Orthogonalizepls and File:Calt savemodel.png: Difference between pages

From Eigenvector Research Documentation Wiki
(Difference between pages)
Jump to navigation Jump to search
imported>Jeremy
No edit summary
 
(Maintenance script uploaded File:Calt savemodel.png)
 
Line 1: Line 1:
===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
: [[Analysis| (This feature is also available from the Analysis window ''Tools'' menu)]]
===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.
===See Also===
[[cov_cv]], [[glsw]], [[pcr]], [[pls]]

Latest revision as of 11:44, 1 August 2019