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

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

`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 |

`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 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.

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. |

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.

`ppcca.metabol.jack`

, `ppcca.scores.plot`

`loadings.plot`

1 2 3 4 5 6 7 | ```
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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