scikit_posthocs.test_osrt
- scikit_posthocs.test_osrt(data: List | ndarray | DataFrame, val_col: str = None, group_col: str = None, sort: bool = False) Tuple[float, float, int]
Hayter’s one-sided studentised range test (OSRT)
Tests a hypothesis against an ordered alternative for normal data with equal variances [1].
- Parameters:
data (Union[List, numpy.ndarray, DataFrame]) – An array, any object exposing the array interface or a pandas DataFrame with data values.
val_col (str = None) – 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 = None) – 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 = False) – If True, sort data by block and group columns.
- Returns:
P value, statistic, and number of degrees of freedom.
- Return type:
Tuple[float, float, int]
Notes
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.test_osrt(x, val_col='values', group_col='groups')