functionals of ROC curve

Description

various functionals of ROC (Receiver Operating Characteristic) curves

Usage

AUC(rocobj)
AUCi(rocobj)
pAUC(rocobj,t0)
pAUCi(rocobj,t0)

Arguments

Details

AUC, pAUC, AUCi and pAUCi compute the Area Under the Curve.

AUC and pAUC employ the trapezoidal rule. AUCi and pAUCi use integrate().

AUC and AUCi compute the area under the curve from 0 to 1 on the x-axis (i.e., the 1 - specificity axis).

pAUC and pAUCi compute the are under the curve from 0 to argument t0 on the x-axis (i.e., the 1 - specificity axis).

Elements of class rocc can be created by rocdemo.sca() or other constructors you might make using the code of rocdemo.sca() as a template.

Author(s)

Vince Carey (stvjc@channing.harvard.edu)

References

Rosner, B., 2000, Fundamentals of Biostatistics, 5th Ed., pp. 63–65

Duda, R. O., Hart, P. E., Stork, D. G., 2001 Pattern Classification, 2nd Ed., p. 49

See Also

rocdemo.sca

Examples

set.seed(123)
R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), dxrule.sca,
   caseLabel="new case", markerLabel="demo Marker" )
print(AUC(R1))
print(pAUC(R1,.3))
print(pAUCi(R1,.3))
print(ROC(R1,.3))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ROC)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ROC/AUC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: AUC
> ### Title: functionals of ROC curve
> ### Aliases: AUC AUCi pAUC pAUCi ROC
> ### Keywords: models
> 
> ### ** Examples
> 
> set.seed(123)
> R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), dxrule.sca,
+    caseLabel="new case", markerLabel="demo Marker" )
NA in cutpts forces recomputation using smallest gap
> print(AUC(R1))
[1] 0.456044
> print(pAUC(R1,.3))
[1] 0.04010989
> print(pAUCi(R1,.3))
[1] 0.04236287
> print(ROC(R1,.3))
[1] 0.2
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>