Simulation of compositional data from Gaussian mixture models {Compositional}

Simulation of compositional data from Gaussian mixture models

Description

Simulation of compositional data from Gaussian mixture models.

Usage

rmixcomp(n, prob, mu, sigma, type = "alr")

Arguments

n

The sample size.

prob

A vector with mixing probabilities. Its length is equal to the number of clusters.

mu

A matrix where each row corresponds to the mean vector of each cluster.

sigma

An array consisting of the covariance matrix of each cluster.

type

Should the additive ("type=alr") or the isometric (type="ilr") log-ration be used? The default value is for the additive log-ratio transformation.

Details

A sample from a multivariate Gaussian mixture model is generated.

Value

A list including:

id

A numeric variable indicating the cluster of simulated vector.

x

A matrix containing the simulated compositional data. The number of dimensions will be + 1.

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.

See Also

mix.compnorm, bic.mixcompnorm

Examples

p <- c(1/3, 1/3, 1/3)
mu <- matrix(nrow = 3, ncol = 4)
s <- array( dim = c(4, 4, 3) )
x <- as.matrix(iris[, 1:4])
ina <- as.numeric(iris[, 5])
mu <- rowsum(x, ina) / 50
s[, , 1] <- cov(x[ina == 1, ])
s[, , 2] <- cov(x[ina == 2, ])
s[, , 3] <- cov(x[ina == 3, ])
y <- rmixcomp(100, p, mu, s, type = "alr")