Implements the EM algorithm for parameterized Gaussian mixture models, starting with the expectation step.
em(data, modelName, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
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 character string indicating the model. The help file for
A names list giving the parameters of the model. The components are as follows:
Specification of a conjugate prior on the means and variances. The default assumes no prior.
A list of control parameters for EM. The defaults are set by the call
A logical value indicating whether or not a warning should be issued
when computations fail. The default is
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).
The number of observations in the data.
The dimension of the data.
The number of mixture components.
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.
The list of control parameters for EM used.
The specification of a conjugate prior on the means and variances used,
NULL if no prior is used.
"info" Information on the iteration.
"WARNING" An appropriate warning if problems are
encountered in the computations.