esmtools.testing.multipletests

esmtools.testing.multipletests(p, alpha=0.05, method=None, **multipletests_kwargs)[source]

Apply statsmodels.stats.multitest.multipletests for multi-dimensional xr.objects.

Parameters:
  • p (xr.object) – uncorrected p-values.
  • alpha (optional float) – FWER, family-wise error rate. Defaults to 0.05.
  • method (str) – 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)
  • **multipletests_kwargs (optional dict) – is_sorted, returnsorted see statsmodels.stats.multitest.multitest
Returns:

true for hypothesis that can be rejected for given

alpha

pvals_corrected (xr.object): p-values corrected for multiple tests
Return type:

reject (xr.object)

Example

>>> from esmtools.testing import multipletests
>>> reject, xpvals_corrected = multipletests(p, method='fdr_bh')