bic.mixvmf: BIC to choose the number of components in a model based...

In Directional: A Collection of Functions for Directional Data Analysis

BIC to choose the number of components in a model based clustering using mixtures of rotationally symmetric distributions

Description

BIC to choose the number of components in a model based clustering using mixtures of rotationally symmetric distributions

Usage

bic.mixvmf(x, G = 5, n.start = , tol = 1e-6, maxiters = 500)
bic.mixspcauchy(x, G = 5, n.start = 5, tol = 1e-6, maxiters = 500)
bic.mixpkbd(x, G = 5, n.start = 5, tol = 1e-6, maxiters = 500)

Arguments

x	A matrix containing directional data.
G	The maximum number of clusters to be tested. Default value is 5.
n.start	The number of random starts to try. See also R's built-in function kmeans for more information about this.
tol	The tolerance value to terminate the EM algorithm.
maxiters	The maximum number of iterations to perform.

Details

The function computes the BIC (and ICL) to decide on the optimal number of clusters when using mixtures of von Mises-Fisher, mixtures of spherical Cauchy or mixtures of Poisson kernel-based distributions.

Value

A plot of the BIC 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 [e-mail].

References

Hornik, K. and Grun, B. (2014). movMF: An R package for fitting mixtures of von Mises-Fisher distributions. Journal of Statistical Software, 58(10): 1–31.

Biernacki C., Celeux G. and Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7): 719–725.

Tsagris M. and Papastamoulis P. (2024). Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution. https://arxiv.org/pdf/2409.03292

See Also

mixvmf.mle, rmixvmf, mixvmf.contour

Examples

x <- as.matrix( iris[, 1:4] )
x <- x / sqrt( rowSums(x^2) )
bic.mixvmf(x)

Directional documentation built on Oct. 30, 2024, 9:15 a.m.