Directional (version 5.3)

Bootstrap 2-sample mean test for (hyper-)spherical data: Bootstrap 2-sample mean test for (hyper-)spherical data

Description

Bootstrap 2-sample mean test for (hyper-)spherical data.

Usage

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)

Arguments

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.

fc

A boolean that indicates whether a corrected F test should be used or not.

B

The number of bootstraps to perform.

Value

A vector including two or three numbers, the test statistic value, the bootstrap p-value of the test and the common concentration parameter kappa based on all the data.

Details

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.

References

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.

See Also

hcf.aov, hcf.perm, spherconc.test, conc.test

Examples

# NOT RUN {
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)
# }