vtest(data, mu=0.0, axis=None, weights=None)¶
Performs the Rayleigh test of uniformity where the alternative hypothesis H1 is assumed to have a known mean angle
data : numpy.ndarray or Quantity
Array of circular (directional) data, which is assumed to be in radians whenever
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
weightsrepresents a weighting factor for each group such that
sum(weights, axis)equals the number of observations. See , remark 1.4, page 22, for detailed explanation.
p-value : float or dimensionless Quantity
[R63] S. R. Jammalamadaka, A. SenGupta. “Topics in Circular Statistics”. Series on Multivariate Analysis, Vol. 5, 2001. [R64] C. Agostinelli, U. Lund. “Circular Statistics from ‘Topics in Circular Statistics (2001)’”. 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> [R65] M. Chirstman., C. Miller. “Testing a Sample of Directions for Uniformity.” Lecture Notes, STA 6934/5805. University of Florida, 2007.
>>> 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>