estepE {mclust} | R Documentation |
Implements the expectation step in the EM algorithm for a parameterized Gaussian mixture model.
estepE(data, parameters, warn = NULL, ...) estepV(data, parameters, warn = NULL, ...) estepEII(data, parameters, warn = NULL, ...) estepVII(data, parameters, warn = NULL, ...) estepEEI(data, parameters, warn = NULL, ...) estepVEI(data, parameters, warn = NULL, ...) estepEVI(data, parameters, warn = NULL, ...) estepVVI(data, parameters, warn = NULL, ...) estepEEE(data, parameters, warn = NULL, ...) estepEEV(data, parameters, warn = NULL, ...) estepVEV(data, parameters, warn = NULL, ...) estepVVV(data, parameters, 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:
|
warn |
A logical value indicating whether or certain warnings should be issued.
The default is set in |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
Character string identifying the model. |
z |
A matrix whose |
parameters |
The input parameters. |
loglik |
The logliklihood 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.
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.
estep
,
em
,
mstep
,
do.call
,
mclustOptions
,
mclustVariance
msEst <- mstepEII(data = iris[,-5], z = unmap(iris[,5])) names(msEst) estepEII(data = iris[,-5], parameters = msEst$parameters)