rmix {gmmsslm}

Normal mixture model generator.

Description

Generate random observations from the normal mixture distributions.

Usage

rmix(n, pi, mu, sigma)

Arguments

n

Number of observations.

pi

A g-dimensional vector for the initial values of the mixing proportions.

mu

A p \times g matrix for the initial values of the location parameters.

sigma

A p\times p covariance matrix,or a list of g covariance matrices with dimension p\times p \times g. It is assumed to fit the model with a common covariance matrix if sigma is a p\times p covariance matrix; otherwise it is assumed to fit the model with unequal covariance matrices.

Value

Y

An n\times p numeric matrix with samples drawn in rows.

Z

An n\times g numeric matrix; each row represents zero-one indicator variables defining the known class of origin of each.

clust

An n-dimensional vector of class partition.

Examples

n<-150
pi<-c(0.25,0.25,0.25,0.25)
sigma<-array(0,dim=c(3,3,4))
sigma[,,1]<-diag(1,3)
sigma[,,2]<-diag(2,3)
sigma[,,3]<-diag(3,3)
sigma[,,4]<-diag(4,3)
mu<-matrix(c(0.2,0.3,0.4,0.2,0.7,0.6,0.1,0.7,1.6,0.2,1.7,0.6),3,4)
dat<-rmix(n=n,pi=pi,mu=mu,sigma=sigma)