scikit_posthocs.posthoc_scheffe¶
- scikit_posthocs.posthoc_scheffe(a: Union[list, ndarray, DataFrame], val_col: Optional[str] = None, group_col: Optional[str] = None, sort: bool = False) DataFrame ¶
Scheffe’s all-pairs comparisons test for normally distributed data with equal group variances. For all-pairs comparisons in an one-factorial layout with normally distributed residuals and equal variances Scheffe’s test can be performed with parametric ANOVA 1, 2, 3.
A total of m = k(k-1)/2 hypotheses can be tested.
- 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 F-distribution.
References
- 1
J. Bortz (1993) Statistik für Sozialwissenschaftler. 4. Aufl., Berlin: Springer.
- 2
Sachs (1997) Angewandte Statistik, New York: Springer.
- 3
H. Scheffe (1953) A Method for Judging all Contrasts in the Analysis of Variance. Biometrika 40, 87-110.
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_scheffe(x, val_col='values', group_col='groups')