me {mclust} | R Documentation |
Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.
me(modelName, data, z, prior = NULL, control = emControl(), Vinv = NULL, warn = NULL, ...)
modelName |
A character string indicating the model. The help file for
|
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.
See the help file for |
control |
A list of control parameters for EM. The defaults are set by the call
|
Vinv |
If the model is to include a noise term, |
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: |
|
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.
meE
,...,
meVVV
,
em
,
mstep
,
estep
,
priorControl
,
mclustModelNames
,
mclustVariance
,
mclustOptions
me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))