meE {mclust} | R Documentation |
Implements the EM algorithm for a parameterized Gaussian mixture model, starting with the maximization step.
meE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
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 |
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
|
Vinv |
An estimate of the reciprocal hypervolume of the data region, when the
model is to include a noise term. Set to a negative value or zero if
a noise term is desired, but an estimate is unavailable — in that
case function |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is set in |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
z |
A matrix whose |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
Attributes: |
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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 (2002a). 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.
em
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me
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estep
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mclustOptions
meVVV(data = iris[,-5], z = unmap(iris[,5]))