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Module « scipy.stats »

Fonction kruskal - module scipy.stats

Signature de la fonction kruskal

def kruskal(*samples, nan_policy='propagate', axis=0, keepdims=False) 

Description

help(scipy.stats.kruskal)

    


Compute the Kruskal-Wallis H-test for independent samples.

The Kruskal-Wallis H-test tests the null hypothesis that the population
median of all of the groups are equal.  It is a non-parametric version of
ANOVA.  The test works on 2 or more independent samples, which may have
different sizes.  Note that rejecting the null hypothesis does not
indicate which of the groups differs.  Post hoc comparisons between
groups are required to determine which groups are different.

Parameters
----------
sample1, sample2, ... : array_like
    Two or more arrays with the sample measurements can be given as
    arguments. Samples must be one-dimensional.
nan_policy : {'propagate', 'omit', 'raise'}
    Defines how to handle input NaNs.
    
    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
keepdims : bool, default: False
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

Returns
-------
statistic : float
    The Kruskal-Wallis H statistic, corrected for ties.
pvalue : float
    The p-value for the test using the assumption that H has a chi
    square distribution. The p-value returned is the survival function of
    the chi square distribution evaluated at H.

See Also
--------

:func:`f_oneway`
    1-way ANOVA.
:func:`mannwhitneyu`
    Mann-Whitney rank test on two samples.
:func:`friedmanchisquare`
    Friedman test for repeated measurements.


Notes
-----
Due to the assumption that H has a chi square distribution, the number
of samples in each group must not be too small.  A typical rule is
that each sample must have at least 5 measurements.

Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
code) are converted to ``np.ndarray`` before the calculation is performed. In
this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
masked array with ``mask=False``.

References
----------
.. [1] W. H. Kruskal & W. W. Wallis, "Use of Ranks in
   One-Criterion Variance Analysis", Journal of the American Statistical
   Association, Vol. 47, Issue 260, pp. 583-621, 1952.
.. [2] https://en.wikipedia.org/wiki/Kruskal-Wallis_one-way_analysis_of_variance

Examples
--------
>>> from scipy import stats
>>> x = [1, 3, 5, 7, 9]
>>> y = [2, 4, 6, 8, 10]
>>> stats.kruskal(x, y)
KruskalResult(statistic=0.2727272727272734, pvalue=0.6015081344405895)

>>> x = [1, 1, 1]
>>> y = [2, 2, 2]
>>> z = [2, 2]
>>> stats.kruskal(x, y, z)
KruskalResult(statistic=7.0, pvalue=0.0301973834223185)

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