data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_v1_wvc(dataY=yCornell,dataX=XCornell,nt=3,dataPredictY=XCornell[1,])
PLS_v1_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),])
PLS_v1_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],keepcoeffs=TRUE)
rm("XCornell","yCornell")
## With an incomplete dataset (X[1,2] is NA)
data(pine)
ypine <- pine[,11]
data(XpineNAX21)
PLS_v1_wvc(dataY=log(ypine)[-1],dataX=XpineNAX21[-1,],nt=3)
PLS_v1_wvc(dataY=log(ypine)[-1],dataX=XpineNAX21[-1,],nt=3,dataPredictY=XpineNAX21[1,])
PLS_v1_wvc(dataY=log(ypine)[-2],dataX=XpineNAX21[-2,],nt=3,dataPredictY=XpineNAX21[2,])
PLS_v1_wvc(dataY=log(ypine),dataX=XpineNAX21,nt=3)
rm("XpineNAX21","ypine")
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