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The Receiver Operating Characteristic (ROC) Curve

The Receiver Operating Characteristic (ROC) Curve. EPP 245/298 Statistical Analysis of Laboratory Data. Binary Classification. Suppose we have two groups for which each case is a member of one or the other, and that we know the correct classification (“truth”).

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The Receiver Operating Characteristic (ROC) Curve

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  1. The Receiver Operating Characteristic (ROC) Curve EPP 245/298 Statistical Analysis of Laboratory Data

  2. Binary Classification • Suppose we have two groups for which each case is a member of one or the other, and that we know the correct classification (“truth”). • Suppose we have a prediction method that produces a single numerical value, and that small values of that number suggest membership in group 1 and large values suggest membership in group 2 EPP 245 Statistical Analysis of Laboratory Data

  3. If we pick a cutpoint t, we can assign any case with a predicted value ≤ t to group 1 and the others to group 2. • For that value of t, we can compute the number correctly assigned to group 2 and the number incorrectly assigned to group 2 (true positives and false positives). • For t small enough, all will be assigned to group 2 and for t large enough all will be assigned to group 1. • The ROC curve is a plot of true positives vs. false positives EPP 245 Statistical Analysis of Laboratory Data

  4. datagen <- function() { truth <- rep(0:1,each=50) pred <- c(rnorm(50,10,1),rnorm(50,12,1)) return(data.frame(truth=truth,pred=pred)) } plot1 <- function() { nz <- sum(truth==0) n <- length(truth) plot(density(pred[1:nz]),lwd=2,xlim=c(6,18), main="Generating an ROC Curve") lines(density(pred[(nz+1):n]),col=2,lwd=2) abline(v=10,col=4,lwd=2) abline(v=11,col=4,lwd=2) abline(v=12,col=4,lwd=2) } ----------------------------------------- > source(“rocsim.r”)> roc.data <- datagen() > attach(roc.data) > plot1() EPP 245 Statistical Analysis of Laboratory Data

  5. EPP 245 Statistical Analysis of Laboratory Data

  6. roc.curve <- function(truth,pred,maxx) { ntp <- sum(truth==1) ntn <- sum(truth==0) n <- length(truth) preds <- sort(unique(pred)) npred <- length(preds) tp <- vector("numeric",npred+1) fp <- tp fp[1] <- ntn tp[1] <- ntp for (i in 1:npred) { cutpt <- preds[i] tp[i+1] <- sum((pred >= cutpt)&(truth==1)) fp[i+1] <- sum((pred >= cutpt)&(truth==0)) } plot(fp,tp, type="l",lwd=2,xlim=c(0,maxx)) title("ROC Curve") }----------------------------------------> roc.curve(truth,pred,50) EPP 245 Statistical Analysis of Laboratory Data

  7. EPP 245 Statistical Analysis of Laboratory Data

  8. datagen2 <- function() { truth <- rep(0:1,c(990,10)) pred <- c(rnorm(990,10,1),rnorm(10,12,1)) return(data.frame(truth=truth,pred=pred)) }--------------------------------------> detach(roc.data) > roc.data2 <- datagen2() > attach(roc.data2)> roc.curve(truth,pred,40) EPP 245 Statistical Analysis of Laboratory Data

  9. ROC Curve for Rare Outcome EPP 245 Statistical Analysis of Laboratory Data

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