rayleigh: Rayleigh's test of uniformity

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/rayleigh.R

Description

It checkes whether the data are uniformly distributed on the sphere or hypersphere.

Usage

1
rayleigh(x, modif = TRUE, B = 999)

Arguments

x

A matrix containing the data, unit vectors.

modif

If modif is TRUE, the modification as suggested by Jupp (2001) is used.

B

If B is greater than 1, bootstap calibation os performed. If it is equal to 1, classical theory is used.

Details

The Rayleigh test of uniformity is not the best, when there are two antipodal mean directions. In this case it will fail. It is good to test whether there is one mean direction or not. To put it differently, it tests whether the concentration parameter of the Fisher distribution is zero or not.

Value

A vector including:

test

The value of the test statistic.

p-value or Bootstrap p-value

The (bootstrap) p-value of the test.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Giorgos Athineou <[email protected]>

References

Mardia, K. V. and Jupp, P. E. (2000). Directional statistics. Chicester: John Wiley & Sons.

Jupp, P. E. (2001). Modifications of the rayleigh and bingham tests for uniformity of directions. Journal of Multivariate Analysis, 77(2):1-20.

Rayleigh, L. (1919). On the problem of random vibrations, and of random flights in one, two, or three dimensions. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 37(220):321-347.

See Also

vmf, meandir.test, acg

Examples

1
2
x <- rvmf(100, rnorm(5), 1)  ## Fisher distribution with low concentration
rayleigh(x)

Example output

             test Bootstrap p-value 
         21.26817           0.00200 

Directional documentation built on Nov. 12, 2018, 5:05 p.m.