U Qv f @s<ddlZddlZddlmZddlmZdgZdddZ dS) N)ttest)_postprocess_dataframetostFc Cst|}t|}t|ttfs*tdt|||||dd}t|||||dd}t|jd|jd}t j ||jd|ddgd }t |S) a;Two One-Sided Test (TOST) for equivalence. Parameters ---------- x, y : array_like First and second set of observations. ``x`` and ``y`` should have the same units. If ``y`` is a single value (e.g. 0), a one-sample test is performed. bound : float Magnitude of region of similarity (a.k.a epsilon). Note that this should be expressed in the same unit as ``x`` and ``y``. paired : boolean Specify whether the two observations are related (i.e. repeated measures) or independent. correction : auto or boolean Specify whether or not to correct for unequal variances using Welch separate variances T-test. This only applies if ``paired`` is False. Returns ------- stats : :py:class:`pandas.DataFrame` * ``'bound'``: bound (= epsilon, or equivalence margin) * ``'dof'``: degrees of freedom * ``'pval'``: TOST p-value See also -------- ttest References ---------- .. [1] Schuirmann, D.L. 1981. On hypothesis testing to determine if the mean of a normal distribution is contained in a known interval. Biometrics 37 617. .. [2] https://cran.r-project.org/web/packages/equivalence/equivalence.pdf Examples -------- 1. Independent two-sample TOST with a region of similarity of 1 (default) >>> import pingouin as pg >>> a = [4, 7, 8, 6, 3, 2] >>> b = [6, 8, 7, 10, 11, 9] >>> pg.tost(a, b) bound dof pval TOST 1 10 0.965097 2. Paired TOST with a different region of similarity >>> pg.tost(a, b, bound=0.5, paired=True) bound dof pval TOST 0.5 5 0.954854 3. One sample TOST >>> pg.tost(a, y=0, bound=4) bound dof pval TOST 4 5 0.825967 zbound must be int or float.Zgreater)paired correction alternativeZless)T-testzp-val)r dof)boundr pvalZTOST)index) npZasarray isinstanceintfloatAssertionErrorrmaxatpdZ DataFramer) xyr rrZdf_aZdf_br statsr8/tmp/pip-unpacked-wheel-2te3nxqf/pingouin/equivalence.pyr s>  )rFF) ZnumpyrZpandasrZpingouin.parametricrZpingouin.utilsr__all__rrrrrs