pc.test: Hypothesis test for SIPC distribution over the SESPC...

In Directional: A Collection of Functions for Directional Data Analysis

Hypothesis test for SIPC distribution over the SESPC distribution

Description

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

Usage

pc.test(x, B = 1, tol = 1e-06)

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.

Details

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

Value

This is an "htest"class object. Thus it returns a list including:

statistic	The test statistic value.
parameter	The degrees of freedom of the test. If bootstrap was employed this is "NA".
p.value	The p-value of the test.
alternative	A character with the alternative hypothesis.
method	A character with the test used.
data.name	A character vector with two elements.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Tsagris M. and Alzeley O. (2024). Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling. https://arxiv.org/pdf/2302.02468.pdf

See Also

iagesag, fishkent, sespc.mle

Examples

x <- rvmf(100, rnorm(3), 15)
iagesag(x)
pc.test(x)

Directional documentation built on Oct. 30, 2024, 9:15 a.m.