mclust (version 4.1)

estep: E-step for parameterized Gaussian mixture models.

Description

Implements the expectation step of EM algorithm for parameterized Gaussian mixture models.

Usage

estep( modelName, data, parameters, 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.

parameters

A names list giving the parameters of the model. The components are as follows: [object Object],[object Object],[object Object],[object Object]

warn

A logical value indicating whether or not a warning should be issued when computations fail. The default is warn=FALSE.

...

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
  The input parameters.
• loglik
  The loglikelihood for the data in the mixture model.
• Attributes
  "WARNING": an appropriate warning if problems are encountered in the computations.

References

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, 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

estepE, ..., estepVVV, em, mstep, mclust.options mclustVariance

Examples

msEst <- mstep(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))
names(msEst)

estep(modelName = msEst$modelName, data = iris[,-5],
      parameters = msEst$parameters)