Nothing
PLSR <- function(Y, X, S=2, InitTransform=5, grouping=NULL, centerY=TRUE, scaleY=TRUE, tolerance=0.000005, maxiter=100, show=FALSE, Validation=NULL, nB=500){
if (is.data.frame(X)) X=as.matrix(X)
ContinuousDataTransform = c("Raw Data", "Substract the global mean", "Double centering",
"Column centering", "Standardize columns", "Row centering",
"Standardize rows", "Divide by the column means and center",
"Normalized residuals from independence", "Divide by the range",
"Within groups standardization", "Ranks")
if (is.numeric(InitTransform))
InitTransform = ContinuousDataTransform[InitTransform]
result=list()
I1=dim(X)[1]
J=dim(X)[2]
if (is.numeric(Y) & !is.matrix(Y)) {Y= matrix(Y, ncol=1)
colnames(Y)="Response"}
if (is.numeric(Y) & !is.matrix(Y)) {Y= matrix(Y, ncol=1)
colnames(Y)="Response"}
I2=dim(Y)[1]
K=dim(Y)[2]
inames=rownames(X)
ynames=colnames(Y)
xnames=colnames(X)
dimnames=paste("Component", 1:S)
result$Method="PLSR"
result$X=X
result$Y=Y
result$centerY=centerY
result$scaleY=scaleY
result$Initial_Transformation=InitTransform
if (!(I1==I2)) stop('The number of rows of both matrices must be the same')
else I=I1
rownames(Y)<-rownames(X)
if (centerY & !scaleY){
Y=TransformIni(Y,transform=4)
}
if (centerY & scaleY){
Y=TransformIni(Y,transform=5)
}
Data = InitialTransform(X, transform = InitTransform, grouping=grouping)
X = Data$X
if (InitTransform=="Within groups standardization") result$Deviations = Data$ColStdDevs
result$ScaledX=X
result$ScaledY=Y
fit=PLSRfit(Y, X, S=S, tolerance=tolerance, maxiter=maxiter, show=show)
rownames(fit$P)=xnames
colnames(fit$P)=dimnames
rownames(fit$Q)=ynames
colnames(fit$Q)=dimnames
rownames(fit$C)=ynames
rownames(fit$T)=inames
colnames(fit$T)=dimnames
rownames(fit$U)=inames
colnames(fit$U)=dimnames
rownames(fit$W)=xnames
colnames(fit$W)=dimnames
rownames(fit$C)=ynames
colnames(fit$C)=dimnames
result$Intercept=fit$c0
result$XScores=fit$T
result$XWeights=fit$W
result$XLoadings=fit$P
result$YScores=fit$U
result$YWeights=fit$C
result$YLoadings=fit$Q
result$RegParameters=fit$W%*% t(fit$C)
t0=matrix(1, I,1)
result$ExpectedY= t0%*%t(fit$c0) + fit$T %*% t(fit$C)
result$R2=diag(t(result$ExpectedY)%*%result$ExpectedY)/diag(t(Y)%*%Y)
result$XStructure=cor(result$X,fit$T)
result$YStructure=cor(result$Y,fit$U)
result$YXStructure=cor(result$Y,fit$T)
if (Validation=="Cross"){
CrossR2=matrix(0,I,1)
PsParameters=matrix(0,J,I)
CrossParameters=matrix(0,J,I)
t0=matrix(1, I-1,1)
for (i in 1:I){
Y1=matrix(result$Y[-i,], ncol=K)
X1=result$X[-i,]
if (centerY & !scaleY){
Y1=TransformIni(Y1,transform=4)
}
if (centerY & scaleY){
Y1=TransformIni(Y1,transform=5)
}
Data = InitialTransform(X1, transform = InitTransform, grouping=grouping)
X1 = Data$X
fit=PLSRfit(Y1, X1, S=S, tolerance=tolerance, maxiter=maxiter, show=show)
ExpectedY= t0%*%t(fit$c0) + fit$T %*% t(fit$C)
CrossR2[i]=diag(t(ExpectedY)%*%ExpectedY)/diag(t(Y1)%*%Y1)
CrossParameters[,i]=fit$W%*% t(fit$C)
PsParameters[,i] = result$RegParameters + (I-1) * ( result$RegParameters -fit$W%*% t(fit$C))
}
rownames(PsParameters)=xnames
rownames(CrossParameters)=xnames
result$Validation=Validation
result$CrossR2=CrossR2
result$CrossParameters=CrossParameters
result$PsParameters = PsParameters
stdErr= apply(PsParameters, 1, sd)/sqrt(I)
Z=result$RegParameters/stdErr
pval=dnorm(Z)
EI=result$RegParameters - 1.96 *stdErr
ES=result$RegParameters + 1.96 *stdErr
result$RegParameters=cbind(result$RegParameters, stdErr, Z , pval, EI, ES)
colnames(result$RegParameters)=c("Beta", "Std. Error", "Z", "p-val", "CI : Lower", "CI : Upper")
}
if (Validation=="Bootstrap"){
CrossR2=matrix(0,nB,1)
CrossParameters=matrix(0,J,nB)
t0=matrix(1, I,1)
for (i in 1:nB){
muestra=sample.int(I, I, replace=TRUE)
Y1=matrix(Y[muestra,], ncol=K)
X1=X[muestra,]
if (centerY & !scaleY){
Y1=TransformIni(Y1,transform=4)
}
if (centerY & scaleY){
Y1=TransformIni(Y1,transform=5)
}
Data = InitialTransform(X1, transform = InitTransform, grouping=grouping)
X1 = Data$X
fit=PLSRfit(Y1, X1, S=S, tolerance=tolerance, maxiter=maxiter, show=show)
ExpectedY= t0%*%t(fit$c0) + fit$T %*% t(fit$C)
CrossR2[i]=diag(t(ExpectedY)%*%ExpectedY)/diag(t(Y1)%*%Y1)
CrossParameters[,i]=fit$W%*% t(fit$C)
}
if (!is.null(Validation)){
rownames(CrossParameters)=xnames
result$Validation=Validation
result$CrossR2=CrossR2
result$CrossParameters=CrossParameters
stdErr= apply(CrossParameters, 1, sd)
Z=result$RegParameters/stdErr
pval=dnorm(Z)
EI=result$RegParameters - 1.96 *stdErr
ES=result$RegParameters + 1.96 *stdErr
result$RegParameters=cbind(result$RegParameters, stdErr, Z , round(pval, digits=5), EI, ES)
colnames(result$RegParameters)=c("Beta", "Std. Error", "Z", "p-val", "CI : Lower", "CI : Upper")
}
}
class(result)="PLSR"
return(result)
}
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