Analysis of variance for (hyper-)spherical data
Analysis of variance for (hyper-)spherical data.
hcf.aov(x, ina, fc = TRUE) hclr.aov(x, ina) lr.aov(x, ina) embed.aov(x, ina) het.aov(x, ina)
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. |
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.
A vector with two or three elements, the test statistic, the p-value and the common concentration parameter kappa based on all the data.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>.
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.
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)
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