# R/hcf.aov.R In Directional: A Collection of Functions for Directional Data Analysis

#### Documented in hcf.aov

```hcf.aov <- function(x, ina, fc = TRUE) {
## x contains all the data
## ina is an indicator variable of each sample
ina <- as.numeric(ina)
g <- max(ina)  ## how many groups are there
p <- dim(x)[2]
n <- dim(x)[1]  ## dimensionality and sample size of the data
S <- rowsum(x, ina)
Ri <- sqrt( Rfast::rowsums(S^2) )  ## the resultant length of each group
S <- Rfast::colsums(x)
R <- sqrt( sum(S^2) )  ## the resultant length based on all the data
## Next we stimate the common concentration parameter kappa
kappa <- Directional::vmf.mle(x, fast = TRUE)\$kappa
## kappa is the estimated concentration parameter based on all the data
Ft <- (n - g) * (sum(Ri) - R) /( (g - 1) * (n - sum(Ri)) )
if (fc) {  ## correction is used
if (p == 3) {
Ft <- kappa * (1/kappa - 1/(5 * kappa^3)) * Ft
} else if (p > 3)  Ft <- kappa * ( 1/kappa - (p - 3)/(4 * kappa^2) - (p - 3)/(4 * kappa^3) ) * Ft
}
pvalue <- pf(Ft, (g - 1) * (p - 1), (n - g) * (p - 1), lower.tail = FALSE)
res <- c(Ft, pvalue, kappa)
names(res) <- c('test', 'p-value', 'kappa')
res
}
```

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Directional documentation built on Nov. 8, 2021, 1:07 a.m.