SimulateMixture {flowClust} | R Documentation

## Random Generation from a t Mixture Model with Box-Cox Transformation

### Description

This function can be used to generate a sample from a multivariate t mixture model with Box-Cox transformation.

### Usage

    SimulateMixture(N, w, mu, sigma, nu = 4, lambda)

### Arguments

`N` | The number of observations.
`w` | A vector of length K, containing the K cluster proportions.
`mu` | A matrix of size K x P, where K is the number of clusters and P is the dimension, containing the K mean vectors.
`sigma` | An array of dimension K x P x P, containing the K covariance matrices.
`nu` | The degrees of freedom used for the t distribution.
`lambda` | The Box-Cox transformation parameter. If missing, the conventional t distribution without transformation will be used.

### Value

A matrix of size N x P.

### Author(s)

Raphael Gottardo <raph@stat.ubc.ca>, Kenneth Lo <c.lo@stat.ubc.ca>

### See Also

`flowClust`

### Examples

    library(flowCore)
    library(flowClust)
    ### Number of components
    K <- 5
    ### Dimension
    p <- 2
    ### Number of observations
    n <- 200
    Mu <- matrix(runif(K*p, 0, 20), K, p)
    Sigma <- array(0, c(K, p, p))

    for (k in 1:K)
    {
        Sigma[k,,][outer(1:p, 1:p, ">")] <- runif(p*(p-1)/2,-.1,.1)
        diag(Sigma[k,,]) <- runif(p,0,1)
        ### Make sigma positive definite
        Sigma[k,,] <- Sigma[k,,] %*% t(Sigma[k,,])
    }

    ### Generate the weights
    w <- rgamma(K,10,1)
    w <- w/sum(w)

    y <- SimulateMixture(n, w, Mu, Sigma, nu=4)

[Package flowClust version 3.30.0 Index]