EMMIXSSL: Fitting Gaussian mixture models

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

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)

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