bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / stats / _hypotests / cramervonmises

function

`scipy.stats._hypotests:cramervonmises`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_hypotests.py:560

Signature

def cramervonmises(rvs, cdf, args = (), *, axis = 0, nan_policy = propagate, keepdims = False)

Summary

Perform the one-sample Cramér-von Mises test for goodness of fit.

Extended Summary

This performs a test of the goodness of fit of a cumulative distribution function (cdf) $F$ compared to the empirical distribution function $F_{n}$ of observed random variates $X_{1}, ..., X_{n}$ that are assumed to be independent and identically distributed (^[1]). The null hypothesis is that the $X_{i}$ have cumulative distribution $F$ .

The test statistic $T$ is defined as in ^[1], where $ω^{2}$ is the Cramér-von Mises criterion and $x_{i}$ are the observed values.

T = n ω^{2} = \frac{1}{12 n} + i = 1 \sum n [\frac{2 i - 1}{2 n} - F (x_{i})]^{2}

Parameters

rvs : array_like

A 1-D array of observed values of the random variables $X_{i}$ . The sample must contain at least two observations.

cdf : str or callable

The cumulative distribution function $F$ to test the observations against. If a string, it should be the name of a distribution in scipy.stats. If a callable, that callable is used to calculate the cdf: cdf(x, *args) -> float.

args : tuple, optional

Distribution parameters. These are assumed to be known; see Notes.

axis : int 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.

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.

nan_policy='raise' and nan_policy='omit' are not supported by lazy backends, and nan_policy='omit' is not compatible with MArray input. For multidimensional input, nan_policy='omit' is supported only by the NumPy backend. Instead, mask NaN values using MArray; and use the default nan_policy. See Notes for support information.

keepdims : bool, 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

res : object with attributes

statistic: Cramér-von Mises statistic $T$ .
pvalue: The p-value.

Notes

The p-value relies on the approximation given by equation 1.8 in ^[2]. It is important to keep in mind that the p-value is only accurate if one tests a simple hypothesis, i.e. the parameters of the reference distribution are known. If the parameters are estimated from the data (composite hypothesis), the computed p-value is not reliable.

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.

Examples

Suppose we wish to test whether data generated by ``scipy.stats.norm.rvs`` were, in fact, drawn from the standard normal distribution. We choose a significance level of ``alpha=0.05``.

import numpy as np
from scipy import stats
rng = np.random.default_rng(165417232101553420507139617764912913465)
x = stats.norm.rvs(size=500, random_state=rng)
res = stats.cramervonmises(x, 'norm')

✓

res.statistic, res.pvalue

✗

(np.float64(0.1072085112565724), np.float64(0.5508482238203417))

The p-value exceeds our chosen significance level, so we do not reject the null hypothesis that the observed sample is drawn from the standard normal distribution. Now suppose we wish to check whether the same samples shifted by 2.1 is consistent with being drawn from a normal distribution with a mean of 2.

y = x + 2.1
res = stats.cramervonmises(y, 'norm', args=(2,))

✓

res.statistic, res.pvalue

✗

(np.float64(0.8364446265294695), np.float64(0.005962867970083496))

Here we have used the `args` keyword to specify the mean (``loc``) of the normal distribution to test the data against. This is equivalent to the following, in which we create a frozen normal distribution with mean 2.1, then pass its ``cdf`` method as an argument.

frozen_dist = stats.norm(loc=2)
res = stats.cramervonmises(y, frozen_dist.cdf)

✓

res.statistic, res.pvalue

✗

(np.float64(0.8364446265294695), np.float64(0.005962867970083496))

In either case, we would reject the null hypothesis that the observed sample is drawn from a normal distribution with a mean of 2 (and default variance of 1) because the p-value is less than our chosen significance level.

Aliases

scipy.stats.cramervonmises