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.
Value
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.
Details
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.
References
Nyamundanda G., Gormley, I.C. and Brennan, L. (2010) Probabilistic principal components analysis for metabolomic data. Technical report, University College Dublin.
# NOT RUN {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])
# }