_posthocs.py 文件源码

def posthoc_tukey_hsd(x, g, alpha = 0.05):

    '''Pairwise comparisons with TukeyHSD confidence intervals. This is a
    convenience function to make statsmodels pairwise_tukeyhsd() method more
    applicable for further use.

        Parameters
        ----------
        x : array_like or pandas Series object, 1d
            An array, any object exposing the array interface, containing
            the response variable. NaN values will cause an error. Please
            handle manually.

        g : array_like or pandas Series object, 1d
            An array, any object exposing the array interface, containing
            the groups' names. Can be string or integers.

        alpha : float, optional
            Significance level for the test. Default is 0.05.

        Returns
        -------
        Numpy ndarray where 0 is False (not significant), 1 is True (significant),
        and -1 is for diagonal elements.

        Examples
        --------

        >>> x = [[1,2,3,4,5], [35,31,75,40,21], [10,6,9,6,1]]
        >>> g = [['a'] * 5, ['b'] * 5, ['c'] * 5]
        >>> sp.posthoc_tukey_hsd(np.concatenate(x), np.concatenate(g))
        array([[-1,  1,  0],
               [ 1, -1,  1],
               [ 0,  1, -1]])

    '''

    result = pairwise_tukeyhsd(x, g, alpha=0.05)
    groups = np.array(result.groupsunique, dtype=np.str)
    groups_len = len(groups)

    vs = np.arange(groups_len, dtype=np.int)[:,None].T.repeat(groups_len, axis=0)

    for a in result.summary()[1:]:
        a0 = str(a[0])
        a1 = str(a[1])
        a0i = np.where(groups == a0)[0][0]
        a1i = np.where(groups == a1)[0][0]
        vs[a0i, a1i] = 1 if str(a[5]) == 'True' else 0

    vs = np.triu(vs)
    np.fill_diagonal(vs, -1)
    tri_lower = np.tril_indices(vs.shape[0], -1)
    vs[tri_lower] = vs.T[tri_lower]


    return vs