Orthogonalizepls: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Condenses y-variance into first component of a PLS model. | Condenses y-variance into first component of a PLS model (similar to OPLS). | ||
===Synopsis=== | ===Synopsis=== |
Revision as of 12:11, 21 December 2010
Purpose
Condenses y-variance into first component of a PLS model (similar to OPLS).
Synopsis
- omodel = orthogonalizepls(model,x,y) %orthogonalize model
- omodel = orthogonalizepls(omodel,x) %calculate scores for applying omodel to x
Description
Produces an orthogonal PLS model which contains all the y-variance capturing direction in the first weight and loading. The predictions of the model are identical to the non-orthogonalized model but the loadings and weights have been rotated.
If no y-block information is passed, it is assumed that the model has already been orthogonalized and is being applied to the passed x-block data. In this case, only the new scores are calculated.
Inputs
- model = Standard PLS model to orthogonalize OR orthogonalized model (if no y passed)
- x = Preprocessed x-block data. Preprocessed in the same way as is indicated in the model.
Optional Inputs
- y = Preprocessed y-block data. If omitted, x is assumed to be NEW data to which the model is being applied. Otherwise, x and y are assumed to be the calibration data from which the model was created and model will be orthogonalized.
Outputs
- omodel = Model with orthogonalized loadings and scores.