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A mistake in my False discovery rate (FDR) correction script

August 8th, 2016

I have posted an FDR script at http://www.alivelearn.net/?p=1840. I noticed that there is a small bug. In rare cases, this bug will cause the most significant voxel to be classified as ‘non-significant’ while other voxels are ’significant’.

Consider the following example:

p = [0.8147 0.9058 0.0030 0.9134 0.6324 0.0029 0.2785 0.5469 0.9575 0.9649 0.1576 0.9706 0.9572 0.4854 0.8003 0.1419 0.4218 0.9157];

The previous script will classify p(3) as significant but p(6) as non-significant.

Here is the updated version of the script:

function y = fdr0(p, q)
% y = fdr0(p, q)
%
% to calculate whether a pvalue survive FDR corrected q
%
% p: an array of p values. (e.g. p values for each channel)
% q: desired FDR threshold (typically 0.05)
% y: an array of the same size with p with only two possible values. 0
% means this position (channel) does not survive the threshold, 1 mean it
% survives
%
% Ref:
% Genovese et al. (2002). Thresholding statistical maps in functional
% neuroimaging using the false discovery rate. Neuroimage, 15:722-786.
%
% Example:
%   y = fdr0(rand(10,1),0.5);
%
% Xu Cui
% 2016/3/14
%

pvalue = p;
y = 0 * p;

[sortedpvalue, sortedposition] = sort(pvalue);
v = length(sortedposition);
for ii=1:v
    if q*ii/v >= sortedpvalue(ii)
        y(sortedposition(1:ii)) = 1;
    end
end

return;
Author: Xu Cui Categories: brain, matlab, nirs, programming Tags:
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  1. August 8th, 2016 at 12:56 | #1

    There is a small bug in this script. An updated version can be found at http://www.alivelearn.net/?p=1914

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