Density 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

dmix.compnorm(x, mu, sigma, prob, type = "alr", logged = TRUE)

Arguments

x

A vector or a matrix with compositional data.

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

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

logged

A boolean variable specifying whether the logarithm of the density values to be returned. It is set to TRUE by default.

Details

A sample from a multivariate Gaussian mixture model is generated.

Value

A vector with the density values.

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")$x
mod <- dmix.compnorm(y, mu, s, p)