Plot confidence intervals
This function adds confidence intervals to a ROC curve plot, either as bars or as a confidence shape.
## S3 method for class 'ci.thresholds'
plot(x, length=.01*ifelse(attr(x,
"roc")$percent, 100, 1), col=par("fg"), ...)
## S3 method for class 'ci.sp'
plot(x, type=c("bars", "shape"), length=.01*ifelse(attr(x,
"roc")$percent, 100, 1), col=ifelse(type=="bars", par("fg"),
"gainsboro"), no.roc=FALSE, ...)
## S3 method for class 'ci.se'
plot(x, type=c("bars", "shape"), length=.01*ifelse(attr(x,
"roc")$percent, 100, 1), col=ifelse(type=="bars", par("fg"),
"gainsboro"), no.roc=FALSE, ...)x |
a confidence interval object from the functions
|
type |
type of plot, “bars” or “shape”. Can be
shortened to “b” or “s”. “shape” is only available for
|
length |
the length (as plot coordinates) of the bar ticks. Only
if |
no.roc |
if |
col |
color of the bars or shape. |
... |
further arguments for |
This function adds confidence intervals to a ROC curve plot, either as
bars or as a confidence shape, depending on the state of the
type argument. The shape is plotted over the ROC curve, so that
the curve is re-plotted unless no.roc=TRUE.
Graphical functions are called with suppressWarnings.
This function returns the confidence interval object invisibly.
With type="shape", the warning “Low definition shape” is
issued when the shape is defined by less than 15 confidence
intervals. In such a case, the shape is not well defined and the ROC
curve could pass outside the shape. To get a better shape, increase
the number of intervals, for example with:
plot(ci.sp(rocobj, sensitivities=seq(0, 1, .01)), type="shape")
Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an open-source package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: doi: 10.1186/1471-2105-12-77.
data(aSAH)
## Not run:
# Start a ROC plot
rocobj <- plot.roc(aSAH$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")
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