mix.diri: Dirichlet mixture models for compositional data

In Compositional: Compositional Data Analysis

Dirichlet mixture models for compositional data

Description

Dirichlet mixture models for compositional data.

Usage

mix.diri(x, g, tol = 1e-4)

Arguments

x	A matrix with the compositional data.
g	How many clusters to create.
tol	The tolerance level indicating no further increase in the log-likelihood.

Details

A Dirichlet mixture model is fitted.

Value

A list including:

param	A matrix where each row corresponds to the mixing probability and the estimated parameters of each cluster.
loglik	The log-likelihood of the model.
probs	A vector with the probability of each observation belonging to each cluster.
est	The estimated cluster membership values.
iters	The number of iterations the EM algorithm performed.
runtime	The runtime of the algorithm.

Author(s)

Michail Tsagris.

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

References

Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas (2015). R package mixture: Mixture Models for Clustering and Classification.

Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.

See Also

bic.mixdiri, rmixdiri, mix.compnorm

Examples

x <- as.matrix(iris[, 1:4])
x <- x/ rowSums(x)
mod <- mix.diri(x, 3)

Compositional documentation built on Aug. 21, 2026, 9:06 a.m.