# vtest¶

astropy.stats.vtest(data, mu=0.0, axis=None, weights=None)[source]

Performs the Rayleigh test of uniformity where the alternative hypothesis H1 is assumed to have a known mean angle mu.

Parameters
datanumpy.ndarray or Quantity

Array of circular (directional) data, which is assumed to be in radians whenever data is numpy.ndarray.

mufloat or Quantity, optional

Mean angle. Assumed to be known.

axisint, optional

Axis along which the V test will be performed.

weightsnumpy.ndarray, optional

In case of grouped data, the i-th element of weights represents a weighting factor for each group such that sum(weights, axis) equals the number of observations. See [1], remark 1.4, page 22, for detailed explanation.

Returns
p-valuefloat or dimensionless Quantity

p-value.

References

1

S. R. Jammalamadaka, A. SenGupta. “Topics in Circular Statistics”. Series on Multivariate Analysis, Vol. 5, 2001.

2

C. Agostinelli, U. Lund. “Circular Statistics from ‘Topics in Circular Statistics (2001)’”. 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>

3

M. Chirstman., C. Miller. “Testing a Sample of Directions for Uniformity.” Lecture Notes, STA 6934/5805. University of Florida, 2007.

Examples

>>> import numpy as np
>>> from astropy.stats import vtest
>>> from astropy import units as u
>>> data = np.array([130, 90, 0, 145])*u.deg
>>> vtest(data)
<Quantity 0.6223678199713766>