opls.par: Fitting Orthogonal-Partial Least Squares Models -...
In kimsche/MetaboMate: All you need for NMR-based metabolic profiling with R!

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

This function is used to fit Orthogonal-Partial Least Squares (O-PLS) models. It can be used to carry out regression or discriminant analysis. In the latter case the outcome can have two or more levels. It automatically uses all cores available on the machine.

1 2	opls.par(X, Y, t_pred = 1, center = T, scale = "UV", cv.k = 7, cv.type = "k-fold", plotting = T, maxPCo = 5)

`X`	Numeric input matrix (measurements derived by NMR spectroscopy or MS) with each row representing an observation and each column a metabolic feature.
`Y`	Response vector or matrix with same length or number of columns than rows in X, respectively.
`t_pred`	Parameter specifying the maximum number of predictive components (needed only for multi-factor Y)
`center`	Logical value (TRUE or FALSE) indicating if features should be mean centered.
`scale`	Desired scaling method (currently only no or unit variance scaling (UV) implemented).
`cv.k`	The number of cross-validation sets. This depends on the number of observations in X but typically takes a value between 3 and 9.
`cv.type`	Type or cross-validation: 'k-fold', 'k-fold_stratified', 'MC', 'MC_stratified' (see Details).
`plotting`	Logical value (TRUE or FALSE) indicating if model parameters (R2X, Q2, etc) should be visualised once the model is trained.
`maxPCo`	The maximum number of orthogonal components (in case stop criteria fail).

Models are fully statistically validated, currently only k-fold cross validation (CV) and class-balanced k-fold cross validation is implemented. Further extensions, e.g. Monte-Carlo CV, are work in progress. Although the algorithm accepts three and more levels as Y, model interpretation is more straightforward for pairwise group comparisons.

This function returns an OPLS_MetaboMate S4 object.

Torben Kimhofer tkimhofer@gmail.com

Trygg J. and Wold, S. (2002) Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16.3, 119-128.

Geladi, P and Kowalski, B.R. (1986), Partial least squares and regression: a tutorial. Analytica Chimica Acta, 185, 1-17.

OPLS_MetaboMate-class dmodx plotscores plotload specload