scipy.stats.mstats.kendalltau¶
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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. ‘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.