Several methods of combining P-values
Project description
MultiTest -- Global Tests for Multiple Hypothesis
MultiTest includes several techniques for multiple hypothesis testing:
MultiTest.hc
Higher CriticismMultiTest.hcstar
Higher Criticism with limited rangeMultiTest.hc_jin
Higher Criticism with limited range proposed by Jiashun JinMultiTest.berk_jones
Berk-Jones test (actually -log(bj))MultiTest.fdr
False-discovery rate with optimized rate parameterMultiTest.minp
Minimal P-values (Bonferroni style inference) (actually -log(minp))MultiTest.fisher
Fisher's method to combine P-values
All tests rejects for large values of the test statistics.
Example:
import numpy as np
from scipy.stats import norm
from multitest import MultiTest
p = 100
z = np.random.randn(p)
pvals = 2*norm.cdf(-np.abs(z)/2)
mtest = MultiTest(pvals)
hc, p_hct = mtest.hc(gamma = 0.3)
bj = mtest.berk_jones()
ii = np.arange(len(pvals))
print(f"HC = {hc}, Indices of P-values below HCT: {ii[pvals <= p_hct]}")
print(f"Berk-Jones = {bj}")
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