
% create plot of likelihood surfaces
colormap(sort(gray,'descend'));
eps=char(hex2dec('65')); lam=char(hex2dec('6c'));
subplot(2,2,1); mesh(epsilon,lambda,logL);
xlabel(eps,'fontname','symbol'); ylabel(lam,'fontname','symbol');
zlabel('log(L)');
title('A)
');
subplot(2,2,3); plot(lambda,logL:),MLpos(2)),'k');
xlabel(lam,'fontname','symbol'); ylabel('log(L)');
title('C)
');
subplot(2,2,4); plot(epsilon,logL(MLpos(1),:),'k');
xlabel(eps,'fontname','symbol'); ylabel('log(L)');
title('D)
');
% replace -Inf with minL
for i=1:max(size(lambda))
for j=1:max(size(epsilon))
if logL(i,j)==-Inf
logL(i,j)=minL;
end
end
end
subplot(2,2,2); contour(epsilon,lambda,logL,2000);
xlabel(eps,'fontname','symbol'); ylabel(lam,'fontname','symbol');
title('B)
');
suptitle('Likelihood Surfaces');
MLE.lambda=lambda(MLpos(1));
MLE.epsilon=epsilon(MLpos(2));
likelihood=maxL;
% pause
pause(1);
% ok, now perform calculations using MLlambda & MLepsilon
Cl=lambda_transform(MLE.lambda,C);
% calculate mean difference
V=Cl+MLE.epsilon*E;
dbar=(one'*V^-1*one)^-1*(one'*V^-1*d);
% calculate SE of dbar
SEdbar=sqrt(MLE.sigma2*(n/(n-np))*(one'*V^-1*one)^-1);
MLE.sig2e=MLE.sigma2*MLE.epsilon;
% calculate t-statistic
tval=dbar*SEdbar^-1;
% calculate P-value
P=2*(1-tcdf(abs(tval),n-np));
% print results to screen
fprintf(1,'dbar = %f\tt(df = %d) = %f\t',dbar,n-np,tval);
fprintf(1,'P(t) = %f\n',P);
fprintf(1,'MLE(lambda) = %f\tlog(L) = %f\n',MLE.lambda,maxL);
fprintf(1,'MLE(epsilon) = %f\tlog(L) = %f\n',MLE.epsilon,maxL);
fprintf(1,'MLE(sig2e) = %f\tlog(L) = %f\n',MLE.sig2e,maxL);
% done
% function Ml=lambda_transform(lambda,M)
% Multiplies off-diagonals of square matrix M by lambda
% and returns Ml.
function Ml=lambda_transform(lambda,M)
Ml=M;
n=max(size(M));
% transform by lambda
for i=1:n
for j=(i+1):n
Ml(i,j)=M(i,j)*lambda;
Ml(j,i)=Ml(i,j);
end
end
% done
 
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