Nothing
PLSR1Bin <- function(Y, X, S=2, InitTransform=5, grouping=NULL, tolerance=0.000005,
maxiter=100, show=FALSE, penalization=0, cte =TRUE, Algorithm=1,
OptimMethod="CG"){
if (is.data.frame(X)) X=as.matrix(X)
if (!CheckBinaryVector(Y)) stop("The response must be binary (0 or 1)")
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)) {Y= as.matrix(Y, I1,1)
rownames(Y)<-rownames(X)
colnames(Y)="Response"}
I2=dim(Y)[1]
K=1
inames=rownames(X)
ynames=colnames(Y)
xnames=colnames(X)
dimnames=paste("Comp.", 1:S)
result$Method="PLSR1 Binary"
result$X=X
result$Y=Y
result$Initial_Transformation=InitTransform
if (!(I1==I2)) stop('The number of rows of both matrices must be the same')
else I=I1
Data = InitialTransform(X, transform = InitTransform, grouping=grouping)
X = Data$X
if (InitTransform=="Within groups standardization") result$Deviations = Data$ColStdDevs
result$ScaledX=X
result$ScaledY=Y
result$tolerance=tolerance
result$maxiter=maxiter
result$penalization=penalization
result$IncludeConst=cte
myfit=PLSR1BinFit(Y, X, S=S, tolerance=tolerance, maxiter=maxiter, show=show, penalization=penalization, cte =cte, Algorithm=Algorithm)
rownames(myfit$T)=inames
colnames(myfit$T)=dimnames
rownames(myfit$W)=xnames
colnames(myfit$W)=dimnames
C=matrix(0, K, S)
rownames(myfit$C)=ynames
colnames(myfit$C)=dimnames
rownames(myfit$P)=xnames
colnames(myfit$P)=dimnames
result$XScores=myfit$T
result$XWeights=myfit$W
result$XLoadings=myfit$P
result$YWeights=myfit$beta
result$XStructure=cor(result$X,myfit$T)
result$BinaryFit=myfit$fit
class(result)="PLSR1Bin"
return(result)
}
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