scipy.stats.mood#
- scipy.stats.mood(x, y, axis=0, alternative='two-sided', *, nan_policy='propagate', keepdims=False)[source]#
Perform Mood’s test for equal scale parameters.
Mood’s two-sample test for scale parameters is a non-parametric test for the null hypothesis that two samples are drawn from the same distribution with the same scale parameter.
- Parameters:
- x, yarray_like
Arrays of sample data.
- axisint or None, default: 0
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.- alternative{‘two-sided’, ‘less’, ‘greater’}, optional
Defines the alternative hypothesis. Default is ‘two-sided’. The following options are available:
‘two-sided’: the scales of the distributions underlying x and y are different.
‘less’: the scale of the distribution underlying x is less than the scale of the distribution underlying y.
‘greater’: the scale of the distribution underlying x is greater than the scale of the distribution underlying y.
New in version 1.7.0.
- 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, aValueError
will be raised.
- 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:
- statisticscalar or ndarray
The z-score for the hypothesis test. For 1-D inputs a scalar is returned.
- pvaluescalar ndarray
The p-value for the hypothesis test.
See also
Notes
The data are assumed to be drawn from probability distributions
f(x)
andf(x/s) / s
respectively, for some probability density function f. The null hypothesis is thats == 1
.For multi-dimensional arrays, if the inputs are of shapes
(n0, n1, n2, n3)
and(n0, m1, n2, n3)
, then ifaxis=1
, the resulting z and p values will have shape(n0, n2, n3)
. Note thatn1
andm1
don’t have to be equal, but the other dimensions do.Beginning in SciPy 1.9,
np.matrix
inputs (not recommended for new code) are converted tonp.ndarray
before the calculation is performed. In this case, the output will be a scalar ornp.ndarray
of appropriate shape rather than a 2Dnp.matrix
. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar ornp.ndarray
rather than a masked array withmask=False
.References
- [1] Mielke, Paul W. “Note on Some Squared Rank Tests with Existing Ties.”
Technometrics, vol. 9, no. 2, 1967, pp. 312-14. JSTOR, https://doi.org/10.2307/1266427. Accessed 18 May 2022.
Examples
>>> import numpy as np >>> from scipy import stats >>> rng = np.random.default_rng() >>> x2 = rng.standard_normal((2, 45, 6, 7)) >>> x1 = rng.standard_normal((2, 30, 6, 7)) >>> res = stats.mood(x1, x2, axis=1) >>> res.pvalue.shape (2, 6, 7)
Find the number of points where the difference in scale is not significant:
>>> (res.pvalue > 0.1).sum() 78
Perform the test with different scales:
>>> x1 = rng.standard_normal((2, 30)) >>> x2 = rng.standard_normal((2, 35)) * 10.0 >>> stats.mood(x1, x2, axis=1) SignificanceResult(statistic=array([-5.76174136, -6.12650783]), pvalue=array([8.32505043e-09, 8.98287869e-10]))