emE {mclust} | R Documentation |
Implements the EM algorithm for a parameterized Gaussian mixture model, starting with the expectation step.
emE(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emV(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emEII(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emVII(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emEEI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emVEI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emEVI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emVVI(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emEEE(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emEEV(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emVEV(data, parameters, prior=NULL, control=emControl(), warn=NULL, ...) emVVV(data, parameters, prior=NULL, control=emControl(), 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. |
parameters |
The parameters of the model:
|
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
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
whenever a singularity is encountered.
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: An R Package for Normal Mixture Modeling and Model-Based Clustering, Technical Report, 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.
msEst <- mstepEEE(data = iris[,-5], z = unmap(iris[,5])) names(msEst) emEEE(data = iris[,-5], parameters = msEst$parameters)