mclust (version 2.1-14)

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

Description

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

Usage

em(modelName, data, mu, ...)

Arguments

modelName

A character string indicating the model: "E": equal variance (one-dimensional) "V": variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, equal volume and shape "VEI": diagonal, varyin

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.

mu

The mean for each component. If there is more than one component, mu is a matrix whose columns are the means of the components.

...

Arguments for model-specific em functions. Specifically:

• An argument describing the variance (depends on the model):[object Object],[object Object],[object Object],[object Object],The form of the variance specification is the same as for the

Value

• A list including the following components:
• z
  A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
• loglik
  The logliklihood for the data in the mixture model.
• mu
  A matrix whose kth column is the mean of the kth component of the mixture model.
• sigma
  For multidimensional models, a three dimensional array in which the [,,k]th entry gives the the covariance for the kth group in the best model.
  For one-dimensional models, either a scalar giving a common variance for the groups or a vector whose entries are the variances for each group in the best model.
• pro
  A vector whose kth component is the mixing proportion for the kth component of the mixture model.
• modelName
  A character string identifying the model (same as the input argument).
• Attributes:
  • "info": Information on the iteration.
  • "warn": An appropriate warning if problems are encountered in the computations.

References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust. C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

Details

This function can be used with an indirect or list call using do.call, allowing the output of e.g. mstep to be passed without the need to specify individual parameters as arguments.

See Also

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

Examples

data(iris)
irisMatrix <- as.matrix(iris[,1:4])
irisClass <- iris[,5]
 
msEst <- mstep(modelName = "EEE", data = irisMatrix, 
               z = unmap(irisClass))
names(msEst)

em(modelName = msEst$modelName, data = irisMatrix,
   mu = msEst$mu, Sigma = msEst$Sigma, pro = msEst$pro)
do.call("em", c(list(data = irisMatrix), msEst))   ## alternative call