Permutation based 2-sample mean test for (hyper-)spherical data
Permutation based 2-sample mean test for (hyper-)spherical data.
hcf.perm(x1, x2, B = 999) lr.perm(x1, x2, B = 999) hclr.perm(x1, x2, B = 999) embed.perm(x1, x2, B = 999) het.perm(x1, x2, B = 999)
x1 |
A matrix with the data in Euclidean coordinates, i.e. unit vectors. |
x2 |
A matrix with the data in Euclidean coordinates, i.e. unit vectors. |
B |
The number of permutations to perform. |
The high concentration (hcf.perm), log-likelihood ratio (lr.perm), high concentration log-likelihood ratio (hclr.perm), embedding approach (embed.perm) or the non equal concentration parameters approach (het.perm) is used.
A vector including:
test |
The test statistic value. |
p-value |
The p-value of the F test. |
kappa |
The common concentration parameter kappa based on all the data. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Mardia, K. V. and Jupp, P. E. (2000). Directional statistics. Chicester: John Wiley & Sons.
Rumcheva P. and Presnell B. (2017). An improved test of equality of mean directions for the Langevin-von Mises-Fisher distribution. Australian & New Zealand Journal of Statistics, 59(1), 119-135.
x <- rvmf(60, rnorm(3), 15) ina <- rep(1:2, each = 30) x1 <- x[ina == 1, ] x2 <- x[ina == 2, ] hcf.perm(x1, x2) lr.perm(x1, x2) het.boot(x1, x2)
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