# rmixcomp: Simulation of compositional data from Gaussian mixture models In Compositional: Compositional Data Analysis

## Description

Simulation of compositional data from Gaussian mixture models.

## Usage

 `1` ```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 <[email protected]> and Giorgos Athineou <[email protected]>

## References

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

```mix.compnorm, bic.mixcompnorm ```
 ``` 1 2 3 4 5 6 7 8 9 10``` ```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]) for (i in 1:3) { mu[i, ] <- colMeans(x[ina == i, ]) s[, , i] <- cov(x[ina == i, ]) } y <- rmixcomp(100, p, mu, s, type = "alr") ```