[FieldTrip] Inconsistency between stat.mask and p-values in ft_freqstatistics
mikkelcv at drcmr.dk
mikkelcv at drcmr.dk
Wed Mar 20 10:32:11 CET 2024
Dear Guillermo
If I remember correctly, the mask for the two-tailed test is based on cfg.alpha / 2.
Best regards
Mikkel
Fra: fieldtrip <fieldtrip-bounces at science.ru.nl> på vegne af Guillermo Sanchez-Garrido Campos via fieldtrip <fieldtrip at science.ru.nl>
Dato: onsdag, 20. marts 2024 kl. 10.25
Til: fieldtrip at science.ru.nl <fieldtrip at science.ru.nl>
Cc: Guillermo Sanchez-Garrido Campos <gsancam at upo.es>
Emne: [FieldTrip] Inconsistency between stat.mask and p-values in ft_freqstatistics
Hello FieldTrip team,
I've been using the ft_freqstatistics function to perform statistical analysis on my LFP data (recorded with Neuropixels system). I've noticed a discrepancy between the values in stat.mask and the p-values in stat.prob, which has caused confusion in interpreting the results.
Detailed Description:
I have configured my analysis as follows:
1. Calculation of Power Spectrum:
cfg = [];
cfg.method = 'mtmfft';
cfg.output = 'pow';
cfg.taper = 'hanning';
cfg.foilim = [1 100]; % Frequency range
cfg.polyremoval = -1;
cfg.pad = 'nextpow2';
datafftpre = ft_freqanalysis(cfg, datapre); % Same for post
1. Statistical Analysis:
cfg = [];
cfg.method = 'montecarlo';
cfg.statistic = 'ft_statfun_depsamplesT';
cfg.correctm = 'no';
cfg.alpha = 0.05;
cfg.tail = 0;
cfg.correcttail = 'prob';
cfg.computestat = 'yes';
cfg.computeprob = 'yes';
cfg.computecritval = 'yes';
% Design configuration for dependent groups
nsubj = size(data, 1);
design = zeros(2, 2 * nsubj);
design(1, 1:nsubj) = 1;
design(1, nsubj+1:2*nsubj) = 2;
design(2, :) = repmat(1:nsubj, 1, 2);
cfg.design = design;
cfg.ivar = 1;
cfg.uvar = 2;
stat = ft_freqstatistics(cfg, datafftpre, data);
However, upon inspecting the results, I noticed that stat.mask did not match the p-values in stat.prob. I expected the values in stat.mask to reflect the p-values lower than cfg.alpha, but this was not the case.
Input Data:
My data is organized into two 3D matrices, datafftpre and datafftpost, each with 3 subjects (1st dimension), 310 rows (recording channels), and 649 columns (frequencies). Each cell contains the value of the power spectrum for each subject, channel, and frequency.
I appreciate any guidance or suggestions on how to correctly interpret the results of my analysis.
Thank you for your attention and support.
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