Nothing
cvpvs.logreg <- function(X,Y,tau.o=10, find.tau=FALSE, delta=2, tau.max=80, tau.min=1,
pen.method=c("vectors","simple","none"),progress=TRUE)
{
pen.method <- match.arg(pen.method)
X <- as.matrix(X)
n <- dim(X)[1]
d <- dim(X)[2]
Y <- factor(Y)
Y <- unclass(Y)
L <- max(Y)
Ylevels <- levels(Y)
# Stop if lengths of X[,1] and Y do not match
if(length(Y) != length(X[,1])) {
stop('length(Y) != length(X[,1])')
}
if (progress)
{
print('Computation of cross-validated p-values',
quote=FALSE)
print(paste('for ',as.character(n),
' training observations.'),
quote=FALSE)
print('Preliminary log. regression:',
quote=FALSE)
}
tmp <- penlogreg(X,Y,tau.o,
pen.method=pen.method,progress=progress)
a0 <- tmp$a
b0 <- tmp$b
PV <- matrix(1,nrow=n,ncol=L)
tau.opt <- PV
for (i in 1:n)
{
if (progress)
{
print(paste('Observation no. ',as.character(i),' ...'),
quote=FALSE)
}
NewX <- X[i,]
Xr <- X[(1:n)!=i,]
Yr <- Y[(1:n)!=i]
tmp <- pvs.logreg(NewX,Xr,Yr,tau.o=tau.o,
find.tau=find.tau, delta=delta,
tau.max=tau.max, tau.min=tau.min,
pen.method=pen.method,a0=a0,b0=b0)
PV[i,] <- tmp
if(find.tau==TRUE)
{
tau.opt[i,] <- attributes(tmp)$tau.opt
}
}
if(find.tau==TRUE)
{
attributes(PV)$tau.opt <- tau.opt
}
dimnames(PV)[[2]] <- Ylevels
return(PV)
}
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