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Bootstrap 2-sample mean test for (hyper-)spherical data.
hcf.boot(x1, x2, fc = TRUE, B = 999)
lr.boot(x1, x2, B = 999)
hclr.boot(x1, x2, B = 999)
embed.boot(x1, x2, B = 999)
het.boot(x1, x2, B = 999)This is an "htest"class object. Thus it returns a list including:
The test statistic value.
The degrees of freedom of the test. Since these are bootstrap based tests this is "NA".
The p-value of the test.
A character with the alternative hypothesis.
A character with the test used.
A character vector with two elements.
A matrix with the data in Euclidean coordinates, i.e. unit vectors.
A matrix with the data in Euclidean coordinates, i.e. unit vectors.
A boolean that indicates whether a corrected F test should be used or not.
The number of bootstraps to perform.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
The high concentration (hcf.boot), log-likelihood ratio (lr.boot), high concentration log-likelihood ratio (hclr.boot), embedding approach (embed.boot) or the non equal concentration parameters approach (het.boot) is used.
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.
Tsagris M. and Alenazi A. (2024). An investigation of hypothesis testing procedures for circular and spherical mean vectors. Communications in Statistics-Simulation and Computation, 53(3): 1387--1408.
hcf.aov, hcf.perm, hcfboot
x <- rvmf(60, rnorm(3), 15)
ina <- rep(1:2, each = 30)
x1 <- x[ina == 1, ]
x2 <- x[ina == 2, ]
hcf.boot(x1, x2)
lr.boot(x1, x2)
het.boot(x1, x2)
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