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statsmodels.sandbox.stats.multicomp.multipletests

statsmodels.sandbox.stats.multicomp.multipletests(pvals, alpha=0.05, method='hs', returnsorted=False)

test results and p-value correction for multiple tests

Parameters:

pvals : array_like

uncorrected p-values

alpha : float

FWER, family-wise error rate, e.g. 0.1

method : string

Method used for testing and adjustment of pvalues. Can be either the full name or initial letters. Available methods are

`bonferroni` : one-step correction
`sidak` : one-step correction
`holm-sidak` : step down method using Sidak adjustments
`holm` : step-down method using Bonferroni adjustments
`simes-hochberg` : step-up method  (independent)
`hommel` : closed method based on Simes tests (non-negative)
`fdr_bh` : Benjamini/Hochberg  (non-negative)
`fdr_by` : Benjamini/Yekutieli (negative)
`fdr_tsbh` : two stage fdr correction (non-negative)
`fdr_tsbky` : two stage fdr correction (non-negative)

returnsorted : bool

not tested, return sorted p-values instead of original sequence

Returns:

reject : array, boolean

true for hypothesis that can be rejected for given alpha

pvals_corrected : array

p-values corrected for multiple tests

alphacSidak: float :

corrected alpha for Sidak method

alphacBonf: float :

corrected alpha for Bonferroni method

Notes

Except for ‘fdr_twostage’, the p-value correction is independent of the alpha specified as argument. In these cases the corrected p-values can also be compared with a different alpha. In the case of ‘fdr_twostage’, the corrected p-values are specific to the given alpha, see fdrcorrection_twostage.

all corrected pvalues now tested against R. insufficient “cosmetic” tests yet new procedure ‘fdr_gbs’ not verified yet, p-values derived from scratch not reference

All procedures that are included, control FWER or FDR in the independent case, and most are robust in the positively correlated case.

fdr_gbs: high power, fdr control for independent case and only small violation in positively correlated case

there will be API changes.

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