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Module « scipy.stats »

Fonction friedmanchisquare - module scipy.stats

Signature de la fonction friedmanchisquare

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

Description

help(scipy.stats.friedmanchisquare)

    


Compute the Friedman test for repeated samples.

The Friedman test tests the null hypothesis that repeated samples of
the same individuals have the same distribution.  It is often used
to test for consistency among samples obtained in different ways.
For example, if two sampling techniques are used on the same set of
individuals, the Friedman test can be used to determine if the two
sampling techniques are consistent.

Parameters
----------
sample1, sample2, sample3... : array_like
    Arrays of observations.  All of the arrays must have the same number
    of elements.  At least three samples must be given.
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.
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.
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 test statistic, correcting for ties.
pvalue : float
    The associated p-value assuming that the test statistic has a chi
    squared distribution.

See Also
--------

:ref:`hypothesis_friedmanchisquare`
    Extended example


Notes
-----
Due to the assumption that the test statistic has a chi squared
distribution, the p-value is only reliable for n > 10 and more than
6 repeated samples.

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] https://en.wikipedia.org/wiki/Friedman_test
.. [2] Demsar, J. (2006). Statistical comparisons of classifiers over
       multiple data sets. Journal of Machine Learning Research, 7, 1-30.

Examples
--------
>>> import numpy as np
>>> rng = np.random.default_rng(seed=18)
>>> x = rng.random((6, 10))
>>> from scipy.stats import friedmanchisquare
>>> res = friedmanchisquare(x[0], x[1], x[2], x[3], x[4], x[5])
>>> res.statistic, res.pvalue
(11.428571428571416, 0.043514520866727614)

The p-value is less than 0.05; however, as noted above, the results may not
be reliable since we have a small number of repeated samples.

For a more detailed example, see :ref:`hypothesis_friedmanchisquare`.

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