Above mentioned formula is wrong - maybe a typo http://en.wikipedia.org/wiki/Receiver_operating_characteristic
The false positive rate is the rate of false positives, meaning how many of the total negatives (all in reality negatives(N), that is, all negatives falsely classified as positives(fp) and all negatives correctly classified as negatives(tn)) have been falsely classified as positive. Also the authors obviously had (N+P=number of features), and therefore at least could have computed this properly. For example: N+P=100 P=TP+FN N=FP+TN -> do the math with what you got On 17.08.2012, at 11:13, vjyns wrote: > Hi, > > thanks for the quick response, but as i said in my case due to two > different threshold the detected features will differ. Moreover, there is > some standard /refined/ formula in calculating the tpr and fpr. herewith i > had attached the refined formula from a standard international journal > http://r.789695.n4.nabble.com/file/n4640577/tpr_and_fpr.jpg > > when i used the above mentioned formula (fpr=fp/fp+tp) then i can able to > see my point are distributed on the extreme left corner. Like this it is > possible to put all the 6 images. Will you please suggest me now how to > obtain the plot for different images of two threshold. > > > > -- > View this message in context: > http://r.789695.n4.nabble.com/no-true-negative-data-need-roc-curve-tp4640474p4640577.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
