# dcor.circular: circular distance correlation function In CircMLE: Maximum Likelihood Analysis of Circular Data

## Description

Perform a distance correlation between circular datasets or between circular and linear datasets.

## Usage

 `1` ```dcor.circular(x, y, method = "chord", type = "c-c", ...) ```

## Arguments

 `x` A vector of class 'circular', or numeric vector of angles measured in radians `y` A vector of class 'circular', numeric vector of angles measured in radians, or numeric vector `method` the distance measure to be used. This must be one of the following functions: ‘"angularseparation"’, ‘"chord"’, '"geodesic"’, or '"circ.range"' (default = "chord"). see ?dist.circular for additional details. `type` if ‘type == "c-c"’ then perform a circular-circular distance corellation, else if ‘type == "c-l"’ then perform a circular-linear distance corellation (default = "c-c"). `...` additional parameters passed to the dcor.test function

## Value

Same as from the `dcor.test` function: a list with class ‘htest’containing

method: description of test

statistic: observed value of the test statistic

estimate: dCov(x,y) or dCor(x,y)

estimates: a vector: [dCov(x,y), dCor(x,y), dVar(x), dVar(y)]

replicates: replicates of the test statistic

p.value: approximate p-value of the test

n: sample size

data.name: description of data

`dcor` `dcov` `DCOR` `dcor.test` `dist.circular`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# Circular-circular distance corellation x <- circular::rvonmises(n = 50, mu = circular::circular(0), kappa = 3) y <- x + circular::rvonmises(n = 50, mu = circular::circular(pi), kappa = 10) dcor.circular(x, y) # Run permutation test with 9999 iterations dcor.circular(x, y, R = 9999) # Circular-linear distance corellation x <- circular::rvonmises(n = 50, mu = circular::circular(0), kappa = 3) y <- as.numeric(x) + rnorm(50, mean = 5, sd = 2) dcor.circular(x, y, type = "c-l", R = 9999) ```