circridge.cv: Cross-validation for the ridge circular regression

In circda: Circular Data Analysis

Cross-validation for the ridge circular regression

Description

Cross-validation for the ridge circular regression.

Usage

circridge.cv(y, x, rads = TRUE, type = "vm", lambda = NULL, nlambda = 100, tol = 1e-06,
maxiters = 100, folds = NULL, nfolds = 10, seed = NULL)

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.
tol	The tolerance value to terminate the Fisher scoring algorithm.
maxiters	The maximum number of iterations the Fisher scoring algorithm will perform.
folds	If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
nfolds	The number of folds to produce.
seed	You can specify your own seed number here or leave it NULL.

Details

K-fold cross-validation for the ridge circular regression models.

Value

A list including:

runtime	The runtime of the ridge regression model.
info	A matrix with two columns containing the lambda and the fit, the \sum_i y_i^T\hat{y}_i/n_k.
lambda_opt	The optimal value of the ridge parameter, \lambda.
be	The coefficients of the ridge circular regression model corresponding to the optimal \lambda value.

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

circ.ridge

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.