Plot a ROC curve

Description

This function plots a ROC curve. It can accept many arguments to tweak the appearance of the plot. Two syntaxes are possible: one object of class “roc”, or either two vectors (response, predictor) or a formula (response~predictor) as in the roc function.

Usage

## S3 method for class 'roc':
plot(x, ...)
## S3 method for class 'smooth.roc':
plot(x, ...)
## S3 method for class 'roc':
plot.roc(x, add=FALSE, reuse.auc=TRUE,
# 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$percent, "Specificity (%)", "Specificity"),
ylab=ifelse(x$percent, "Sensitivity (%)", "Sensitivity"),
width=8.5,
height=8.5,
# col, lty and lwd for the ROC line only
col=par("col"),
lty=par("lty"),
lwd=3,
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=16,
print.thres.col="black",
print.thres.pattern=ifelse(x$percent, "%.1f (%.1f%%, %.1f%%)", "%.3f (%.3f, %.3f)"),
print.thres.cex=par("cex"),
print.thres.pattern.cex=print.thres.cex,
# Print the AUC on the plot
print.auc=FALSE,
print.auc.pattern=NULL,
print.auc.x=ifelse(x$percent, 50, .5),
print.auc.y=ifelse(x$percent, 50, .5),
print.auc.col=col,
print.auc.cex=par("cex"),
# Grid
grid=FALSE,
grid.v={if(is.logical(grid) && grid[1]==TRUE){seq(0, 1, 0.1) * ifelse(x$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$percent, 100, 1), grid[2])} else {NULL}},
grid.lty=2,
grid.lwd=1,
grid.col="#DDDDDD",
# Polygon for the AUC
auc.polygon=FALSE,
auc.polygon.col="gainsboro",
auc.polygon.lty=par("lty"),
auc.polygon.border="#000000",
auc.polygon.density=-1,
auc.polygon.angle=45,
# Polygon for the maximal AUC possible                           
max.auc.polygon=FALSE,
max.auc.polygon.col="#EEEEEE", 
max.auc.polygon.lty=par("lty"),
max.auc.polygon.border="#000000",
max.auc.polygon.density=-1,
max.auc.polygon.angle=45,
# Confidence interval
ci=!is.null(x$ci),
ci.type=c("bars", "shape", "no"),
ci.col=ifelse(ci.type=="bars", par("fg"), "gainsboro"),
...)
## S3 method for class 'formula':
plot.roc(x, data, ...)
## Default S3 method:
plot.roc(x, predictor, ...)
## S3 method for class 'smooth.roc':
plot.roc(x, ...)

Arguments

Details

This function is typically called from 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:

1. A new plot is created if add=FALSE. If no graphical device is opened, a new graphsheet is started with margins set to make the plot square. Otherwise the plot will have the proportions defined by the device previously opened.
2. The grid is added if grid.v and grid.h are not NULL.
3. The maximal AUC polygon is added if max.auc.polygon=TRUE.
4. The CI shape is added if ci=TRUE, ci.type="shape" and x$ci isn't a “ci.auc”.
5. The AUC polygon is added if auc.polygon=TRUE.
6. The identity line if identity=TRUE.
7. The actual ROC line is added.
8. The CI bars are added if ci=TRUE, ci.type="bars" and x$ci isn't a “ci.auc”.
9. The selected thresholds are printed if print.thres is TRUE or numeric.
10. The AUC is printed if print.auc=TRUE.

Value

This function returns a list of class “roc” invisibly. See roc for more details.

AUC specification

For 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:

1. the “auc” field in the “roc” object if reuse.auc is set to TRUE (default). It is naturally inherited from any call to roc and fits most cases.
2. passing the specification to 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.

If 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.

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

roc, auc, ci

Examples

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(.6, .4, 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)