stats.multitest.fdrcorrection_twostage()
statsmodels.stats.multitest.fdrcorrection_twostage
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statsmodels.stats.multitest.fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False, is_sorted=False)
[source] -
(iterated) two stage linear step-up procedure with estimation of number of true hypotheses
Benjamini, Krieger and Yekuteli, procedure in Definition 6
Parameters: pvals : array_like
set of p-values of the individual tests.
alpha : float
error rate
method : {‘bky’, ‘bh’)
see Notes for details
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- ‘bky’ - implements the procedure in Definition 6 of Benjamini, Krieger
- and Yekuteli 2006
- ‘bh’ - the two stage method of Benjamini and Hochberg
iter : bool
Returns: rejected : array, bool
True if a hypothesis is rejected, False if not
pvalue-corrected : array
pvalues adjusted for multiple hypotheses testing to limit FDR
m0 : int
ntest - rej, estimated number of true hypotheses
alpha_stages : list of floats
A list of alphas that have been used at each stage
Notes
The returned corrected p-values are specific to the given alpha, they cannot be used for a different alpha.
The returned corrected p-values are from the last stage of the fdr_bh linear step-up procedure (fdrcorrection0 with method=’indep’) corrected for the estimated fraction of true hypotheses. This means that the rejection decision can be obtained with
pval_corrected <= alpha
, wherealpha
is the origianal significance level. (Note: This has changed from earlier versions (<0.5.0) of statsmodels.)BKY described several other multi-stage methods, which would be easy to implement. However, in their simulation the simple two-stage method (with iter=False) was the most robust to the presence of positive correlation
TODO: What should be returned?
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© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.fdrcorrection_twostage.html