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function.MaxEfficiency <-
function(data, marker, status, tag.healthy = 0, direction = c("<", ">"), control = control.cutpoints(), pop.prev, ci.fit = FALSE, conf.level = 0.95, measures.acc){
direction <- match.arg(direction)
if (is.logical(control$costs.benefits.Efficiency) == FALSE) {
stop("'costs.benefits.Efficiency' must be a logical-type argument.", call. = FALSE)
}
if (is.logical(control$standard.deviation.accuracy) == FALSE) {
stop("'standard.deviation.accuracy' must be a logical-type argument.", call. = FALSE)
}
Efficiency <- pop.prev*measures.acc$Se[,1]+(1-pop.prev)*measures.acc$Sp[,1]
if (control$costs.benefits.Efficiency == FALSE) {
cMaxEfficiency <- measures.acc$cutoffs[which(round(Efficiency,10) == round(max(Efficiency,na.rm=TRUE),10))]
}
if (control$costs.benefits.Efficiency == TRUE) {
control$costs.ratio <- 1
cMaxEfficiency <- function.CB(data, marker, status, tag.healthy = 0, direction = c("<", ">"), control = control, pop.prev, ci.fit, conf.level, measures.acc)$optimal.cutoff$cutoff
}
optimal.Efficiency <- max(Efficiency,na.rm=TRUE)
optimal.cutoff <- obtain.optimal.measures(cMaxEfficiency, measures.acc)
# Standard deviation associated with accuracy or efficiency at the optimal cutpoint is computed:
if (control$standard.deviation.accuracy == TRUE) {
optimal.Efficiency.sd <- ((optimal.Efficiency * (1 - optimal.Efficiency))/(measures.acc$n$d+measures.acc$n$h - 1))^0.5
}
if (control$standard.deviation.accuracy == FALSE) {
res <- list(measures.acc = measures.acc, optimal.cutoff = optimal.cutoff, criterion = Efficiency, optimal.criterion = optimal.Efficiency)
}
if (control$standard.deviation.accuracy == TRUE) {
res <- list(measures.acc = measures.acc, optimal.cutoff = optimal.cutoff, criterion = Efficiency, optimal.criterion = optimal.Efficiency, sd.maximum.Efficiency = optimal.Efficiency.sd)
}
res
}
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