Mixture model selection with the alpha-transformation using BIC {Compositional}R Documentation

Mixture model selection with the α-transformation using BIC


Mixture model selection with the α-transformation using BIC.


bic.alfamixnorm(x, G, a = seq(-1, 1, by = 0.1), veo = FALSE, graph = TRUE)



A matrix with compositional data.


A numeric vector with the number of components, clusters, to be considered.


A vector with a grid of values of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If α=0 the isometric log-ratio transformation is applied.


Stands for "Variables exceed observations". If TRUE then if the number variablesin the model exceeds the number of observations, but the model is still fitted.


A boolean variable, TRUE or FALSE specifying whether a graph should be drawn or not.


The α-transformation is applied to the compositional data first and then mixtures of multivariate Gaussian distributions are fitted. BIC is used to decide on the optimal model and number of components.


A plot with the BIC of the best model for each number of components versus the number of components and a list with the results of the Gaussian mixture model for each value of α.


Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.


Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas (2018). mixture: Mixture Models for Clustering and Classification. R package version 1.5.

Ryan P. Browne and Paul D. McNicholas (2014). Estimating Common Principal Components in High Dimensions. Advances in Data Analysis and Classification, 8(2), 217-226.

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

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

See Also

alfa.mix.norm, mix.compnorm, mixnorm.contour, rmixcomp, alfa, alfa.knn, alfa.rda, comp.nb


## Not run: 
x <- as.matrix( iris[, 1:4] )
x <- x/ rowSums(x)
bic.alfamixnorm(x, 1:3, a = c(0.4, 0.5, 0.6), graph = FALSE)

## End(Not run)

[Package Compositional version 5.2 Index]