roc: ROC and AUC

In aucm: AUC Maximization

Description

ROC/AUC methods. fast.auc calculates the AUC using a sort operation, instead of summing over pairwise differences in R.
computeRoc computes an ROC curve.
plotRoc plots an ROC curve.
addRoc adds an ROC curve to a plot.
classification.error computes classification error

Usage

fast.auc (score, outcome, t0 = 0, t1 = 1, reverse.sign.if.nece = TRUE, quiet = FALSE)
computeRoc (score, outcome, reverse.sign.if.nece = TRUE, cutpoints
         = NULL)
plotRoc(x, add = FALSE, type = "l", diag.line=TRUE,...)
addRoc (x,...)
classification.error(score, outcome, threshold=NULL, verbose=FALSE)

Arguments

score	a vector. Linear combination or score.
outcome	a vector of 0 and 1. Outcome.
t0	a number between 0 and 1 that is the lower boundary of pAUC
t1	a number between 0 and 1 that is the upper boundary of pAUC
reverse.sign.if.nece	a boolean. If TRUE, score is multiplied by -1 if AUC is less than 0.5.
x	a list of two elements: sensitivity and specificity.
diag.line	boolean. If TRUE, a diagonal line is plotted
add	boolean. If TRUE, add to existing plot. If FALSE, create a new plot.
quiet	boolean
cutpoints	cutpoints
threshold	threshold
verbose	boolean
type	line type for lines
...	arguments passed to plot or lines

Details

These functions originally come from Thomas Lumley and Tianxi Cai et al.

Value

computeRoc returns a list of sensitivity and specificity.
plotRoc and addRoc plots ROC curves.

Author(s)

Shuxin Yin
Youyi Fong [e-mail]
Krisztian Sebestyen

Examples

n=1e2
score=c(rnorm(n/2,1), rnorm(n/2,0))
outcome=rep(1:0, each=n/2)
# cannot print due to r cmd check
#plotRoc(computeRoc(score, outcome))

# commented out b/c slower on pc and cause note when r cmd check
## test, fast.auc2 is a version without all the checking
#score=rnorm(1e5)
#outcome=rbinom(1e5,1,.5)
#system.time(for (i in 1:1e2) fast.auc(score,outcome)) # 4.9 sec
#system.time(for (i in 1:1e2) fast.auc2(score,outcome)) # 3.8 sec

aucm documentation built on Jan. 11, 2020, 9:43 a.m.