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function.MinValueNPV <-
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 (control$valueNPV < 0 || control$valueNPV > 1) {
stop("You have entered an invalid minimum value for Negative Predictive Value. \n The minimum value for Negative Predictive Value must be between 0 and 1.", call. = FALSE)
}
if (control$valueNPV == 0) {
warning ("You have entered the minimum possible value for Negative Predictive \n Value. All the cutpoints fulfill the condition. Please check this value.", call. = FALSE, immediate. = TRUE)
}
if (control$valueNPV == 1) {
warning ("You have entered the maximum possible value for Negative Predictive \n Value. Please check this value.", call. = FALSE, immediate. = TRUE)
}
index.cutpointsNPV <- which(measures.acc$NPV[,1] >= control$valueNPV)
if (length(index.cutpointsNPV) == 0) {
warning("There is no cutoff that fulfills this condition. Please introduce another value, if desired.", call. = FALSE, immediate. = TRUE)
cMinValueNPV <- NULL
}
if (length(index.cutpointsNPV)!= 0) {
cutpointsNPV <- measures.acc$cutoffs[index.cutpointsNPV]
if (length(index.cutpointsNPV) == 1) {
cMinValueNPV <- cutpointsNPV
}
if (length(index.cutpointsNPV)> 1) {
PPVnew <- obtain.optimal.measures(cutpointsNPV, measures.acc)$PPV
cutpointsPPVnew <- cutpointsNPV[which(round(PPVnew[,1],10) == round(max(PPVnew[,1], na.rm = TRUE),10))]
if (length(cutpointsPPVnew)> 1) {
NPVnew <- obtain.optimal.measures(cutpointsPPVnew, measures.acc)$NPV
cMinValueNPV <- cutpointsPPVnew[which(round(NPVnew[,1],10) == round(max(NPVnew[,1], na.rm = TRUE),10))]
}
if (length(cutpointsPPVnew) == 1) {
cMinValueNPV <- cutpointsPPVnew
}
}
}
optimal.cutoff <- obtain.optimal.measures(cMinValueNPV, measures.acc)
res <- list(measures.acc = measures.acc, optimal.cutoff = optimal.cutoff)
res
}
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