scikit_posthocs.posthoc_dscf
- scikit_posthocs.posthoc_dscf(a: list | ndarray | DataFrame, val_col: str = None, group_col: str = None, sort: bool = False) DataFrame
Dwass, Steel, Critchlow and Fligner all-pairs comparison test for a one-factorial layout with non-normally distributed residuals. As opposed to the all-pairs comparison procedures that depend on Kruskal ranks, the DSCF test is basically an extension of the U-test as re-ranking is conducted for each pairwise test [1], [2], [3].
- Parameters:
a (Union[list, np.ndarray, DataFrame]) – An array, any object exposing the array interface or a pandas DataFrame.
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.
sort (bool, optional) – If True, sort data by block and group columns.
- Returns:
result – P values.
- Return type:
pandas.DataFrame
Notes
The p values are computed from the Tukey-distribution.
References
Examples
>>> import scikit_posthocs as sp >>> import pandas as pd >>> x = pd.DataFrame({"a": [1,2,3,5,1], "b": [12,31,54,62,12], "c": [10,12,6,74,11]}) >>> x = x.melt(var_name='groups', value_name='values') >>> sp.posthoc_dscf(x, val_col='values', group_col='groups')