mne.stats.fdr_correction

mne.stats.fdr_correction(pvals, alpha=0.05, method='indep')

P-value correction with False Discovery Rate (FDR).

Correction for multiple comparison using FDR [1].

This covers Benjamini/Hochberg for independent or positively correlated and Benjamini/Yekutieli for general or negatively correlated tests.

Parameters:

pvalsnumpy:array_like

Set of p-values of the individual tests.

alphafloat

Error rate.

method‘indep’ | ‘negcorr’

If ‘indep’ it implements Benjamini/Hochberg for independent or if ‘negcorr’ it corresponds to Benjamini/Yekutieli.

Returns:

rejectarray, bool

True if a hypothesis is rejected, False if not.

pval_correctedarray

P-values adjusted for multiple hypothesis testing to limit FDR.

References

[1]

Christopher R. Genovese, Nicole A. Lazar, and Thomas Nichols. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15(4):870–878, 2002. doi:https://doi.org/10.1006/nimg.2001.1037.

Examples using mne.stats.fdr_correction

FDR correction on T-test on sensor data

FDR correction on T-test on sensor data

Analysing continuous features with binning and regression in sensor space

Analysing continuous features with binning and regression in sensor space

Statistical inference

Statistical inference

Mass-univariate twoway repeated measures ANOVA on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power