ppca.metabol: Fit a probabilistic principal components analysis (PPCA)...
In MetabolAnalyze: Probabilistic Latent Variable Models for Metabolomic Data

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

This function fits a probabilistic principal components analysis model to metabolomic spectral data via the EM algorithm.

1 2	ppca.metabol(Y, minq=1, maxq=2, scale = "none", epsilon = 0.1, plot.BIC = FALSE, printout=TRUE)

`Y`	An N x p data matrix where each row is a spectrum.
`minq`	The minimum number of principal components to be fit. By default minq is 1.
`maxq`	The maximum number of principal components to be fit. By default maxq is 2.
`scale`	Type of scaling of the data which is required. The default is "none". Options include "pareto' and "unit" scaling. See `scaling` for further details.
`epsilon`	Value on which the convergence assessment criterion is based. Set by default to 0.1.
`plot.BIC`	Logical indicating whether or not a plot of the BIC values for the different models fitted should be provided. By default, the plot is not produced.
`printout`	Logical indicating whether or not a statement is printed on screen detailing the progress of the algorithm.

This function fits a probabilistic principal components analysis model to metabolomic spectral data via the EM algorithm. A range of models with different numbers of principal components can be fitted.

A list containing:

`q`	The number of principal components in the optimal PPCA model, selected by the BIC.
`sig`	The posterior mode estimate of the variance of the error terms.
`scores`	An N x q matrix of estimates of the latent locations of each observation in the principal subspace.
`loadings`	The maximum likelihood estimate of the p x q loadings matrix.
`BIC`	A vector containing the BIC values for the fitted models.
`AIC`	A vector containing the AIC values for the fitted models.

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.

ppca.metabol.jack, loadings.plot, ppca.scores.plot

data(UrineSpectra)
## Not run: 
mdlfit<-ppca.metabol(UrineSpectra[[1]], minq=2, maxq=2, scale="none")
loadings.plot(mdlfit)
ppca.scores.plot(mdlfit, group=UrineSpectra[[2]][,1])

## End(Not run)