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:

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.

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:

References

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