scikit_posthocs.posthoc_anderson
- scikit_posthocs.posthoc_anderson(a: list | ndarray | DataFrame, val_col: str = None, group_col: str = None, midrank: bool = True, p_adjust: str = None, sort: bool = False) DataFrame
Anderson-Darling Pairwise Test for k-samples. Tests the null hypothesis that k-samples are drawn from the same population without having to specify the distribution function of that population [1].
- Parameters:
a (array_like or pandas DataFrame object) – 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.
midrank (bool, optional) – Type of Anderson-Darling test which is computed. If set to True (default), the midrank test applicable to continuous and discrete populations is performed. If False, the right side empirical distribution is used.
sort (bool, optional) – If True, sort data by block and group columns.
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)
- Returns:
result – P values.
- Return type:
pandas.DataFrame
References
Examples
>>> x = np.array([[2.9, 3.0, 2.5, 2.6, 3.2], [3.8, 2.7, 4.0, 2.4], [2.8, 3.4, 3.7, 2.2, 2.0]]) >>> sp.posthoc_anderson(x)