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#' @title Points of the ROC curve
#' @description It calculates the coordinates (fpr, sen) of the ROC curve.
#' This function sorts the scores of a model test and generates
#' the points which will be used to plot its the ROC curve
#' @param x It is the vector of the status (gold standar)
#' @param y It is the vector with the values of a predictor
#' variable or clasificator
#' @return return a matrix with the points of 1-specificity and
#' sensibility that will be used to generate a ROC curve
#' @export pointsCurve
#' @examples
#'library(fission)
#'data("fission")
#'strain <- fission@colData@listData$strain
#'pointsCurve<- pointsCurve(strain, t(assay(fission))[,"SPNCRNA.1080"])
##1st col<-x<-gold standard, 2nd col<-y<-sample
pointsCurve<-function(x, y){
stopifnot(is.numeric(y)||is.integer(y))
xsample <- NULL; ysample <- NULL
xsample <- cbind(x[which(is.na(x)==FALSE & is.na(y)==FALSE)])
ysample <- y[which(is.na(x)==FALSE & is.na(y)==FALSE)]
points<-NULL; pre.point<-NULL; fpr.point<-NULL; sen.point<-NULL; xy<-NULL
points<-sort(ysample)
points<-append(points[-length(points)]+diff(points)/2, min(points)-1, 0)
points<-append(points, max(ysample)+1, length(points))
for (point in points) {
pre.point<-(ysample>point)*1
fpr.point[which(points == point)]<-sum((pre.point == 1)*(xsample == 1))/sum(xsample == 1)
sen.point[which(points == point)]<-sum((pre.point == 1)*(xsample == 2))/sum(xsample == 2)
}
if (is.unsorted(sen.point)) {
sen.point<-rev(sen.point)
fpr.point<-rev(fpr.point)}
xy<-cbind(fpr.point, sen.point)
return(xy)
}
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