EMMIXSSL {EMMIXSSL}

Fitting Gaussian mixture models

Description

Fitting Gaussian mixture model to a complete classified dataset or a incomplete classified dataset with/without the missing-data mechanism.

Usage

EMMIXSSL(
  dat,
  zm,
  pi,
  mu,
  sigma,
  ncov,
  xi = NULL,
  type,
  iter.max = 500,
  eval.max = 500,
  rel.tol = 1e-06,
  sing.tol = 1e-20
)

Arguments

dat

An n\times p matrix where each row represents an individual observation

zm

An n-dimensional vector containing the class labels including the missing-label denoted as NA.

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.

xi

A 2-dimensional vector containing the initial values of the coefficients in the logistic function of the Shannon entropy.

type

Three types of Gaussian mixture models, 'ign' indicates fitting the model to a partially classified sample on the basis of the likelihood that ignores the missing label mechanism, 'full' indicates fitting the model to a partially classified sample on the basis of the full likelihood, taking into account the missing-label mechanism, and 'com' indicate fitting the model to a completed classified sample.

iter.max

Maximum number of iterations allowed. Defaults to 500

eval.max

Maximum number of evaluations of the objective function allowed. Defaults to 500

rel.tol

Relative tolerance. Defaults to 1e-15

sing.tol

Singular convergence tolerance; defaults to 1e-20.

Value

objective

Value of objective likelihood

convergence

Value of convergence

iteration

Number of iteration

pi

Estimated vector of the mixing proportions.

mu

Estimated matrix of the location parameters.

sigma

Estimated covariance matrix

xi

Estimated coefficient vector for a logistic function of the Shannon entropy

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)
xi<-c(-0.5,1)
m<-rlabel(dat=dat$Y,pi=pi,mu=mu,sigma=sigma,xi=xi,ncov=2)
zm<-dat$clust
zm[m==1]<-NA
inits<-initialvalue(g=4,zm=zm,dat=dat$Y,ncov=2)
## Not run: 
fit_pc<-EMMIXSSL(dat=dat$Y,zm=zm,pi=inits$pi,mu=inits$mu,sigma=inits$sigma,xi=xi,type='full',ncov=2)

## End(Not run)