Nothing
compute.threshold.FPF.cROC.kernel <-
function(object, newdata, FPF = 0.5, ci.level = 0.95, parallel = c("no", "multicore", "snow"), ncpus = 1, cl = NULL) {
if(class(object)[1] != "cROC.kernel") {
stop(paste0("This function cannot be used for this object class: ", class(object)[1]))
}
# Newdata
names.cov <- object$covariate
if(!missing(newdata) && !inherits(newdata, "data.frame"))
stop("Newdata must be a data frame")
if(!missing(newdata) && length(names.cov) != 0 && sum(is.na(match(names.cov, names(newdata)))))
stop("Not all needed variables are supplied in newdata")
if(missing(newdata)) {
newdata <- cROCData(object$data, names.cov, object$group)
} else {
newdata <- as.data.frame(newdata)
newdata <- na.omit(newdata[,names.cov,drop = FALSE])
}
xp <- newdata[,names.cov]
res.aux <- compute.threshold.FPF.kernel(object = object$fit$h, newdata = xp, FPF = FPF)
# Organised results as desired
thresholds <- vector("list", length(FPF))
names(thresholds) <- FPF
for(i in 1:length(FPF)){
thresholds[[i]] <- matrix(res.aux$thresholds[i,], ncol = 1)
colnames(thresholds[[i]]) <- "est"
}
res <- list()
res$thresholds <- thresholds
res$FPF <- FPF
# Compute associated TPF
fit.mean.new <- npreg(object$fit$d$bw.mean, exdat = xp, residuals = TRUE)
fit.var.new <- npreg(object$fit$d$bw.var, exdat = xp, residuals = TRUE)
d.residuals <- object$fit$d$fit.mean$resid/sqrt(object$fit$d$fit.var$mean)
aux <- t(t(res.aux$thresholds) - fit.mean.new$mean)
aux <- t(t(aux)/sqrt(fit.var.new$mean))
TPF.aux <- matrix(1 - ecdf(d.residuals)(aux), nrow = length(FPF))
# Organised results as desired
TPF <- vector("list", length(FPF))
names(TPF) <- FPF
for(i in 1:length(FPF)){
TPF[[i]] <- matrix(TPF.aux[i,], ncol = 1)
colnames(TPF[[i]]) <- "est"
}
res$TPF <- TPF
res$newdata <- newdata
res
}
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