circ.ridge: Ridge circular regression

In circda: Circular Data Analysis

Ridge circular regression

Description

Ridge circular regression.

Usage

circ.ridge(y, x, rads = TRUE, type = "vm", lambda = NULL, nlambda = 100,
xnew = NULL, tol = 1e-6, maxiters = 100)

Arguments

y	A vector with the circular data expressed in radians, or angles.
x	The independent variable(s). Can be Euclidean or categorical (factor variables).
rads	If the data are expressed in angles set this to FALSE.
type	The distribution to fit, "vm" is von Mises distribution, "pn" is projected normal distribution, "cipc" is CIPC (or wrapped Cauchy) distribution.
lambda	A vector with a sequence of the ridge \lambda values. If you do not know it leave it NULL and the function will compute it internally.
nlambda	The number of \lambda values to compute.
xnew	The new values of some independent variable(s) whose circular values you want to predict. The can be Euclidean or categorical. If you have no new x values, leave it NULL (default).
tol	The tolerance value to terminate the Newton-Raphson algorithm.
maxiters	The maximum number of iterations the Newton-Raphson algorithm will perform.

Details

Ridge regression using the von Mises, the projected normal or the CIPC (or wrapped Cauchy) distribution.

Value

A list including:

runtime	The runtime of the ridge regression model.
info	A matrix with two columns containing the lambda, and the penalized log-likelihood.
be	A list with the ridge beta coefficients.
est	A list with the ridge estimated compositions.

Author(s)

Michail Tsagris.

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

References

Tsagris M., Papastamoulis P. and Kato S. (2025). Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution. Statistics and Computing, 35:51.

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

See Also

circridge.cv, circ.reg

Examples

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

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