scikit_posthocs.posthoc_lsd

scikit_posthocs.posthoc_lsd(a: list | ndarray | DataFrame, val_col: str | None = None, group_col: str | None = None, p_adjust: str | None = None, sort: bool = False) → DataFrame

Fisher’s Least Significant Difference (LSD) all-pairs comparison test for normally distributed data with equal group variances, following a parametric ANOVA [1].

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.

  • p_adjust (str, optional) – Method for adjusting p values. See statsmodels.sandbox.stats.multicomp for details. Left as None (the default), this reproduces Fisher’s protected LSD test, which is only valid when applied after a significant omnibus ANOVA F-test.

  • sort (bool, optional) – If True, sort data by group columns.

Returns:

result – P values.

Return type:

pandas.DataFrame

Notes

Test statistics use the pooled within-group variance, equivalent to posthoc_ttest with pool_sd=True and no p value adjustment.

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_lsd(x, val_col='values', group_col='groups')