estep {mclust} | R Documentation |
Implements the expectation step of EM algorithm for parameterized Gaussian mixture models.
estep( modelName, data, parameters, 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. |
parameters |
A names list giving the parameters of the model. The components are as follows:
|
warn |
A logical value indicating whether or not a warning should be issued
when computations fail. The default is |
... |
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 |
The input parameters. |
loglik |
The loglikelihood for the data in the mixture model. |
Attribute |
|
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 (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
estepE
, ...,
estepVVV
,
em
,
mstep
,
mclustOptions
mclustVariance
msEst <- mstep(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5])) names(msEst) estep(modelName = msEst$modelName, data = iris[,-5], parameters = msEst$parameters)