Simulation of compositional data from Gaussian mixture models {Compositional} | R Documentation |
Simulation of compositional data from Gaussian mixture models.
rmixcomp(n, prob, mu, sigma, type = "alr")
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. |
A sample from a multivariate Gaussian mixture model is generated.
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. |
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas (2015). R package mixture: Mixture Models for Clustering and Classification.
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")