mclust (version 3.1-0)

emE: EM algorithm starting with E-step for a parameterized Gaussian mixture model.

Description

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

Usage

emE(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emV(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emEII(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emVII(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emEEI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emVEI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emEVI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emVVI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emEEE(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emEEV(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emVEV(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...)
emVVV(data, parameters, prior=NULL, control=emControl(), 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]

prior

The default assumes no prior, but this argument allows specification of a conjugate prior on the means and variances through the function priorControl.

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 whenever a singularity is encountered. 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 (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 (2006). MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering, Technical Report, Department of Statistics, University of Washington.

See Also

me, mstep, mclustOptions

Examples

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

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