koplsPredict {kopls} | R Documentation |
Performs prediction of new samples from an existing K-OPLS model
(see koplsModel
).
The function projects the Y-predictive and Y-orthogonal scores components
to predict a value of the response matrix Y.
The dimensionality of the parameters is determined from model
.
koplsPredict(KteTr, Ktest, Ktrain, model, nox = NA, rescaleY = FALSE)
KteTr |
The hybrid test/training kernel matrix; KteTr = <phi(Xte),phi(Xtr)>. |
Ktest |
The test kernel matrix; Ktest = <phi(Xte),phi(Xte)>. |
Ktrain |
The training kernel matrix (same as used in model training); Ktrain = <phi(Xtr),phi(Xtr)> |
model |
The trained K-OPLS model (see koplsModel ). |
nox |
Number of Y-orthogonal score vectors. If undefined, the value used in model will be employed. |
rescaleY |
If true, the estimated Yhat values will be rescaled according to the scaling parameters in model . Otherwise Yhat values will be returned as is (default). |
Tp |
Predicted predictive score matrix for all generations 0:nox of Y-orthogonal vectors. |
T |
Predictive score matrix for the final model with nox Y-orthogonal vectors. |
to |
Predicted Y-orthogonal score vectors. |
EEprime |
Calculated residuals for the test kernel matrix Ktest , useful e.g. for residual statistics. |
Yhat |
Predicted values of the response matrix. |
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 kernels Ktr<-koplsKernel(Xtr,NULL,'g',sigma) KteTr<-koplsKernel(Xte,Xtr,'g',sigma) KteTe<-koplsKernel(Xte,NULL,'g',sigma) ## Model model<-koplsModel(Ktr,Ytr,1,nox,'mc','mc'); ## Predict modelPred<-koplsPredict(KteTr,KteTe,Ktr,model,rescaleY=TRUE) ## Visualize plot(modelPred$Yhat, Yte, xlab="Predicted", ylab="Observed") abline(v=0.5, col="Red", lty=2) ## Approximate decision boundary