SciPy

scipy.stats.mstats.kendalltau

scipy.stats.mstats.kendalltau(x, y, use_ties=True, use_missing=False, method='auto')[source]

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. ‘auto’ is the default and selects the appropriate method based on a trade-off between speed and accuracy.

Returns:
correlation : float

Kendall tau

pvalue : float

Approximate 2-side p-value.

References

[1](1, 2) Maurice G. Kendall, “Rank Correlation Methods” (4th Edition), Charles Griffin & Co., 1970.

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