Empirical ROC curve

Computes the empirical ROC curve for a diagnostic tool.

Usage

roc.curve(marker, status, method=c("empirical"))
  # S3 method for class 'roc.curve'
print(x, ...)
  # S3 method for class 'roc.curve'
plot(x, PRC=FALSE, ...)
  # S3 method for class 'roc.curve'
lines(x, PRC=FALSE, ...)

Arguments

marker

the marker values for each subject.

status

binary disease status indicator

method

the method for estimating the ROC curve. Currently only the empirical curve is implemented.

x

object of class roc.curve output from this function.

PRC

flag to tell whether ROC or Precision-Recall curve plotted.

...

optional arguments to the print, plot and lines functions.

Value

a list with the following elements

marker

the diagnostic marker being studied.

status

binary disease

tpr

true positive rates for all thresholds.

fpr

true positive rates for all thresholds.

ppv

positive predictive values for all thresholds.

npv

negative predictive values for all thresholds.

The "print" method returns the nonparametric AUC and its s.e.

The "plot" and "lines" methods can be used to draw a new plot and add to an existing plot of ROC curve.

Details

The computation is based on assuming that larger values of the marker is indicative of the disease. So for a given threshold x0, TPR is P(marker >= x0|status =1) and FPR is P(marker >= x0|status =0). This function computes the empirical estimates of TPR and FPR.

Examples

g <- rep(0:1, 50)
x <- rnorm(100) + g
y <- rnorm(100) + 1.5*g
o <- roc.curve(x, g)
plot(o)
lines(roc.curve(y, g), col=2)