mclust (version 4.1)

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

Description

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

Usage

meE(data, z, prior=NULL, control=emControl(), 
    Vinv=NULL, warn=NULL, ...)
meV(data, z, prior=NULL, control=emControl(),
    Vinv=NULL, warn=NULL, ...)
meEII(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meVII(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meEEI(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meVEI(data, z, prior=NULL, control=emControl(),
     Vinv=NULL, warn=NULL, ...)
meEVI(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meVVI(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meEEE(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meEEV(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meVEV(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)
meVVV(data, z, prior=NULL, control=emControl(),
      Vinv=NULL, warn=NULL, ...)

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.

prior

Specification of a conjugate prior on the means and variances. The default assumes no prior.

control

A list of control parameters for EM. The defaults are set by the call emControl().

Vinv

An estimate of the reciprocal hypervolume of the data region, when the model is to include a noise term. Set to a negative value or zero if a noise term is desired, but an estimate is unavailable --- in that case function hypvol w

warn

A logical value indicating whether or not certain warnings (usually related to singularity) should be issued when the estimation fails. The default is given by mclust.options("warn").

...

Catches unused arguments in indirect or list calls via do.call.

Value

• A list including the following components:
• modelName
  A character string identifying the model (same as the input argument).
• z
  A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
• parameters
  [object Object],[object Object],[object Object],[object Object]
• loglik
  The log likelihood for the data in the mixture model.
• Attributes:
  "info" Information on the iteration. "WARNING" 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. C. Fraley and A. E. Raftery (2005). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington.

C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

em, me, estep, mclust.options

Examples

meVVV(data = iris[,-5], z = unmap(iris[,5]))