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
- datanumpy.ndarray or Quantity
Array of circular (directional) data, which is assumed to be in radians whenever
- 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
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-valuefloat or dimensionless Quantity
S. R. Jammalamadaka, A. SenGupta. “Topics in Circular Statistics”. Series on Multivariate Analysis, Vol. 5, 2001.
C. Agostinelli, U. Lund. “Circular Statistics from ‘Topics in Circular Statistics (2001)’”. 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
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>