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# Preliminary function to use in the TpAUC function
# points.curve function calculates the coordinates (FPR, TPR) of the ROC curve
# Inputs:
# xsample : binary variable with 1 indicating success occurs
# ysample : variable of the diagnostic test results
# Output:
# xy.roc : pairs of points (fpr, tpr) of the ROC curve
#
points.curve <- function(xsample, ysample) {
fpr.p <- NULL; sen.p <- NULL
x.p <- xsample[which(is.na(xsample)==FALSE & is.na(ysample)==FALSE)]
y.p <- ysample[which(is.na(xsample)==FALSE & is.na(ysample)==FALSE)]
pts <- sort(y.p)
pts <- append(pts[-length(pts)]+diff(pts)/2, min(pts)-1, 0)
pts <- append(pts, max(y.p)+1, length(pts))
for (i.pt in 1:length(pts)) {
pt <- pts[i.pt]
pre.p <- (y.p>pt)*1
fpr.p[i.pt] <- sum((pre.p==1)*(x.p==0))/sum(x.p==0)
sen.p[i.pt] <- sum((pre.p==1)*(x.p==1))/sum(x.p==1)
}
if (is.unsorted(sen.p)) {
sen.p <- rev(sen.p)
fpr.p <- rev(fpr.p)
}
xy.roc <- cbind(fpr.p,sen.p)
return(xy.roc)
}
#' Defined function to check points
points.thresholds <- function(xsample, ysample) {
x.p <- xsample[which(is.na(xsample) == FALSE & is.na(ysample) == FALSE)]
y.p <- ysample[which(is.na(xsample) == FALSE & is.na(ysample) == FALSE)]
pts <- sort(y.p)
pts <- append(pts[-length(pts)] + diff(pts) / 2, min(pts) - 1, 0)
pts <- append(pts, max(y.p) + 1, length(pts))
}
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