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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;
```
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 About the author: Xu Cui is a human brain research scientist in Stanford University. He lives in the Bay Area in the United States. He is also the founder of Stork (smart publication alert app), PaperBox and BizGenius.   He was born in He'nan province, China. He received education in Beijing University(BS), University of Tennessee (Knoxville) (MS), Baylor College of Medicine (PhD) and Stanford University (PostDoc). Read more ...
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