me.weighted | R Documentation |
Implements the EM algorithm for fitting MVN mixture models parameterized by eigenvalue decomposition, when observations have weights, starting with the maximization step.
me.weighted(data, modelName, z, weights = NULL, prior = NULL,
control = emControl(), Vinv = NULL, 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. |
modelName |
A character string indicating the model. The help file for
|
z |
A matrix whose |
weights |
A vector of positive weights, where the |
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 by |
... |
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: |
|
Thomas Brendan Murphy
me
,
meE
, ...,
meVVV
,
em
,
mstep
,
estep
,
priorControl
,
mclustModelNames
,
mclustVariance
,
mclust.options
w <- rep(1,150)
w[1] <- 0
me.weighted(data = iris[,-5], modelName = "VVV",
z = unmap(iris[,5]), weights = w)
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