statsmodels.stats.multitest.fdrcorrection_twostage¶

statsmodels.stats.multitest.
fdrcorrection_twostage
(pvals, alpha=0.05, method='bky', iter=False, is_sorted=False)[source]¶ (iterated) two stage linear stepup procedure with estimation of number of true hypotheses
Benjamini, Krieger and Yekuteli, procedure in Definition 6
Parameters: pvals : array_like
set of pvalues of the individual tests.
alpha : float
error rate
method : {‘bky’, ‘bh’)
see Notes for details
 ‘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
pvaluecorrected : 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 pvalues are specific to the given alpha, they cannot be used for a different alpha.
The returned corrected pvalues are from the last stage of the fdr_bh linear stepup 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 multistage methods, which would be easy to implement. However, in their simulation the simple twostage method (with iter=False) was the most robust to the presence of positive correlation
TODO: What should be returned?