mclust (version 2.1-14)

meE: EM algorithm starting with M-step for a parameterized MVN mixture model.

Description

Implements the EM algorithm for a parameterized MVN mixture model, starting with the maximization step.

Usage

meE(data, z, eps, tol, itmax, equalPro, warnSingular, 
    noise = FALSE, Vinv)
meV(data, z, eps, tol, itmax, equalPro, warnSingular, 
    noise = FALSE, Vinv)
meEII(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meVII(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meEEI(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meVEI(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meEVI(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meVVI(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meEEE(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meEEV(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meVEV(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)
meVVV(data, z, eps, tol, itmax, equalPro, warnSingular, 
      noise = FALSE, Vinv)

Arguments

data

A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.

z

A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.

eps

A scalar tolerance for deciding when to terminate computations due to computational singularity in covariances. Smaller values of eps allows computations to proceed nearer to singularity. The default is .Mclust$eps

tol

A scalar tolerance for relative convergence of the loglikelihood values. The default is .Mclust$tol.

itmax

An integer limit on the number of EM iterations. The default is .Mclust$itmax.

equalPro

Logical variable indicating whether or not the mixing proportions are equal in the model. The default is .Mclust$equalPro.

warnSingular

A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. The default is .Mclust$warnSingular.

noise

A logical value indicating whether or not the model includes a Poisson noise component. The default assumes there is no noise component.

Vinv

An estimate of the reciprocal hypervolume of the data region. The default is determined by applying function hypvol to the data. Used only when noise = TRUE.

Value

• A list including the following components:
• mu
  A matrix whose kth column is the mean of the kth component of the mixture model.
• sigma
  For multidimensional models, a three dimensional array in which the [,,k]th entry gives the the covariance for the kth group in the best model.
  For one-dimensional models, either a scalar giving a common variance for the groups or a vector whose entries are the variances for each group in the best model.
• pro
  A vector whose kth component is the mixing proportion for the kth component of the mixture model.
• z
  A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
• modelName
  Character string identifying the model.
• loglik
  The logliklihood for the data in the mixture model.
• Attributes:
  The return value also has the following attributes: "info": Information on the iteration. "warn": An appropriate warning if problems are encountered in the computations.

  References

  { C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust. C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust. } em, me, estep, mclustOptions data(iris) irisMatrix <- as.matrix(iris[,1:4]) irisClass <- iris[,5]

  meVVV(data = irisMatrix, z = unmap(irisClass))

  cluster