scikit_posthocs.posthoc_wilcoxon

scikit_posthocs.posthoc_wilcoxon(a: list | ndarray | DataFrame, val_col: str | None = None, group_col: str | None = None, method: str = 'auto', zero_method: str = 'wilcox', correction: bool = False, p_adjust: str | None = None, sort: bool = False) → DataFrame

Pairwise comparisons with Wilcoxon signed-rank test.

It is a non-parametric version of the paired T-test for use with non-parametric ANOVA.

Parameters:

  • a (array_like or pandas DataFrame object) – An array, any object exposing the array interface or a pandas DataFrame. Array must be two-dimensional.

  • val_col (str, optional) – Name of a DataFrame column that contains dependent variable values (test or response variable). Values should have a non-nominal scale. Must be specified if a is a pandas DataFrame object.

  • group_col (str, optional) – Name of a DataFrame column that contains independent variable values (grouping or predictor variable). Values should have a nominal scale (categorical). Must be specified if a is a pandas DataFrame object.

  • method ({"auto", "exact", "approx"}, optional) – Method to calculate the p-value. Default is “auto”.

  • zero_method ({"pratt", "wilcox", "zsplit"}, optional) – “pratt”: Pratt treatment, includes zero-differences in the ranking process (more conservative) “wilcox”: Wilcox treatment, discards all zero-differences “zsplit”: Zero rank split, just like Pratt, but spliting the zero rank between positive and negative ones

  • correction (bool, optional) – If True, apply continuity correction by adjusting the Wilcoxon rank statistic by 0.5 towards the mean value when computing the z-statistic. Default is False.

  • p_adjust (str, optional) – Method for adjusting p values. See statsmodels.sandbox.stats.multicomp for details. 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)

  • sort (bool, optional) – Sort groups while preserving the input order of observations within each group. Default is False.

Returns:

result – P values.

Return type:

pandas.DataFrame

Notes

Observations are paired by their input order within each group. Callers must align the pairs before passing the data. Sorting only changes group order. Refer to scipy.stats.wilcoxon reference page for further details [1].

References

Examples

>>> import scikit_posthocs as sp
>>> x = [[1,2,3,4,5], [35,31,75,40,21], [10,6,9,6,1]]
>>> sp.posthoc_wilcoxon(x)