Directional (version 4.7)

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)
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 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.

Details

The high concentration (hcf.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.

See Also

hcf.boot, spherconc.test, conc.test

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)
# }