Description Usage Arguments Details Value Author(s) References See Also
Calculation of OPLS model predictions using new data
1 | pred.opls(opls_model, newdata)
|
opls_model |
OPLS model (regression of discriminant analysis) of class |
newdata |
NMR data matrix or dataframe with rows representing spectra and identical features in columns as data matrix used to calculate original OPLS model. |
Class predictions for discriminant analysis are not adjusted for unbalanced sample sizes and therefore, predictions can be biased towards the group with the largest number of samples. The list element t_orth_pca
represent scores of the first principal component of a PCA model caclulated with all orthogonal components, therefore, summarises all orthogonal components into a single one. This can only be done if there are more than one orthogonal components in opls_modelel
, otherwise this list element is NULL
.
Returned is a list with the following elements:
Class or numeric outcome predictions for discriminant analysis or regression, repspectively.
Predicted OPLS model scores for predictive component(s).
Predicted OPLS model scores for orthogonal component(s).
Scores of a PCA model (first component) calculated using all predicted OPLS orthogonal component scores - only done when there are more than one orthogonal components in opls_model
.
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.
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