mclust (version 3.4.7)

me: EM algorithm starting with M-step for parameterized MVN mixture models.

Description

Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.

Usage

me(modelName, data, z, prior = NULL, control = emControl(), 
   Vinv = NULL, warn = NULL, ...)

Arguments

modelName

A character string indicating the model. The help file for mclustModelNames describes the available models.

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 an initial estimate of 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. See the help file for priorControl for further information. The default assumes no prior.

control

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

Vinv

If the model is to include a noise term, Vinv is an estimate of the reciprocal hypervolume of the data region. If set to a negative value or 0, the model will include a noise term with the reciprocal hypervolume estimated by the

warn

A logical value indicating whether or not certain warnings (usually related to singularity) should be issued when the estimation fails. The default is set in .Mclust$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 (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.

C. Fraley and A. E. Raftery (2002). 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.

See Also

meE,..., meVVV, em, mstep, estep, priorControl, mclustModelNames, mclustVariance, mclustOptions

Examples

me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))