R: EM algorithm starting with M-step for parameterized MVN...

me {mclust}

R Documentation

EM algorithm starting with M-step for parameterized MVN mixture models.

Description

Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.

Usage

me(modelName, data, z, prior = NULL, control = emControl(), 
   Vinv = NULL, warn = NULL, ...)

Arguments

`modelName`	A character string indicating the model. The help file for `mclustModelNames` describes the available models.
`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.
`z`	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.
`prior`	Specification of a conjugate prior on the means and variances. See the help file for `priorControl` for further information. The default assumes no prior.
`control`	A list of control parameters for EM. The defaults are set by the call `emControl()`.
`Vinv`	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`.
`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 in `mclust.options("warn")`.
`...`	Catches unused arguments in indirect or list calls via `do.call`.

Value

A list including the following components:

`modelName`	A character string identifying the model (same as the input argument).
`n`	The number of observations in the data.
`d`	The dimension of the data.
`G`	The number of mixture components.
`z`	A matrix whose `[i,k]`th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
`parameters`	`pro` 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. `mean` 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. `variance` A 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. `Vinv` 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.
`loglik`	The log likelihood for the data in the mixture model.
`control`	The list of control parameters for EM used.
`prior`	The specification of a conjugate prior on the means and variances used, `NULL` if no prior is used.
`Attributes:`	`"info"` Information on the iteration. `"WARNING"` An appropriate warning if problems are encountered in the computations.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

C. Fraley and A. E. Raftery (2005). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington.

C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

Examples

## Not run: 
me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))
## End(Not run)

[Package mclust version 5.3 Index]