mne.stats.fdr_correction#

mne.stats.fdr_correction(pvals, alpha=0.05, method='indep')[source]#

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
pvalsarray_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#

Statistical inference

Statistical inference

Statistical inference
Mass-univariate twoway repeated measures ANOVA on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power
FDR correction on T-test on sensor data

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

Analysing continuous features with binning and regression in sensor space

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