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## __________________________________________________________
##
## FUNCTION ROCcurve
##
## Given a score matrix and a validation matrix, this function
## allows to compute the corresponding ROC curve by returning a list
## with the respective x and y coordinates of the ROC curve.
##
## The score matrix has been computed from a network inference algorithm
## (e.g. DBNScoreStep1 or DBNScoreStep2, Shrinkage, Lasso, ...).
## __________________________________________________________
##
ROCcurve <- function(score,validMat,dec=FALSE){
## Initializing...
r <- dim(score)[1] # nb of target genes
d <- dim(score)[2] # nb of predictor genes
## TestEdges is a boolean vector that contains
## the comparison between inferred and real edges
ScoreIdx <- order(score,decreasing=dec)
TrueEdges <- which((abs(validMat)>0))
TestEdges <- ScoreIdx %in% TrueEdges
## Building the ROC curve...
rocx<-matrix(0,length(ScoreIdx)+1,1) # x coord of the roc curve
rocy<-matrix(0,length(ScoreIdx)+1,1) # y coord of the roc curve
for (i in 1:length(ScoreIdx)){
if(TestEdges[i]==TRUE){
rocx[i+1]<-rocx[i]
rocy[i+1]<-rocy[i]+1}
else{
rocx[i+1]<-rocx[i]+1
rocy[i+1]<-rocy[i]
}
}
return(list(x=rocx,y=rocy))
}
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