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| ===Purpose===
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| Condenses y-variance into first component of a PLS model (similar to OPLS).
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| ===Synopsis===
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| : omodel = orthogonalizepls(model,x,y) %orthogonalize model
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| : omodel = orthogonalizepls(omodel,x) %calculate scores for applying omodel to x
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| : [[Analysis| (This feature is also available from the Analysis window ''Tools'' menu)]]
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| ===Description===
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| 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.
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| 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.
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| ====Inputs====
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| * '''model''' = Standard PLS model to orthogonalize OR orthogonalized model (if no y passed)
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| * '''x''' = Preprocessed x-block data. Preprocessed in the same way as is indicated in the model.
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| ====Optional Inputs====
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| * '''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.
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| ====Outputs====
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| * '''omodel''' = Model with orthogonalized loadings and scores.
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| ===See Also===
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| [[cov_cv]], [[glsw]], [[pcr]], [[pls]]
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Latest revision as of 11:44, 1 August 2019