varoc {varoc}

VAROC: value added receiver operating characteristics (ROC) curve

Description

ROC curve to visualize classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models.

Usage

varoc(fit,
mzr,mzr.min=NULL,mzr.max=NULL,
main="VAROC",ylab="True positive fraction",xlab="False positive fraction",
col=c("#9932cc","#87ceeb","#ffe135","#f56642"),
legend="right",lwd=1,
cex.main=1,cex.axis=1,cex.lab=1,cex.legend=1,digits=2)

Arguments

fit

fitted results from the amd() function in the varoc R package.

mzr

mzr="AMD" (or "zAMD") if VAROC curve adds AMD (or zAMD, i.e. normalized AMD or test statistics). Note that mzr="zAMD" works only when pval="yes" was used for the amd() function.

mzr.min

minimum value of AMD (or ZAMD) that is displayed on the plot.

mzr.max

maximum value of AMD (or ZAMD) that is displayed on the plot.

main

title for the plot

ylab

title for the y axis.

xlab

title for the x axis.

col

color that separates AMD on the plot. Default: c("#9932cc","#87ceeb","#ffe135","#f56642")

legend

legend location, "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center".

lwd

line width

cex.main

main size.

cex.axis

axis size.

cex.lab

label size.

cex.legend

legend size.

digits

number of decimals.

Details

The varoc function plot true positive fraction(c) (or sensitivity(c)) versus false positive fraction(c) (or one minus specificty(c)) at each threshold c colored by above mean difference(c). See the amd fuction for more details.

Value

No return value, called for side effects.

Author(s)

Yunro Chung [aut, cre]

References

Danielle Brister and Yunro Chung, Value added receiver operating characteristics curve (in-progress)

Examples

set.seed(1)

n1=50
n0=50

#1. marker 1 (useless biomaker)
y1=c(rep(1,n1),rep(0,n0))
x1=abs(c(rnorm(n1,0,1),rnorm(n0,0,1)))

#1.1.amd
fit1=amd(y=y1,x=x1,fpf=0.3)
print(fit1)

#1.2. varoc
varoc(fit1)

#1.3. jdp
jdp(fit1)

#2. marker 2 (useful biomarker)
y2=y1
x2=abs(c(rnorm(n1,1,1),rnorm(n0,0,1)))

#2.1. amd
fit2=amd(y=y2,x=x2,fpf=0.3)

#2.2. varoc for marker 1 vs marker 2
mzr.min=min(c(fit1$res$amd,fit2$res$amd))
mzr.max=max(c(fit1$res$amd,fit2$res$amd))

varoc(fit1,mzr="AMD",mzr.min=mzr.min,mzr.max=mzr.max)
varoc(fit2,mzr="AMD",mzr.min=mzr.min,mzr.max=mzr.max)

#2.3. varoc for marker 1 vs marker 2
min=min(c(x1,x2))
max=max(c(x1,x2))
jdp(fit1,min=min,max=max)
jdp(fit2,min=min,max=max)