kendalltau#
- scipy.stats.kendalltau(x, y, *, nan_policy='propagate', method='auto', variant='b', alternative='two-sided')[source]#
Calculate Kendall’s tau, a correlation measure for ordinal data.
Kendall’s tau is a measure of the correspondence between two rankings. Values close to 1 indicate strong agreement, and values close to -1 indicate strong disagreement. This implements two variants of Kendall’s tau: tau-b (the default) and tau-c (also known as Stuart’s tau-c). These differ only in how they are normalized to lie within the range -1 to 1; the hypothesis tests (their p-values) are identical. Kendall’s original tau-a is not implemented separately because both tau-b and tau-c reduce to tau-a in the absence of ties.
- Parameters:
- x, yarray_like
Arrays of rankings, of the same shape. If arrays are not 1-D, they will be flattened to 1-D.
- nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional
Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):
‘propagate’: returns nan
‘raise’: throws an error
‘omit’: performs the calculations ignoring nan values
- method{‘auto’, ‘asymptotic’, ‘exact’}, optional
Defines which method is used to calculate the p-value [5]. The following options are available (default is ‘auto’):
‘auto’: selects the appropriate method based on a trade-off between speed and accuracy
‘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.
- variant{‘b’, ‘c’}, optional
Defines which variant of Kendall’s tau is returned. Default is ‘b’.
- 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:
- resSignificanceResult
An object containing attributes:
- statisticfloat
The tau statistic.
- pvaluefloat
The p-value for a hypothesis test whose null hypothesis is an absence of association, tau = 0.
See also
spearmanr
Calculates a Spearman rank-order correlation coefficient.
theilslopes
Computes the Theil-Sen estimator for a set of points (x, y).
weightedtau
Computes a weighted version of Kendall’s tau.
- Kendall’s tau test
Extended example
Notes
The definition of Kendall’s tau that is used is [2]:
tau_b = (P - Q) / sqrt((P + Q + T) * (P + Q + U)) tau_c = 2 (P - Q) / (n**2 * (m - 1) / m)
where P is the number of concordant pairs, Q the number of discordant pairs, T the number of ties only in x, and U the number of ties only in y. If a tie occurs for the same pair in both x and y, it is not added to either T or U. n is the total number of samples, and m is the number of unique values in either x or y, whichever is smaller.
References
[1]Maurice G. Kendall, “A New Measure of Rank Correlation”, Biometrika Vol. 30, No. 1/2, pp. 81-93, 1938.
[2]Maurice G. Kendall, “The treatment of ties in ranking problems”, Biometrika Vol. 33, No. 3, pp. 239-251. 1945.
[3]Gottfried E. Noether, “Elements of Nonparametric Statistics”, John Wiley & Sons, 1967.
[4]Peter M. Fenwick, “A new data structure for cumulative frequency tables”, Software: Practice and Experience, Vol. 24, No. 3, pp. 327-336, 1994.
[5]Maurice G. Kendall, “Rank Correlation Methods” (4th Edition), Charles Griffin & Co., 1970.
Examples
>>> from scipy import stats >>> x1 = [12, 2, 1, 12, 2] >>> x2 = [1, 4, 7, 1, 0] >>> res = stats.kendalltau(x1, x2) >>> res.statistic -0.47140452079103173 >>> res.pvalue 0.2827454599327748
For a more detailed example, see Kendall’s tau test.