statsmodels.sandbox.stats.multicomp.multipletests¶

statsmodels.sandbox.stats.multicomp.
multipletests
(pvals, alpha=0.05, method='hs', is_sorted=False, returnsorted=False)[source]¶ test results and pvalue correction for multiple tests
Parameters: pvals : array_like
uncorrected pvalues
alpha : float
FWER, familywise 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` : onestep correction `sidak` : onestep correction `holmsidak` : step down method using Sidak adjustments `holm` : stepdown method using Bonferroni adjustments `simeshochberg` : stepup method (independent) `hommel` : closed method based on Simes tests (nonnegative) `fdr_bh` : Benjamini/Hochberg (nonnegative) `fdr_by` : Benjamini/Yekutieli (negative) `fdr_tsbh` : two stage fdr correction (nonnegative) `fdr_tsbky` : two stage fdr correction (nonnegative)
is_sorted : bool
If False (default), the p_values will be sorted, but the corrected pvalues are in the original order. If True, then it assumed that the pvalues are already sorted in ascending order.
returnsorted : bool
not tested, return sorted pvalues instead of original sequence
Returns: reject : array, boolean
true for hypothesis that can be rejected for given alpha
pvals_corrected : array
pvalues corrected for multiple tests
alphacSidak: float
corrected alpha for Sidak method
alphacBonf: float
corrected alpha for Bonferroni method
Notes
There may be API changes for this function in the future.
Except for ‘fdr_twostage’, the pvalue correction is independent of the alpha specified as argument. In these cases the corrected pvalues can also be compared with a different alpha. In the case of ‘fdr_twostage’, the corrected pvalues are specific to the given alpha, see
fdrcorrection_twostage
.The ‘fdr_gbs’ procedure is not verified against another package, pvalues are derived from scratch and are not derived in the reference. In Monte Carlo experiments the method worked correctly and maintained the false discovery rate.
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
Timing:
Most of the time with large arrays is spent in argsort. When we want to calculate the pvalue for several methods, then it is more efficient to presort the pvalues, and put the results back into the original order outside of the function.
Method=’hommel’ is very slow for large arrays, since it requires the evaluation of n partitions, where n is the number of pvalues.