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Module « scipy.stats.mstats »

Fonction brunnermunzel - module scipy.stats.mstats

Signature de la fonction brunnermunzel

def brunnermunzel(x, y, alternative='two-sided', distribution='t') 

Description

help(scipy.stats.mstats.brunnermunzel)

Compute the Brunner-Munzel test on samples x and y.

Any missing values in `x` and/or `y` are discarded.

The Brunner-Munzel test is a nonparametric test of the null hypothesis that
when values are taken one by one from each group, the probabilities of
getting large values in both groups are equal.
Unlike the Wilcoxon-Mann-Whitney's U test, this does not require the
assumption of equivariance of two groups. Note that this does not assume
the distributions are same. This test works on two independent samples,
which may have different sizes.

Parameters
----------
x, y : array_like
    Array of samples, should be one-dimensional.
alternative : 'less', 'two-sided', or 'greater', optional
    Whether to get the p-value for the one-sided hypothesis ('less'
    or 'greater') or for the two-sided hypothesis ('two-sided').
    Defaults value is 'two-sided' .
distribution : 't' or 'normal', optional
    Whether to get the p-value by t-distribution or by standard normal
    distribution.
    Defaults value is 't' .

Returns
-------
statistic : float
    The Brunner-Munzer W statistic.
pvalue : float
    p-value assuming an t distribution. One-sided or
    two-sided, depending on the choice of `alternative` and `distribution`.

See Also
--------
mannwhitneyu : Mann-Whitney rank test on two samples.

Notes
-----
For more details on `brunnermunzel`, see `scipy.stats.brunnermunzel`.

Examples
--------
>>> from scipy.stats.mstats import brunnermunzel
>>> import numpy as np
>>> x1 = [1, 2, np.nan, np.nan, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1]
>>> x2 = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
>>> brunnermunzel(x1, x2)
BrunnerMunzelResult(statistic=1.4723186918922935, pvalue=0.15479415300426624)  # may vary

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