Nothing
PLSRBin <- function(Y, X, S=2, InitTransform=5, grouping=NULL, tolerance=0.00005,
maxiter=100, show=FALSE, penalization=0.1, cte =TRUE,
OptimMethod="CG", Multiple=FALSE){
if (is.data.frame(X)) X=as.matrix(X)
if (is.data.frame(Y)) X=as.matrix(Y)
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]
I2=dim(Y)[1]
K=1
inames=rownames(X)
ynames=colnames(Y)
xnames=colnames(X)
dimnames=paste("Comp.", 1:S)
result$Method="PLSR for binary responses"
result$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$IncludeConst=cte
myfit=PLSRBinFit(Y=Y, X=X, S=S, tolerance=tolerance, maxiter=maxiter, show=show, penalization=penalization, cte =cte)
rownames(myfit$TT)=inames
colnames(myfit$TT)=dimnames
C=matrix(0, K, S)
rownames(myfit$B)=xnames
colnames(myfit$B)=ynames
rownames(myfit$P)=xnames
colnames(myfit$P)=dimnames
result$XScores=myfit$TT
result$XLoadings=myfit$P
result$YScores=myfit$U
result$YLoadings=myfit$Q
rownames(result$YLoadings)=ynames
colnames(result$YLoadings)=paste("Dim", 1:S)
result$Coefficients=myfit$B
result$XStructure=cor(result$X,myfit$TT)
result$BinaryFits=myfit$fit
result$Intercepts=myfit$q0
result$LinTerm=myfit$Linterm
result$Expected=myfit$Expected
result$Predictions=myfit$Predictions
rownames(result$Predictions)=inames
colnames(result$Predictions)=ynames
result$PercentCorrect=myfit$PercentCorrect
result$PercentCorrectCols=myfit$PercentCorrectCols
maxima=rep(0,I2)
for (i in 1:I2)
maxima[i]=which(result$Expected[i,]==max(result$Expected[i,]))
result$Maxima=maxima
class(result)="PLSRBin"
return(result)
}
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