mclust (version 2.1-14)

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.

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
  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
  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