bic.mixcirc: BIC and ICL for the model based clustering with circular...

In circda: Circular Data Analysis

BIC and ICL for the model based clustering with circular distributions

Description

BIC and ICL for the model based clustering with circular distributions.

Usage

bic.mixcirc(u, rads = TRUE, type = "vm", G = 5, tol = 1e-4, maxiters = 500)

Arguments

u	A matrix containing directional data.
rads	If the data are expressed in angles set this to FALSE.
type	The distribution to fit, "vm" is von Mises mixtures, "cp" is circular Purkayastha mixtures, "pn" is projected normal mixtures, "gcpc" is GCPC mixtures and "cipc" is CIPC (or wrapped Cauchy) mixtures.
G	The maximum number of clusters to be tested. Default value is 5.
tol	The tolerance value to terminate the EM algorithm.
maxiters	The maximum number of iterations the EM algorithm will perform.

Details

The function computes the BIC and ICL to decide on the optimal number of clusters when using mixtures of SESPC or mixtures of ESAG distributions.

Value

A plot of the ICL values and a list including:

bic	The BIC values for all the models tested.
icl	The ICL values for all the models tested.
runtime	The run time of the algorithm. A numeric vector. The first element is the user time, the second element is the system time and the third element is the elapsed time.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris.

References

Perdikis T., Alharbi N. and Tsagris M. (2026). Model–based clustering for spherical and hyper–spherical data using elliptically symmetric distributions.

https://arxiv.org/abs/2605.27496

See Also

mixcirc.mle, circ.mle

Examples

u1 <- rnorm(100, 3, 0.1)
u2 <- rnorm(100, 4, 0.1)
u <- c(u1, u2)
bic.mixcirc(u, type = "vm", G = 5)

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