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.
1 2 |
X |
Numeric input matrix or dataframe (usually measurements obtained through NMR spectroscopy or mass spectrometry) 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
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