roc function.
"plot"(x, ...)
"plot"(x, ...)
"plot.roc"(x, add=FALSE, reuse.auc=TRUE,
axes=TRUE, legacy.axes=FALSE,
# Generic arguments for par:
xlim=if(x$percent){c(100, 0)} else{c(1, 0)},
ylim=if(x$percent){c(0, 100)} else{c(0, 1)},
xlab=ifelse(x$percent, ifelse(legacy.axes, "100 - Specificity (%)", "Specificity (%)"), ifelse(legacy.axes, "1 - Specificity", "Specificity")),
ylab=ifelse(x$percent, "Sensitivity (%)", "Sensitivity"),
asp=1,
mar=c(4, 4, 2, 2)+.1,
mgp=c(2.5, 1, 0),
# col, lty and lwd for the ROC line only
col=par("col"),
lty=par("lty"),
lwd=2,
type="l",
# Identity line
identity=!add,
identity.col="darkgrey",
identity.lty=1,
identity.lwd=1,
# Print the thresholds on the plot
print.thres=FALSE,
print.thres.pch=20,
print.thres.adj=c(-.05,1.25),
print.thres.col="black",
print.thres.pattern=ifelse(x$percent, "%.1f (%.1f%%, %.1f%%)", "%.3f (%.3f, %.3f)"),
print.thres.cex=par("cex"),
print.thres.pattern.cex=print.thres.cex,
print.thres.best.method=NULL,
print.thres.best.weights=c(1, 0.5),
# Print the AUC on the plot
print.auc=FALSE,
print.auc.pattern=NULL,
print.auc.x=ifelse(x$percent, 50, .5),
print.auc.y=ifelse(x$percent, 50, .5),
print.auc.adj=c(0,1),
print.auc.col=col,
print.auc.cex=par("cex"),
# Grid
grid=FALSE,
grid.v={if(is.logical(grid) && grid[1]==TRUE) {seq(0, 1, 0.1) * ifelse(x$percent, 100, 1)} else if(is.numeric(grid)) {seq(0, ifelse(x$percent, 100, 1), grid[1])} else {NULL}},
grid.h={if (length(grid) == 1) {grid.v} else if (is.logical(grid) && grid[2]==TRUE) {seq(0, 1, 0.1) * ifelse(x$percent, 100, 1)} else if(is.numeric(grid)) {seq(0, ifelse(x$percent, 100, 1), grid[2])} else {NULL}},
grid.lty=3,
grid.lwd=1,
grid.col="#DDDDDD",
# Polygon for the AUC
auc.polygon=FALSE,
auc.polygon.col="gainsboro",
auc.polygon.lty=par("lty"),
auc.polygon.density=NULL,
auc.polygon.angle=45,
auc.polygon.border=NULL,
# Polygon for the maximal AUC possible
max.auc.polygon=FALSE,
max.auc.polygon.col="#EEEEEE",
max.auc.polygon.lty=par("lty"),
max.auc.polygon.density=NULL,
max.auc.polygon.angle=45,
max.auc.polygon.border=NULL,
# Confidence interval
ci=!is.null(x$ci),
ci.type=c("bars", "shape", "no"),
ci.col=ifelse(ci.type=="bars", par("fg"), "gainsboro"),
...)
"plot.roc"(x, data, ...)
"plot.roc"(x, predictor, ...)
"plot.roc"(x, ...)TRUE (default) and the “roc” object
contains an “auc” field, re-use these specifications for the
plot (specifically print.auc, auc.polygon and
max.auc.polygon arguments). See details.
FALSE, the default) or increasing “1 - specificity”
(TRUE) as in most legacy software. This affects only the
axis, not the plot coordinates.add=FALSE.
plot.FALSE, NULL or “no”: no threshold is
displayed. TRUE or “best”: the threshold with the
highest sum sensitivity + specificity is plotted (this might be more
than one threshold). “all”: all the points of the ROC
curve. “local maximas”: all the local maximas. Numeric
vector: direct definition of the thresholds to display.
Note that on a smoothed ROC curve, only “best” is supported.
print.thres="best" or print.thres=TRUE, what method must be used to determine which
threshold is the best. See argument best.method and best.weights to
coords for more details.
type and col arguments for
plot.ci. The special value “no” disables the plotting of confidence intervals.
roc and plot.roc.roc when calling
plot.roc.default or plot.roc.formula. Note that the
plot argument for roc is not allowed.
Arguments for auc and graphical functions
plot, abline, polygon,
points, text and plot.ci
if applicable.
print.auc, auc.polygon and max.auc.polygon
arguments, an AUC specification is
required. By default, the total AUC is plotted, but you may want a
partial AUCs. The specification is defined by:
reuse.auc is set to TRUE (default). It is naturally
inherited from any call to roc and fits most cases.
auc with ...
(arguments partial.auc, partial.auc.correct and
partial.auc.focus). In this case, you must ensure either that
the roc object do not contain an auc field (if
you called roc with auc=FALSE), or set
reuse.auc=FALSE.
reuse.auc=FALSE the auc function will always
be called with ... to determine the specification, even if
the “roc” object do contain an auc field. As well if the “roc” object do not contain an auc
field, the auc function will always be called with
... to determine the specification. Warning: if the roc object passed to plot.roc contains an auc
field and reuse.auc=TRUE, auc is not called and
arguments such as partial.auc are silently ignored. roc when plot=TRUE (not by
default). plot.roc.formula and plot.roc.default are convenience methods
that build the ROC curve (with the roc function) before
calling plot.roc.roc. You can pass them arguments for both
roc and plot.roc.roc. Simply use plot.roc
that will dispatch to the correct method.The plotting is done in the following order:
add=FALSE.
grid.v and grid.h are not NULL.
max.auc.polygon=TRUE.
ci=TRUE, ci.type="shape" and x$ci isn't a “ci.auc”.
auc.polygon=TRUE.
identity=TRUE.
ci=TRUE, ci.type="bars" and x$ci isn't a “ci.auc”.
print.thres is TRUE or numeric.
print.auc=TRUE.
Graphical functions are called with suppressWarnings.
roc, auc, ci
data(aSAH)
# Syntax (response, predictor):
plot.roc(aSAH$outcome, aSAH$s100b)
# With a roc object:
rocobj <- roc(aSAH$outcome, aSAH$s100b)
# identical:
plot(rocobj)
plot.roc(rocobj)
# Add a smoothed ROC:
plot.roc(smooth(rocobj), add=TRUE, col="blue")
legend("bottomright", legend=c("Empirical", "Smoothed"),
col=c(par("fg"), "blue"), lwd=2)
# With more options:
plot(rocobj, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2),
grid.col=c("green", "red"), max.auc.polygon=TRUE,
auc.polygon.col="blue", print.thres=TRUE)
# To plot a different partial AUC, we need to ignore the existing value
# with reuse.auc=FALSE:
plot(rocobj, print.auc=TRUE, auc.polygon=TRUE, partial.auc=c(1, 0.8),
partial.auc.focus="se", grid=c(0.1, 0.2), grid.col=c("green", "red"),
max.auc.polygon=TRUE, auc.polygon.col="blue", print.thres=TRUE,
reuse.auc=FALSE)
# Add a line to the previous plot:
plot.roc(aSAH$outcome, aSAH$wfns, add=TRUE)
# Alternatively, you can get the plot directly from roc():
roc(aSAH$outcome, aSAH$s100b, plot=TRUE)
Run the code above in your browser using DataLab