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