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
- xsequence
First data list (for example, time).
- ysequence
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.
- Returns
- correlationfloat
Kendall tau
- pvaluefloat
Approximate 2-side p-value.
References
- 1
Maurice G. Kendall, “Rank Correlation Methods” (4th Edition), Charles Griffin & Co., 1970.