Directional (version 5.3)

Anova for (hyper-)spherical data: Analysis of variance for (hyper-)spherical data

Description

Analysis of variance for (hyper-)spherical data.

Usage

hcf.aov(x, ina, fc = TRUE)
hclr.aov(x, ina)
lr.aov(x, ina)
embed.aov(x, ina)
het.aov(x, ina)

Arguments

x

A matrix with the data in Euclidean coordinates, i.e. unit vectors.

ina

A numerical variable or a factor indicating the group of each vector.

fc

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

Value

A vector with two or three elements, the test statistic, the p-value and the common concentration parameter kappa based on all the data.

Details

The high concentration (hcf.aov), high concentration likelihood ratio (hclr.aov), log-likelihood ratio (lr.aov), embedding approach (embed.aov) or the non equal concentration parameters approach (het.aov) 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.boot, spherconc.test, conc.test, hclr.circaov,

Examples

# NOT RUN {
x <- rvmf(60, rnorm(3), 15)
ina <- rep(1:3, each = 20)
hcf.aov(x, ina)
hcf.aov(x, ina, fc = FALSE)
lr.aov(x, ina)
embed.aov(x, ina)
het.aov(x, ina)
# }