Compositional (version 4.3)

Mixture model selection via BIC: Mixture model selection via BIC

Description

Mixture model selection via BIC.

Usage

bic.mixcompnorm(x, G, type = "alr", graph = TRUE)

Arguments

x

A matrix with compositional data.

G

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

type

The type of trasformation to be used, either additive log-ratio ("alr") or the isometric log-ratio ("ilr").

graph

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

Value

a plot with the BIC of the best model for each number of components versus the number of components. A list including:

mod

A message informing the user about the best model.

BIC

The BIC values for every possible model and number of components.

Details

The alr or the ilr-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.

References

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.

See Also

mix.compnorm, mixnorm.contour, rmixcomp

Examples

# NOT RUN {
x <- as.matrix( iris[, 1:4] )
x <- x/ rowSums(x)
bic.mixcompnorm(x, 1:3, type = "alr", graph = FALSE)
bic.mixcompnorm(x, 1:3, type = "ilr", graph = FALSE)
# }