Fit a probabilistic principal components and covariates analysis (PPCCA) model to a metabolomic data set via the EM algorithm.

Description

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

Usage

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

Arguments

Y

An N x p data matrix in which each row is a spectrum.

Covars

An N x L covariate data matrix in which each row is a set of covariates.

minq

The minimum number of principal components to be fit.

maxq

The maximum number of principal components to be fit.

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.

Details

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

Care should be taken with the form of covariates supplied. All covariates are standardized (to lie in [0,1]) within the ppcca.metabol function for stability reasons. Hence continuous covariates and binary valued categorical covariates are easily handled. For a categorical covariate with V levels, the equivalent V-1 dummy variables representation should be passed as an argument to ppcca.metabol.

Value

A list containing:

q

The number of principal components in the optimal PPCCA 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.

coefficients

The maximum likelihood estimates of the regression coefficients associated with the covariates in the PPCCA model.

BIC

A vector containing the BIC values for the fitted models.

AIC

A vector containing the AIC values for the fitted models.

Author(s)

Nyamundanda Gift, Isobel Claire Gormley and Lorraine Brennan.

References

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

See Also

ppcca.metabol.jack, ppcca.scores.plotloadings.plot

Examples

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data(UrineSpectra)
## Not run: 
mdlfit<-ppcca.metabol(UrineSpectra[[1]], UrineSpectra[[2]][,2], minq=2, maxq=2)
loadings.plot(mdlfit)
ppcca.scores.plot(mdlfit, UrineSpectra[[2]][,2], group=UrineSpectra[[2]][,1], covarnames="Weight")

## End(Not run)

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