mclust (version 2.1-14)

me: EM algorithm starting with M-step for parameterized MVN mixture models.

Description

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

Usage

me(modelName, data, z, ...)

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, varying v

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.

z

A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.

...

Any number of the following:

• eps

{ A scalar tolerance for deciding when to terminate computations due to computational singularity in covariances. Smaller values of eps allow computations to proceed nearer to singulari

Value

• A list including the following components:
• 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.
• 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.
• 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.

See Also

meE,..., meVVV, em, mstep, estep, mclustOptions

Examples

data(iris)
irisMatrix <- as.matrix(iris[,1:4])
irisClass <- iris[,5]
 
me(modelName = "VVV", data = irisMatrix, z = unmap(irisClass))