| roc.curve {clinfun} | R Documentation |
Empirical ROC curve
Description
Computes the empricial 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.area.test output from this function. |
PRC |
flag to tell whether ROC or Precision-Recall curve plotted. |
... |
optional arguments to the print, plot and lines functions. |
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.
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.
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)