Directional (version 4.0)

Hypothesis test for IAG distribution over the ESAG distribution: Hypothesis test for IAG distribution over the ESAG distribution

Description

The null hypothesis is whether an IAG distribution fits the data well, where the altenrative is that ESAG distribution is more suitable.

Usage

iagesag(x, B = 1, tol = 1e-07)

Arguments

x

A numeric matrix with three columns containing the data as unit vectors in Euclidean coordinates.

B

The number of bootstrap re-samples. By default is set to 999. If it is equal to 1, no bootstrap is performed and the p-value is obtained throught the asymptotic distribution.

tol

The tolerance to accept that the Newton-Raphson algorithm used in the IAG distribution has converged.

Value

A vector including:

test

The value of the test statistic.

p-value or Bootstrap p-value

The p-value of the test.

Details

Essentially it is a test of rotational symmetry, whether the two \(\gamma\) parameters are equal to zero. This works for spherical data only.

References

Paine P.J., Preston S.P., Tsagris M. and Wood A.T.A. (2018). An Elliptically Symmetric Angular Gaussian Distribution. Statistics and Computing, 28(3):689--697.

See Also

fishkent, ESAGmle, kent.mle, iag.mle

Examples

# NOT RUN {
x <- rvmf(100, rnorm(3), 15)
iagesag(x)
fishkent(x, B = 1)
# }