function [u,Ps,Ts] = spm_uc_FDR(q,df,STAT,n,Vs,Vm)
% False Discovery critical height threshold
% FORMAT [u,Ps,Ts] = spm_uc_FDR(q,df,STAT,n,Vs[,Vm])
%
% q - critical expected False Discovery Rate
% df - [df{interest} df{residuals}]
% STAT - Statisical feild (see comments below about FWER and EFDR)
% 'Z' - Gaussian feild
% 'T' - T - feild
% 'X' - Chi squared feild
% 'F' - F - feild
% n - number of component SPMs in conjunction
% Vs - Mapped statistic image(s)
% -or-
% Vector of sorted p-values, p1<p2<... (saves i/o w/ repeated calls)
% Vm - Mask in 1 of 3 forms
% o Scalar, indicating implicit mask value in statistic image(s)
% o Vector of indicies of elements within mask
% o Mapped mask image
% (Ignored if Vs is a vector.)
%
% u - critical height
% Ps - Sorted p-values
% Ts - Sorted statistic values
%
%___________________________________________________________________________
%
% The Benjamini & Hochberch (1995) False Discovery Rate (FDR) procedure
% finds a threshold u such that the expected FDR is at most q.
% spm_uc_FDR returns this critical threshold u.
%
% For repeated use of a given statistic image, return Ps in the place
% of Vs:
% [P Ps] = spm_uc_FDR(Z1,df,STAT,n,Vs); %-Initialization, image read
% P = spm_uc_FDR(Z2,df,STAT,n,Ps); %-Image not read, Ps used
%
% Note that a threshold of Inf is possible if q is very small. This
% means that there is no threshold such that FDR is controlled at q.
%
%
% Background
%
% For a given threshold on a statistic image, the False Discovery Rate
% is the proportion of suprathreshold voxels which are false positives.
% Recall that the thresholding of each voxel consists of a hypothesis
% test, where the null hypothesis is rejected if the statistic is larger
% than threshold. In this terminology, the FDR is the proportion of
% rejected tests where the null hypothesis is actually true.
%
% A FDR proceedure produces a threshold that controls the expected FDR
% at or below q. The FDR adjusted p-value for a voxel is the smallest q
% such that the voxel would be suprathreshold.
%
% In comparison, a traditional multiple comparisons proceedure
% (e.g. Bonferroni or random field methods) controls Familywise Error
% rate (FWER) at or below alpha. FWER is the chance of one or more
% false positives *anywhere* (not just among suprathreshold voxels). A
% FWER adjusted p-value for a voxel is the smallest alpha such that the
% voxel would be suprathreshold.
%
%
% If there is truely no signal in the image anywhere, then a FDR
% proceedure controls FWER, just as Bonferroni and random field methods
% do. (Precisely, controlling E(FDR) yeilds weak control of FWE). If
% there *is* some signal in the image, a FDR method will be more powerful
% than a traditional method.
%
%
% References
%
% Benjamini & Hochberg (1995), "Controlling the False Discovery Rate: A
% Practical and Powerful Approach to Multiple Testing". J Royal Stat Soc,
% Ser B. 57:289-300.
%
% Benjamini & Yekutieli (2001), "The Control of the false discovery rate
% in multiple testing under dependency". To appear, Annals of Statistics.
% Available at http://www.math.tau.ac.il/~benja
%___________________________________________________________________________
% @(#)spm_uc_FDR.m 1.11 Thomas Nichols 02/11/17
if (nargin<6), Vm = []; end
% Set Benjamini & Yeuketeli cV for independence/PosRegDep case
%-----------------------------------------------------------------------
cV = 1;
% Load, mask & sort statistic image (if needed)
%-----------------------------------------------------------------------
if isstruct(Vs)
if (n ~= length(Vs))
error(sprintf('n & number of mapped images doesn''t match (%d,%d)',...
n,length(Vs)));
end
Ts = spm_read_vols(Vs(1));
for i = 2:n
Ts = min(Ts,spm_read_vols(Vs(i)));
end
if ~isempty(Vm)
if isstruct(Vm)
Ts(spm_read_vols(Vm)==0) = [];
elseif (prod(size(Vm))==1)
Ts(Ts==Vm) = [];
else
Ts = Ts(Vm);
end
end
Ts(isnan(Ts)) = [];
Ts = flipud(sort(Ts(:)));
end
% Calculate p values of image (if needed)
%-----------------------------------------------------------------------
if isstruct(Vs)
if STAT == 'Z'
Ps = (1-spm_Ncdf(Ts)).^n;
elseif STAT == 'T'
Ps = (1-spm_Tcdf(Ts,df(2))).^n;
elseif STAT == 'X'
Ps = (1-spm_Xcdf(Ts,df(2))).^n;
elseif STAT == 'F'
Ps = (1-spm_Fcdf(Ts,df)).^n;
end
else
Ps = Vs;
end
S = length(Ps);
% Calculate FDR inequality RHS
%-----------------------------------------------------------------------
Fi = (1:S)'/S*q/cV;
% Find threshold
%-----------------------------------------------------------------------
I = max(find(Ps<=Fi));
if isempty(I)
u = Inf;
else
if isstruct(Vs)
u = Ts(I);
else
% We don't have original statistic values; determine from p-value
if STAT == 'Z'
u = spm_invNcdf(1-Ps(I));
elseif STAT == 'T'
u = spm_invTcdf(1-Ps(I),df(2));
elseif STAT == 'X'
u = spm_invXcdf(1-Ps(I),df(2));
elseif STAT == 'F'
u = spm_invFcdf(1-Ps(I),df);
end
end
end