vtest#
- astropy.stats.vtest(data: NDArray | Quantity, mu: float | Quantity | None = 0.0, axis: int | None = None, weights: NDArray | None = None) float | Quantity [source]#
Performs the Rayleigh test of uniformity where the alternative hypothesis H1 is assumed to have a known mean angle
mu
.- Parameters:
- data
ndarray
orQuantity
Array of circular (directional) data, which is assumed to be in radians whenever
data
isnumpy.ndarray
.- mu
float
orQuantity
[:ref: ‘angle’], 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 thatsum(weights, axis)
equals the number of observations. See [1], remark 1.4, page 22, for detailed explanation.
- data
- Returns:
- p-value
float
orQuantity
[:ref: ‘dimensionless’]
- 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>