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
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
A vector of positive weights, where the
Specification of a conjugate prior on the means and variances.
See the help file for
A list of control parameters for EM. The defaults are set by the call
If the model is to include a noise term,
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:
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.
A 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.
The 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.
A list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for
The 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.
Thomas Brendan Murphy