em {mclust} | R Documentation |
Implements the EM algorithm for parameterized Gaussian mixture models, starting with the expectation step.
em(modelName, data, parameters, prior = NULL, control = emControl(), 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:
|
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
|
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 |
|
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.
emE
, ...,
emVVV
,
estep
,
me
,
mstep
,
mclustOptions
,
do.call
msEst <- mstep(modelName = "EEE", data = iris[,-5], z = unmap(iris[,5])) names(msEst) em(modelName = msEst$modelName, data = iris[,-5], parameters = msEst$parameters) ## Not run: do.call("em", c(list(data = iris[,-5]), msEst)) ## alternative call ## End(Not run)