Implements the EM algorithm for fitting MVN mixture models parameterized by eigenvalue decomposition, when observations have weights, starting with the maximization step.
me.weighted(modelName, data, z, weights = NULL, prior = NULL,
control = emControl(), Vinv = NULL, warn = NULL, …)A character string indicating the model. The help file for
mclustModelNames describes the available models.
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.
A matrix whose [i,k]th entry is an initial estimate of the
conditional probability of the ith observation belonging to
the kth component of the mixture.
A vector of positive weights, where the [i]th entry is the weight
for the ith observation. If any of the weights are greater than one,
then they are scaled so that the maximum weight is one.
Specification of a conjugate prior on the means and variances.
See the help file for priorControl for further information.
The default assumes no prior.
A list of control parameters for EM. The defaults are set by the call
emControl.
If the model is to include a noise term, Vinv is an estimate of the
reciprocal hypervolume of the data region. If set to a negative value
or 0, the model will include a noise term with the reciprocal hypervolume
estimated by the function hypvol.
The default is not to assume a noise term in the model through the
setting Vinv=NULL.
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 warn using
mclust.options.
Catches unused arguments in indirect or list calls via do.call.
A list including the following components:
A character string identifying the model (same as the input argument).
A matrix whose [i,k]th entry is the
conditional probability of the ith observation belonging to
the kth component of the mixture.
proA vector whose kth component is the mixing proportion for the kth component of the mixture model. If the model includes a Poisson term for noise, there should be one more mixing proportion than the number of Gaussian components.
meanThe mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
varianceA list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for mclustVariance
for details.
VinvThe estimate of the reciprocal hypervolume of the data region used in the computation when the input indicates the addition of a noise component to the model.
The log likelihood for the data in the mixture model.
"info" Information on the iteration.
"WARNING" An appropriate warning if problems are encountered
in the computations.
me,
meE, …,
meVVV,
em,
mstep,
estep,
priorControl,
mclustModelNames,
mclustVariance,
mclust.options
# NOT RUN {
w <- rep(1,150)
w[1] <- 0
me.weighted(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]),weights=w)
# }
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