mclust (version 3.4.7)

em: EM algorithm starting with E-step for parameterized Gaussian mixture models.

Description

Implements the EM algorithm for parameterized Gaussian mixture models, starting with the expectation step.

Usage

em(modelName, data, parameters, prior = NULL, control = emControl(),
   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]

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().

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
  [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

emE, ..., emVVV, estep, me, mstep, mclustOptions, do.call

Examples

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

em(modelName = msEst$modelName, data = iris[,-5],
   parameters = msEst$parameters)
do.call("em", c(list(data = iris[,-5]), msEst))   ## alternative call