# vtest¶

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

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

Parameters: data : numpy.ndarray or Quantity Array of circular (directional) data, which is assumed to be in radians whenever data is numpy.ndarray. mu : float or Quantity, optional Mean angle. Assumed to be known. axis : int, optional Axis along which the V test will be performed. weights : numpy.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 [R102], remark 1.4, page 22, for detailed explanation. p-value : float or dimensionless Quantity p-value.

References

 [R102] (1, 2) S. R. Jammalamadaka, A. SenGupta. “Topics in Circular Statistics”. Series on Multivariate Analysis, Vol. 5, 2001.
 [R103] C. Agostinelli, U. Lund. “Circular Statistics from ‘Topics in Circular Statistics (2001)’”. 2015.
 [R104] 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>