mclust (version 4.1)

estepE: E-step in the EM algorithm for a parameterized Gaussian mixture model.

Description

Implements the expectation step in the EM algorithm for a parameterized Gaussian mixture model.

Usage

estepE(data, parameters, warn = NULL, ...)
estepV(data, parameters, warn = NULL, ...)
estepEII(data, parameters, warn = NULL, ...)
estepVII(data, parameters, warn = NULL, ...)
estepEEI(data, parameters, warn = NULL, ...)
estepVEI(data, parameters, warn = NULL, ...)
estepEVI(data, parameters, warn = NULL, ...)
estepVVI(data, parameters, warn = NULL, ...)
estepEEE(data, parameters, warn = NULL, ...)
estepEEV(data, parameters, warn = NULL, ...)
estepVEV(data, parameters, warn = NULL, ...)
estepVVV(data, parameters, 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.

parameters

The parameters of the model: [object Object],[object Object],[object Object],[object Object]

warn

A logical value indicating whether or certain warnings should be issued. 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
  Character string identifying the 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.
• parameters
  The input parameters.
• loglik
  The logliklihood for the data in the mixture model.
• Attribute
  "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.

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

estep, em, mstep, do.call, mclust.options, mclustVariance

Examples

msEst <- mstepEII(data = iris[,-5], z = unmap(iris[,5]))
names(msEst)

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