circ.regs: Many simple circular regressions

In circda: Circular Data Analysis

Many simple circular regressions

Description

Many simple circular regressions.

Usage

circ.regs(y, x, rads = TRUE, type = "vm", tol = 1e-6, logged = FALSE,
maxiters = 100, ncores = 1)

Arguments

y	A vector with the circular data expressed in radians, or angles.
x	A numerical matrix with many variables. A circular regression will be fit to each of these variables.
rads	If the data are expressed in angles set this to FALSE.
type	The distribution to fit, "vm" is von Mises distribution, "cp" is the circular Purkayastha distribution, "pn" is projected normal distribution, and "cipc" is CIPC (or wrapped Cauchy) distribution.
tol	The tolerance value to terminate the Newton-Raphson algorithm.
logged	Do you want the logarithm of the p-value to be returned?
maxiters	The maximum number of iterations the Newton-Raphson algorithm will perform.
ncores	A number specifying the number of cores to use. If more than 1, then parallel processing is performed.

Details

The function performs many regressions with one circular dependent variable and one Euclidean independent variable. For each colum of x a circular regression model is fitted and the hypothesis testing of no association between y and this variable is performed.

Value

A matrix with two columns, the test statistics and their associated (log) p-values.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Mardia K. V. and Jupp P. E. (2000). Directional statistics. Chicester: John Wiley & Sons.

Presnell Brett, Morrison Scott P. and Littell Ramon C. (1998). Projected multivariate linear models for directional data. Journal of the American Statistical Association, 93(443): 1068–1077.

Tsagris M. and Alzeley O. (2025). Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modelling. Australian & New Zealand Journal of Statistics, 67(1): 77–103. https://arxiv.org/pdf/2302.02468.pdf

Purkayastha S. (1991). A Rotationally Symmetric Directional Distribution: Obtained through Max- imum Likelihood Characterization. The Indian Journal of Statistics, Series A, 53(1): 70–83.

See Also

circ.reg

Examples

y <- rcirc(100, mu = 3, kappa = 2, rads = TRUE, type = "vm")
x <-  matrix(rnorm(100 * 10), ncol = 10)
circ.regs(y, x, rads = TRUE, type = "vm")

circda documentation built on Sept. 15, 2026, 5:09 p.m.