mppca: mppca
In VBmix: Variational Bayesian Mixture Models

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

estimates the variational posterior distribution of a MPPCA on a data set. A lower bound is calculated and monitored at each iteration. This posterior can be used for various purposes (e.g. MC proposal distribution). It can be transformed using mppcaToGmm and subMppca, outputing a GMM.

1	mppca(data, ncomp, thres = 0.1, maxit = NULL, qmax = NULL)

`data`	matrix of row-elements.
`ncomp`	number of components in the posterior.
`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.
`qmax`	maximal rank of the posterior factor matrices. If NULL, is set to d-1.

estimated posterior MPPCA with ncomp components.

Pierrick Bruneau

Beal, M. J. (2003) _Variational Algorithms for approximate inference_, PhD Thesis, University of London.

newMppca mppcaToGmm subMppca

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# for packaging needs, a low amount of initial components (ie 10) was used.
# A larger amount may be used for better results.
temp <- mppca(pendat, 10, maxit=20, qmax=8)