alternativeROC (version 1.0.1)

plotROC: Diagnostic performance: Plot ROC curve

Description

Diagnostic performance: Plot ROC curve

Usage

plotROC(
  x,
  annotate = FALSE,
  col.diagonal = "#00000080",
  lty.diagonal = "solid",
  lwd.diagonal = 1,
  col = "#303030",
  lwd = 2,
  cex = 2,
  ppv = NULL,
  npv = NULL,
  prevalence = NULL,
  col.pvs = "#888888",
  col.ci = "#dd0000",
  lwd.ci = lwd,
  len.ci = 0.1,
  boot.n = 1000,
  conf.level = 0.95,
  ...
)

Value

A list with the following elements:

• AUC: A numeric vector of length 3 containing the median, lower bound, and upper bound of the AUC.

• PPV: The positive predictive value cutoff.

• sensitivity@PPV: A numeric vector of length 3 containing the median, lower bound, and upper bound of the sensitivity at the specified PPV.

• NPV: The negative predictive value cutoff.

• specificity@NPV: A numeric vector of length 3 containing the median, lower bound, and upper bound of the specificity at the specified NPV.

Arguments

x

Object of class roc.

annotate

Annotate plot.

col.diagonal

Color of the diagonal.

lty.diagonal

Line type of the diagonal.

lwd.diagonal

Line width of the diagonal.

col

Color.

lwd

Line width.

cex

Size of the symbols.

ppv

Positive predictive value cutoff

npv

Negative predictive value cutoff

prevalence

Prevalence of the positive outcome

col.pvs

Color of the predictive value triangles

col.ci

Color of the positive and negative predictive values.

lwd.ci

Line width for the positive and negative predictive values.

len.ci

Length of the end segment for positive and negative predictive values (see arrows).

boot.n

Number of bootstrap replicates for the computation of the confidence interval of the specificity at NPV and of the sensitivity at PPV.

conf.level

Width of the confidence interval of the specificity at NPV and of the sensitivity at PPV.

...

parameters to be passed to plot.

Examples

set.seed(0)
count.neg <- count.pos <- 200
sd.neg <- sd.pos <- 1
mean.neg <- .5
mean.pos <- 1
skew.neg <- -8
skew.pos <- 0
x <- c(sn::rsn(count.neg,mean.neg,sd.neg,skew.neg),
       sn::rsn(count.pos,mean.pos,sd.pos,skew.pos))
y <- ordered(c(rep("neg",count.neg),rep("pos",count.pos)),levels=c("neg","pos"))
r1 <- roc(y,x)
plotROC(r1,ppv=0.2,prevalence=0.05,boot.n=1e2)
skew.neg <- 0
skew.pos <- 8
x <- c(sn::rsn(count.neg,mean.neg,sd.neg,skew.neg),
       sn::rsn(count.pos,mean.pos,sd.pos,skew.pos))
y <- ordered(c(rep("neg",count.neg),rep("pos",count.pos)),levels=c("neg","pos"))
r2 <- roc(y,x)
plotROC(r2,npv=0.995,prevalence=0.05,boot.n=1e2)