Hypothesis test for IAG distribution over the ESAG distribution
The null hypothesis is whether an IAG distribution fits the data well, where the altenrative is that ESAG distribution is more suitable.
iagesag(x, B = 1, tol = 1e-07)
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. |
Essentially it is a test of rotational symmetry, whether the two γ parameters are equal to zero. This works for spherical data only.
A vector including:
test |
The value of the test statistic. |
p-value or Bootstrap p-value |
The p-value of the test. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr
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.
x <- rvmf(100, rnorm(3), 15) iagesag(x) fishkent(x, B = 1)
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