rmix: Normal mixture model generator.

In EMMIXSSL: Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism

Normal mixture model generator.

Description

Generate random observations from the normal mixture distributions.

Usage

rmix(n, pi, mu, sigma, ncov = 2)

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 if ncov=1, or a list of g covariance matrices with dimension p\times p \times g if ncov=2.
ncov	Options of structure of sigma matrix; the default value is 2; ncov = 1 for a common covariance matrix; ncov = 2 for the unequal covariance/scale 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,ncov=2)

EMMIXSSL documentation built on Oct. 18, 2022, 5:08 p.m.