OSC-PLS-DA
Description
Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of minimum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares Discriminant Analysis (PLS-DA) is a variant used when the Y is categorical.See Wikipedia.
But, because PLS has problems in dealing with strong and structured noise in the descriptor matrix X, the Orthogonal Signal Correction (OSC) filters can be used to remove structured Y-orthogonal variation from X. Cf Wehrens R. (2011)
See also:
Usage
| Field | Description |
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| Launch | Field indicating whether you want to execute this analysis in the workflow (checked) or not (unchecked). |
to finish …
Output Examples
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F1 OSC-PLS
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F1 Validation
Data: X dimension: 54 191
Y dimension: 54 1
Fit method: kernelpls
Number of components considered: 7
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[1] "R2 Y,X = 0.22965262"
PLS OSC1 OSC2
R2 Yhat,X 0.23037326 0.23021366 0.23003459
R2 Y,Xm 0.28590360 0.38412967 0.43262628
R2 to,X NA 0.20987811 0.10139296
R2 t1,X 0.25413110 0.24079471 0.23426993
R2 t2,X 0.19654173 0.09486818 0.03244769
R2 t1,Xm 0.31741439 0.39273250 0.44036621
R2 t2,Xm 0.24635207 0.16870403 0.06984592
R2 Y,t1 0.92481714 0.96073283 0.98150053
R2 Y,t2 0.03591568 0.02076770 0.01087043
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F2 OSC-PLS
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F2 Validation
Data: X dimension: 54 191
Y dimension: 54 1
Fit method: kernelpls
Number of components considered: 13
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[1] "R2 Y,X = 0.18532673"
PLS OSC1 OSC2
R2 Yhat,X 0.1854653 0.18540961 0.18538841
R2 Y,Xm 0.2081634 0.27796614 0.33316421
R2 to,X NA 0.19488805 0.17253656
R2 t1,X 0.2419817 0.20145188 0.19307770
R2 t2,X 0.1543582 0.16416238 0.08040183
R2 t1,Xm 0.2712700 0.30044413 0.34878165
R2 t2,Xm 0.1710830 0.21808801 0.15893459
R2 Y,t1 0.8237835 0.93760281 0.96324369
R2 Y,t2 0.1138193 0.02564088 0.01264715
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