Directional (version 5.3)

Anova for circular data: Analysis of variance for circular data

Description

Analysis of variance for circular data.

Usage

hcf.circaov(u, ina, rads = FALSE)
hclr.circaov(u, ina, rads = FALSE)
lr.circaov(u, ina, rads = FALSE)
het.circaov(u, ina, rads = FALSE)
embed.circaov(u, ina, rads = FALSE)

Arguments

u

A numeric vector containing the data.

ina

A numerical or factor variable indicating the group of each value.

rads

If the data are in radians, this should be TRUE and FALSE otherwise.

Value

A vector including:

test

The value of the test statistic.

p-value

The p-value of the test.

kappa

The concentration parameter based on all the data. If the het.circaov is used this argument is not returned.

Details

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

conc.test, hclr.aov, hcfcirc.boot

Examples

# NOT RUN {
x <- rvonmises(100, 2.4, 15)
ina <- rep(1:4,each = 25)
hcf.circaov(x, ina, rads = TRUE)
lr.circaov(x, ina, rads = TRUE)
het.circaov(x, ina, rads = TRUE)
embed.circaov(x, ina, rads = TRUE)
# }