mclust (version 5.2.2)

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:

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:

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)