mstep2: Second M-step of the AECM algorithm when fitting a mixture of...
In MetabolAnalyze: Probabilistic Latent Variable Models for Metabolomic Data

Description Usage Arguments Details Value Note Author(s) References See Also

Internal function required for fitting a mixture of PPCA models.

1	mstep2(Y, Tau, Pi, mu, W, Sig, g, p, q)

`Y`	A N x p data matrix.
`Tau`	An N x G matrix of posterior group membership probabilities.
`Pi`	A g vector of group probabilities.
`mu`	A p x g matrix containing the mean for each group.
`W`	A p x q x g array, each sheet of which contains a group specific loadings matrix.
`Sig`	The variance parmeter.
`g`	The number of groups currently being fitted.
`p`	The number of spectral bins in the NMR spectrum.
`q`	The number of principal components in the model being fitted.

Second M-step of the AECM algorithm when fitting a mixture of PPCA models. An internal function.

A list containing

`W`	A p x q x g array, each sheet of which contains a group specific loadings matrix.
`Sig`	The variance parameter.

An internal function.

Nyamundanda Gift, Isobel Claire Gormley and Lorraine Brennan

Nyamundanda, G., Gormley, I.C. and Brennan, L. (2010) Probabilistic principal components analysis for metabolomic data. Technical report. University College Dublin, Ireland.

mppca.metabol

MetabolAnalyze documentation built on Aug. 31, 2019, 5:05 p.m.