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Module « scipy.stats.mstats »

Fonction kendalltau - module scipy.stats.mstats

Signature de la fonction kendalltau

def kendalltau(x, y, use_ties=True, use_missing=False, method='auto', alternative='two-sided') 

Description

help(scipy.stats.mstats.kendalltau)

Computes Kendall's rank correlation tau on two variables *x* and *y*.

Parameters
----------
x : sequence
    First data list (for example, time).
y : sequence
    Second data list.
use_ties : {True, False}, optional
    Whether ties correction should be performed.
use_missing : {False, True}, optional
    Whether missing data should be allocated a rank of 0 (False) or the
    average rank (True)
method : {'auto', 'asymptotic', 'exact'}, optional
    Defines which method is used to calculate the p-value [1]_.
    'asymptotic' uses a normal approximation valid for large samples.
    'exact' computes the exact p-value, but can only be used if no ties
    are present. As the sample size increases, the 'exact' computation
    time may grow and the result may lose some precision.
    'auto' is the default and selects the appropriate
    method based on a trade-off between speed and accuracy.
alternative : {'two-sided', 'less', 'greater'}, optional
    Defines the alternative hypothesis. Default is 'two-sided'.
    The following options are available:

    * 'two-sided': the rank correlation is nonzero
    * 'less': the rank correlation is negative (less than zero)
    * 'greater':  the rank correlation is positive (greater than zero)

Returns
-------
res : SignificanceResult
    An object containing attributes:

    statistic : float
       The tau statistic.
    pvalue : float
       The p-value for a hypothesis test whose null hypothesis is
       an absence of association, tau = 0.

References
----------
.. [1] Maurice G. Kendall, "Rank Correlation Methods" (4th Edition),
       Charles Griffin & Co., 1970.

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