kendalltau — SciPy v1.16.0 Manual (original) (raw)

scipy.stats.

scipy.stats.kendalltau(x, y, *, nan_policy='propagate', method='auto', variant='b', alternative='two-sided', axis=None, keepdims=False)[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’, ‘omit’, ‘raise’}

Defines how to handle input NaNs.

• propagate: if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN.
• omit: NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN.
• raise: if a NaN is present, a ValueError will be raised.

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)

axisint or None, default: None

If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If None, the input will be raveled before computing the statistic.

keepdimsbool, default: False

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

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.

Raises:

ValueError

If nan_policy is ‘omit’ and variant is not ‘b’ or if method is ‘exact’ and there are ties between x and y.

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 tied pairs only in x, and U the number of tied pairs 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.

Beginning in SciPy 1.9, np.matrix inputs (not recommended for new code) are converted to np.ndarray before the calculation is performed. In this case, the output will be a scalar or np.ndarray of appropriate shape rather than a 2D np.matrix. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or np.ndarray rather than a masked array with mask=False.

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.