spm_uc_FDR.m

```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
%         (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
%
%
% 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;

%-----------------------------------------------------------------------
if isstruct(Vs)
if (n ~= length(Vs))
error(sprintf('n & number of mapped images doesn''t match (%d,%d)',...
n,length(Vs)));
end
for i = 2:n
end
if ~isempty(Vm)
if isstruct(Vm)
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```