/
multi_comp.py
101 lines (82 loc) · 2.96 KB
/
multi_comp.py
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# Authors: Josef Pktd and example from H Raja and rewrite from Vincent Davis
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# Code borrowed from statsmodels
#
# License: BSD (3-clause)
import numpy as np
def _ecdf(x):
"""No frills empirical cdf used in fdrcorrection."""
nobs = len(x)
return np.arange(1, nobs + 1) / float(nobs)
def fdr_correction(pvals, alpha=0.05, method='indep'):
"""P-value correction with False Discovery Rate (FDR).
Correction for multiple comparison using FDR.
This covers Benjamini/Hochberg for independent or positively correlated and
Benjamini/Yekutieli for general or negatively correlated tests.
Parameters
----------
pvals : array_like
set of p-values of the individual tests.
alpha : float
error rate
method : 'indep' | 'negcorr'
If 'indep' it implements Benjamini/Hochberg for independent or if
'negcorr' it corresponds to Benjamini/Yekutieli.
Returns
-------
reject : array, bool
True if a hypothesis is rejected, False if not
pval_corrected : array
pvalues adjusted for multiple hypothesis testing to limit FDR
Notes
-----
Reference:
Genovese CR, Lazar NA, Nichols T.
Thresholding of statistical maps in functional neuroimaging using the false
discovery rate. Neuroimage. 2002 Apr;15(4):870-8.
"""
pvals = np.asarray(pvals)
shape_init = pvals.shape
pvals = pvals.ravel()
pvals_sortind = np.argsort(pvals)
pvals_sorted = pvals[pvals_sortind]
sortrevind = pvals_sortind.argsort()
if method in ['i', 'indep', 'p', 'poscorr']:
ecdffactor = _ecdf(pvals_sorted)
elif method in ['n', 'negcorr']:
cm = np.sum(1. / np.arange(1, len(pvals_sorted) + 1))
ecdffactor = _ecdf(pvals_sorted) / cm
else:
raise ValueError("Method should be 'indep' and 'negcorr'")
reject = pvals_sorted < (ecdffactor * alpha)
if reject.any():
rejectmax = max(np.nonzero(reject)[0])
else:
rejectmax = 0
reject[:rejectmax] = True
pvals_corrected_raw = pvals_sorted / ecdffactor
pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
pvals_corrected[pvals_corrected > 1.0] = 1.0
pvals_corrected = pvals_corrected[sortrevind].reshape(shape_init)
reject = reject[sortrevind].reshape(shape_init)
return reject, pvals_corrected
def bonferroni_correction(pval, alpha=0.05):
"""P-value correction with Bonferroni method.
Parameters
----------
pval : array_like
set of p-values of the individual tests.
alpha : float
error rate
Returns
-------
reject : array, bool
True if a hypothesis is rejected, False if not
pval_corrected : array
pvalues adjusted for multiple hypothesis testing to limit FDR
"""
pval = np.asarray(pval)
pval_corrected = pval * float(pval.size)
reject = pval_corrected < alpha
return reject, pval_corrected