mclust (version 2.0-1)

mstepE: M-step in the EM algorithm for a parameterized MVN mixture model.

Description

Maximization step in the EM algorithm for a parameterized MVN mixture model.

Usage

mstepE(data, z, equalPro, noise = FALSE, ...)
mstepV(data, z, equalPro, noise = FALSE, ...)
mstepEII(data, z, equalPro, noise = FALSE, ...)
mstepVII(data, z, equalPro, noise = FALSE, ...)
mstepEEI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
mstepVEI(data, z, equalPro, noise = FALSE, eps, tol, itmax, warnSingular, ...)
mstepEVI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
mstepVVI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
mstepEEE(data, z, equalPro, noise = FALSE, ...)
mstepEEV(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
mstepVVV(data, z, equalPro, noise = FALSE, ...)

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.

equalPro

A logical value indicating whether or not the components in the model are present in equal proportions. The default is .Mclust$equalPro.

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.

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

For models with iterative M-step ("VEI", "VEE", "VVE", "VEV"), a scalar tolerance for relative convergence of the parameters. The default is .Mclust$tol.

itmax

For models with iterative M-step ("VEI", "VEE", "VVE", "VEV"), an integer limit on the number of EM iterations. The default is .Mclust$itmax.

warnSingular

A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. The default is .Mclust$warnSingular. Not used for models "EII", "VII", "EEE", "VVV".

...

Provided to allow lists with elements other than the arguments can be passed in indirect or list calls with do.call.

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
  An array whose [,,k]th entry is the the covariance matrix of the kth component of the mixture.
• 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.
• loglik
  The logliklihood for the data in the mixture model.
• modelName
  A character string identifying the model (same as the input argument).
• 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. } mstep, me, estep, mclustOptions data(iris) irisMatrix <- as.matrix(iris[,1:4]) irisClass <- iris[,5]

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

  cluster