scikit_posthocs.posthoc_ttest

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

Pairwise T test for multiple comparisons of independent groups. May be used after a parametric ANOVA to do pairwise comparisons.

Parameters:

  • a (array_like or pandas DataFrame object) – An array, any object exposing the array interface or a pandas DataFrame. Array must be two-dimensional. Observations must be finite; remove or impute missing values first.

  • 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.

  • equal_var (bool, optional) – If True (default), perform a standard independent test that assumes equal population variances [1]. If False, perform Welch’s t-test, which does not assume equal population variance [2].

  • pool_sd (bool, optional) – Calculate a common SD for all groups and use that for all comparisons (this can be useful if some groups are small). This method does not actually call scipy ttest_ind() function, so extra arguments are ignored. Default is False.

  • 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)

  • sort (bool, optional) – Specifies whether to sort DataFrame by group_col or not. Recommended unless you sort your data manually.

Returns:

result – P values.

Return type:

pandas.DataFrame

References

Examples

>>> import scikit_posthocs as sp
>>> x = [[1, 2, 3, 5, 1], [12, 31, 54, 62, 12], [10, 12, 6, 74, 11]]
>>> p_values = sp.posthoc_ttest(x, p_adjust='holm')
>>> round(float(p_values.loc[1, 2]), 6)
0.047356