scikit_posthocs.posthoc_nemenyi¶
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scikit_posthocs.posthoc_nemenyi(a: Union[list, numpy.ndarray, pandas.core.frame.DataFrame], val_col: str = None, group_col: str = None, dist: str = 'chi', sort: bool = True) → pandas.core.frame.DataFrame¶ Post hoc pairwise test for multiple comparisons of mean rank sums (Nemenyi’s test). May be used after Kruskal-Wallis one-way analysis of variance by ranks to do pairwise comparisons [1].
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. Second dimension may vary, i.e. groups may have different lengths.
- 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.
- dist (str, optional) – Method for determining the p value. The default distribution is “chi” (chi-squared), else “tukey” (studentized range).
- sort (bool, optional) – Specifies whether to sort DataFrame by group_col or not. Recommended unless you sort your data manually.
Returns: result – P values.
Return type: pandas.DataFrame
Notes
A tie correction will be employed according to Glantz (2012).
References
[1] Lothar Sachs (1997), Angewandte Statistik. Berlin: Springer. Pages: 395-397, 662-664. Examples
>>> x = [[1,2,3,5,1], [12,31,54, np.nan], [10,12,6,74,11]] >>> sp.posthoc_nemenyi(x)