Implementation of Dunn's multiple comparison test, following a Kruskal-Wallis 1-way ANOVA

dunn.py

import numpy as np
from scipy import stats
from itertools import combinations
from statsmodels.stats.multitest import multipletests
from statsmodels.stats.libqsturng import psturng
import warnings


def kw_dunn(groups, to_compare=None, alpha=0.05, method='bonf'):
    """

    Kruskal-Wallis 1-way ANOVA with Dunn's multiple comparison test

    Arguments:
    ---------------
    groups: sequence
        arrays corresponding to k mutually independent samples from
        continuous populations

    to_compare: sequence
        tuples specifying the indices of pairs of groups to compare, e.g.
        [(0, 1), (0, 2)] would compare group 0 with 1 & 2. by default, all
        possible pairwise comparisons between groups are performed.

    alpha: float
        family-wise error rate used for correcting for multiple comparisons
        (see statsmodels.stats.multitest.multipletests for details)

    method: string
        method used to adjust p-values to account for multiple corrections (see
        statsmodels.stats.multitest.multipletests for options)

    Returns:
    ---------------
    H: float
        Kruskal-Wallis H-statistic

    p_omnibus: float
        p-value corresponding to the global null hypothesis that the medians of
        the groups are all equal

    Z_pairs: float array
        Z-scores computed for the absolute difference in mean ranks for each
        pairwise comparison

    p_corrected: float array
        corrected p-values for each pairwise comparison, corresponding to the
        null hypothesis that the pair of groups has equal medians. note that
        these are only meaningful if the global null hypothesis is rejected.

    reject: bool array
        True for pairs where the null hypothesis can be rejected for the given
        alpha

    Reference:
    ---------------
    Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric Statistical
    Inference (5th ed., pp. 353-357). Boca Raton, FL: Chapman & Hall.

    """

    # omnibus test (K-W ANOVA)
    # -------------------------------------------------------------------------

    groups = [np.array(gg) for gg in groups]

    k = len(groups)

    n = np.array([len(gg) for gg in groups])
    if np.any(n < 5):
        warnings.warn("Sample sizes < 5 are not recommended (K-W test assumes "
                      "a chi square distribution)")

    allgroups = np.concatenate(groups)
    N = len(allgroups)
    ranked = stats.rankdata(allgroups)

    # correction factor for ties
    T = stats.tiecorrect(ranked)
    if T == 0:
        raise ValueError('All numbers are identical in kruskal')

    # sum of ranks for each group
    j = np.insert(np.cumsum(n), 0, 0)
    R = np.empty(k, dtype=np.float)
    for ii in range(k):
        R[ii] = ranked[j[ii]:j[ii + 1]].sum()

    # the Kruskal-Wallis H-statistic
    H = (12. / (N * (N + 1.))) * ((R ** 2.) / n).sum() - 3 * (N + 1)

    # apply correction factor for ties
    H /= T

    df_omnibus = k - 1
    p_omnibus = stats.chisqprob(H, df_omnibus)

    # multiple comparisons
    # -------------------------------------------------------------------------

    # by default we compare every possible pair of groups
    if to_compare is None:
        to_compare = tuple(combinations(range(k), 2))

    ncomp = len(to_compare)

    Z_pairs = np.empty(ncomp, dtype=np.float)
    p_uncorrected = np.empty(ncomp, dtype=np.float)
    Rmean = R / n

    for pp, (ii, jj) in enumerate(to_compare):

        # standardized score
        Zij = (np.abs(Rmean[ii] - Rmean[jj]) /
               np.sqrt((1. / 12.) * N * (N + 1) * (1. / n[ii] + 1. / n[jj])))
        Z_pairs[pp] = Zij

    # corresponding p-values obtained from upper quantiles of the standard
    # normal distribution
    p_uncorrected = stats.norm.sf(Z_pairs) * 2.

    # correction for multiple comparisons
    reject, p_corrected, alphac_sidak, alphac_bonf = multipletests(
        p_uncorrected, method=method
    )

    return H, p_omnibus, Z_pairs, p_corrected, reject

@jazon33y You are correct. After adding your snippet, the output is now equal to that of R when there are ties. Thanks!

What order do the post host arrays output in? From my own testing I think for three groups it's (1 vs 2), (1 vs 3), (2 vs 3)?

stats.chisqprob is deprecated in scipy 0.17.0; use stats.distributions.chi2.sf
p_omnibus = stats.distributions.chi2.sf(H, df_omnibus)

The format of 'groups' is still unclear; I get a "too many values to unpack" error when I use an array of samples as indicated.

@whokan I think your assumption is correct. I just tested 7 groups and this was the return order:

[(0,1), (0,2), (0,3), (0,4), (0,5), (0,6), (1,2), (1,3), (1,4), (1,5), (1,6), (2,3), (2,4), (2,5), (2,6), (3,4), (3,5), (3,6), (4,5), (4,6), (5,6)]

Hello there @alimuldal I would like to include your code in a GPLv3 project. Your function does not seem to be licensed, so I'd like to know if I have permission to use and modify it. And if so, how would you like me to credit you? Would this be OK?

# The following section is adapted from code written by Alistair Muldal.
# Permission for use and modification has been granted by the author.
# Source: https://gist.github.com/alimuldal/fbb19b73fa25423f02e8
# Retrieved: 2018.10.12 UTC
# START adapted code
your python code....
# END adapted code

@jazon33y thanks for the correction (now if you have k=2 samples the p-value is consistent with K-W test as it should). FYI for Python 2.x users that don't future import division, you will get Inf for Z-scores (and corresponding p-values of 0) because of the integer division for theta2.

Hi all,
did somebody tried the script with 3 groups or more? My p-values are wrong (compared with R, and I checked the same adjustment method was applied).
If it does work with you can you please share the script?
Thanks

If you are getting an error with 'chisqprob' (see so question here) the Bug Fix is Here via @VincentLa14

from scipy import stats
stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)