EM: EM
In VBmix: Variational Bayesian Mixture Models

Description Usage Arguments Value Author(s) References See Also Examples

estimates a GMM on data using EM algorithm.

1 2	EM(data, ncomp, model=c("general", "diagonal", "spherical"), class=FALSE, thres = 0.1, maxit = NULL, rbic=FALSE, debug=FALSE)

`data`	matrix of row-elements.
`ncomp`	maximal number of components in the GMM. In case of degeneracies, the final model size may be less than ncomp.
`model`	Hypothesis on the modfel to estimate: "general", "diagonal" or "spherical" covariance matrices.
`class`	If TRUE, hard allocate elements in the E step (see CEM variant in Biernacki et al.). If FALSE, compute soft responsibilities as in usual EM algorithm.
`thres`	threshold for lower bound variations between 2 iterations. Convergence is decided if this variation is below thres.
`maxit`	if NULL, the stopping criterion is related to thres. If not NULL, maxit iterations are performed.
`rbic`	if FALSE, output BIC criterion associated to the obtained GMM. If TRUE, use a variant that accounts for the dimensionality of the model.
`debug`	if TRUE, display debug markers.

estimated GMM with at most ncomp components, with labels containing associated labels for data in addition.

`labels`	Cluster labels taking values in 1..k
`w`	Numeric vector of cluster weights
`mean`	List of mean vectors
`cov`	List of covariance matrices
`likelihood`	Likelihood value of the model
`bic`	BIC criterion of the model

Pierrick Bruneau

Bishop, C. M. (2006) _Pattern Recognition and Machine Learning_, Chapter 9, Springer. Biernacki, C. et al. _Model-based cluster and discriminant analysis with the MIXMOD software_, Computational Statistics and Data Analysis 51.2 (2006): 587-600.

newGmm varbayes