koplsModel {kopls} | R Documentation |
Function for training a K-OPLS model. The function constructs a predictive regression
model for predicting the values of Y
by using the information in K
.
The explained variation is separated into predictive components,
which dimensionality is determined by the parameter A
, and Y
-orthogonal components;
dimensionality determined by the parameter nox
.
koplsModel(K, Y, A, nox, preProcK = "mc", preProcY = "mc")
K |
Kernel matrix (un-centered); K = <phi(Xtr),phi(Xtr)> |
Y |
Response matrix (un-centered/scaled). |
A |
Number of predictive components. |
nox |
Number of Y -orthogonal components. |
preProcK |
Pre-processing parameters for the K matrix: 'mc' for mean-centering, 'no' for no centering. |
preProcY |
Pre-processing parameters for the Y matrix: 'mc' for mean-centering, 'uv' for mc + scaling to unit variance, 'pa' for mc + Pareto, 'no' for no scaling. |
List with the following components:
Cp |
Y loading matrix. |
Sp |
Sigma matrix, containing singular values from Y '*K *Y used for scaling. |
Sps |
Sp^(-1/2). |
Up |
Y score matrix. |
Tp |
Predictive score matrix for all Y -orthogonal components. |
T |
Predictive score matrix for the final Y -orthogonal component model. |
co |
Y -orthogonal loading vectors. |
so |
Eigenvalues from estimation of Y -orthogonal loading vectors. |
To |
Y -orthogonal score matrix. |
toNorm |
Norm of the Y -orthogonal score matrix prior to scaling. |
Bt |
T-U regression coefficients for predictions. |
A |
Number of predictive components. |
nox |
Number of Y -orthogonal components. |
K |
The kernel matrix. |
EEprime |
The deflated kernel matrix for residual statistics. |
sstot_K |
Total sums of squares in K |
R2X |
Cumulative explained variation for all model components. |
R2XO |
Cumulative explained variation for Y -orthogonal model components. |
R2XC |
Explained variation for predictive model components after addition of Y -orthogonal model components. |
sstot_Y |
Total sums of squares in Y . |
R2Y |
Explained variation of Y . |
preProc |
Pre-processing parameters:
K = preProcK .Y = preProcY .Y . |
Max Bylesjo and Mattias Rantalainen
Rantalainen M, Bylesjo M, Cloarec O, Nicholson JK, Holmes E and Trygg J. Kernel-based orthogonal projections to latent structures (K-OPLS), J Chemometrics 2007; 21:376-385. doi:10.1002/cem.1071.
## Load data set data(koplsExample) ## Define kernel function parameter sigma<-25 ## Define number of Y-orthogonal components nox<-3 ## Construct kernel Ktr<-koplsKernel(Xtr,NULL,'g',sigma) ## Model model<-koplsModel(Ktr,Ytr,1,nox,'mc','mc'); ## Visualize results koplsPlotModelDiagnostics(model) title("Model diagnostics without cross-validation")