Angular Randomisation Test of Homogeneity

Performs Angular Randomisation Test for homogeneity on two samples of circular data after Ruxton et al. (2023). P-values are estimated using permutation.

Usage

ar_test(x, y, n_perm = 1000L, axial = TRUE)

Arguments

x, y

numeric vectors. Angles in degrees

n_perm

integer. Number of permutations

axial

logical. Whether the data are axial, i.e. \(\pi\)-periodical (TRUE, the default) or circular, i.e. \(2 \pi\)-periodical (FALSE).

Value

list containing the test statistic and the p-value

References

Ruxton, G.D., Malkemper, E.P. & Landler, L. Evaluating the power of a recent method for comparing two circular distributions: an alternative to the Watson U2 test. Sci Rep 13, 10007 (2023). https://doi.org/10.1038/s41598-023-36960-1

See also

Other Tests: kuiper_test(), norm_chisq(), rayleigh-test, watson_test(), watson_two_sample, watson_wheeler_test_perm(), weighted-rayleigh-test

Examples

set.seed(20250411)
x1 <- c(35, 45, 50, 55, 60, 70, 85, 95, 105, 120)
x2 <- c(75, 80, 90, 100, 110, 130, 135, 140, 150, 160, 165)
ar_test(x1, x2, axial = FALSE)
#> $statistic
#> [1] 104.3707
#> 
#> $p.value
#> [1] 0.003996004
#> 

# San Andreas Fault Data:
data(san_andreas)
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
ar_test(sa.por$azi.PoR, rvm(100, 135, 10))
#> $statistic
#> [1] 92652.2
#> 
#> $p.value
#> [1] 0.993007
#>