pROC (version 1.19.1)

plot.ci: Plot confidence intervals

Description

This function adds confidence intervals to a ROC curve plot, either as bars or as a confidence shape.

Usage

# S3 method for ci.thresholds
plot(x, length=.01*ifelse(attr(x,
  "roc")$percent, 100, 1), col=par("fg"), ...)
# S3 method for ci.sp
plot(x, type=c("bars", "shape"), length=.01*ifelse(attr(x,
"roc")$percent, 100, 1), col=ifelse(type=="bars", par("fg"),
"gainsboro"), no.roc=FALSE, ...)
# S3 method for ci.se
plot(x, type=c("bars", "shape"), length=.01*ifelse(attr(x,
"roc")$percent, 100, 1), col=ifelse(type=="bars", par("fg"),
"gainsboro"), no.roc=FALSE, ...)
# S3 method for ci.coords
plot(x, type=c("bars", "shape"), length=NULL,
col=ifelse(type=="bars", par("fg"), "gainsboro"), ...)

Value

This function returns the confidence interval object invisibly.

Details

This function adds confidence intervals to a ROC curve plot, either as bars or as a confidence shape, depending on the state of the type argument. The shape is plotted over the ROC curve, so that the curve is re-plotted unless no.roc=TRUE.

Graphical functions are called with suppressWarnings.

References

Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) ``pROC: an open-source package for R and S+ to analyze and compare ROC curves''. BMC Bioinformatics, 7, 77. DOI: tools:::Rd_expr_doi("10.1186/1471-2105-12-77").

See Also

plot.roc, ci.thresholds, ci.sp, ci.se

Examples

data(aSAH)
if (FALSE) {
# Start a ROC plot
rocobj <- plot.roc(aSAH$outcome, aSAH$s100b)
plot(rocobj)
# Thresholds
ci.thresolds.obj <- ci.thresholds(rocobj)
plot(ci.thresolds.obj)
# Specificities
plot(rocobj) # restart a new plot
ci.sp.obj <- ci.sp(rocobj, boot.n=500)
plot(ci.sp.obj)
# Sensitivities
plot(rocobj) # restart a new plot
ci.se.obj <- ci(rocobj, of="se", boot.n=500)
plot(ci.se.obj)

# Plotting a shape. We need more
ci.sp.obj <- ci.sp(rocobj, sensitivities=seq(0, 1, .01), boot.n=100)
plot(rocobj) # restart a new plot
plot(ci.sp.obj, type="shape", col="blue")

# Direct syntax (response, predictor):
plot.roc(aSAH$outcome, aSAH$s100b,
         ci=TRUE, of="thresholds")

# CI of a PR curve
co <- coords(rocobj, x = "all", input="recall", ret=c("recall", "precision"))
ci <- ci.coords(rocobj, x = seq(0, 1, .1), input="recall", ret="precision")
plot(co, type="l", ylim = c(0, 1))
plot(ci, type="shape")
plot(ci, type="bars")
lines(co)
}