Simulation of compositional data from Gaussian mixture models.
dmix.compnorm(x, mu, sigma, prob, type = "alr", logged = TRUE)A vector or a matrix with compositional data.
A vector with mixing probabilities. Its length is equal to the number of clusters.
A matrix where each row corresponds to the mean vector of each cluster.
An array consisting of the covariance matrix of each cluster.
The type of trasformation used, either the additive log-ratio ("alr"), the isometric log-ratio ("ilr") or the pivot coordinate ("pivot") transformation.
A boolean variable specifying whether the logarithm of the density values to be returned. It is set to TRUE by default.
A vector with the density values.
A sample from a multivariate Gaussian mixture model is generated.
Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas (2015). R package mixture: Mixture Models for Clustering and Classification.
# NOT RUN {
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)
# }
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