circular_test: Two sample Test of Homogeneity
In SMAC-Group/circTest: Two-sample Tests for Circular Data

Description Usage Arguments Value Examples

This function allows to perform various nonparametric test for homogeneity on two samples of circular data.

circular_test(
  x,
  y,
  test = "dixon",
  alpha = 0.05,
  B = NULL,
  type = "exact",
  seed = 1982,
  circle = TRUE
)

`x`	first sample
`y`	second sample
`test`	considered test (`dixon` for Dixon test (default); `ww` for Wheeler-Watson; `wilcox` for Wilcoxon test; `rao` for Rao test; `vdw` for van der Waerden test; `savage` for Savage test
`alpha`	significance level (default = 0.05)
`B`	number of bootstrap replications
`seed`	seed used for random number generation
`type`	method to compute pvalues (available methods: `exact` for exact computation (default) which is appropriate for small sample sizes; `mc` for approximation based on Monte-carlo simulations)

A list with the following structure:

cv: a list containing the results of the exact distribution (NULL if type = "mc"), see function get_critical_values for details
mc: a list containing the results of the approximated distribution obtained by simulation (NULL if type = "exact"), see function MC_pvalue for details
B: number of bootstrap replications
alpha: significance level
test: the considered test
stat: observed test statistic
spacings: a list containing the observed spacings, see function compute_Sk for details
x: expression deparsing of the first dataset
y: expression deparsing of the second dataset

# Load dataset
data(pigeons)
# Dixon test (exact pvalue)
circular_test(pigeons$experimental, pigeons$control)

# Dixon test (approximated pvalue)
circular_test( pigeons$experimental, pigeons$control, type = "mc")