plot.PRROC: Plotting PRROC objects
In PRROC: Precision-Recall and ROC Curves for Weighted and Unweighted Data

Description Usage Arguments Details Author(s) See Also Examples

Plots the PR or ROC curves of a PRROC object. To obtain such curves, pr.curve or roc.curve must be called with argument curve=TRUE.

## S3 method for class 'PRROC'
plot(x, xlim=c(0,1), ylim=c(0,1), auc.main=TRUE, 
    auc.type=c("integral","davis.goadrich"), 
    legend=ifelse(is.logical(color) & color==TRUE,4,NA), xlab=NULL, ylab=NULL, 
    main=NULL, color=TRUE, lwd=3, add=FALSE, 
    scale.color=hsv(h=seq(0,1,length=100)*0.8, s=1, v=1), 
    max.plot = FALSE, min.plot = FALSE, rand.plot = FALSE, 
    fill.area = (max.plot & min.plot), maxminrand.col = grey(0.5), 
    fill.color = grey(0.95), ...)

`x`	a PRROC object obtained from `pr.curve` or `roc.curve`
`xlim`	as in `plot`
`ylim`	as in `plot`
`auc.main`	`TRUE` to show the area under curve in the title
`auc.type`	the area under the curve shown in the title (see also `pr.curve`). Ignored if `auc.main=FALSE` or `x$type=="ROC"`.
`legend`	if `TRUE` (and `color==TRUE`), a legend for the color scheme for the scores is shown on the right side of the main plot. If `legend` is a number between 1 and 4, the legend is drawn on the correspond side of the main plot (see `axis`). If `legend` is `FALSE` or `NA`, no legend is drawn.
`xlab`	the label of the x-axis. If `NULL`, label is chosen according the kind of the curve.
`ylab`	the label of the y-axis. If `NULL`, label is chosen according the kind of the curve.
`main`	the title of the plot. If `NULL`, title is chosen according the kind of the curve.
`color`	if `TRUE`, curve is plotted in colors reflecting score thresholds, if `FALSE`, the curve is plotted in black, if a color value (e.g., `2` or `"red"` for red) the curve is plotted in that color. For everything different from `TRUE`, the legend is omitted irrespective of the value of argument `legend`
`lwd`	the line width of the curve
`add`	if `TRUE`, the curve is added to an existing plot. Only works correctly, if in the previous call (with `add==FALSE`), no legend has been added to the plot.
`scale.color`	vector of colors that are used to reflect score thresholds, compare `color`
`max.plot`	if `TRUE` and `x` has been computed including the maximum curve, plot this maximum curve (ignored if `add=TRUE`)
`min.plot`	if `TRUE` and `x` has been computed including the minimum curve, plot this maximum curve (ignored if `add=TRUE`)
`rand.plot`	if `TRUE` and `x` has been computed including the curve of a random classifier, plot this curve (ignored if `add=TRUE`)
`fill.area`	fill the area between maximum and minimum curve (given both have been computed for `x`) (ignored if `add=TRUE`)
`maxminrand.col`	the plot color for the maximum, minimum, and random curves
`fill.color`	the fill color for the area between minimum and maximum curve
`...`	see `plot`

The plot method for PRROC objects can be used in different ways.

The first is to plot a visualization of a single ROC or PR curve that also represents the classification thresholds of individual points on the curve by a color scale. In this case, a PRROC object must be provided as x, add must be FALSE, and color must be TRUE. If, in addition, legend is set to TRUE, a legend translating colors to numerical threshold values is included to the right of the curve plot itself. The layout of curve plot and legend is accomplished using layout(), which means that this type of ROC/PR plot cannot be combined with other/complex layouts.

The second application of the plot method is to compare the performance of different classifiers (typically on the same data set). To do so, plot must be called with add=FALSE and color set to one specific color (e.g., 2, "red",...) for the first PRROC object provided as x. Subsequent calls of plot with add=TRUE can be used to add further curves to the first plot, where different colors may be specified by the color parameter.

In both cases, the first (or only) call to plot also allows for including plots of the maximum and minimum curve, highlighting the area between minimum and maximum, and the curve of a random classifier. For this purpose, the PRROC object needs to be created (using pr.curve or roc.curve) with the corresponding parameters (e.g., max.compute) set to TRUE.

Additional examples for the different use cases and corresponding plot commands are given in the documentations of pr.curve and roc.curve.

Jan Grau and Jens Keilwagen

pr.curve

roc.curve

# create artificial scores as random numbers
x <- rnorm( 1000 );
y <- rnorm( 1000, -1 );

# compute PR curve
pr <- pr.curve( x, y, curve = TRUE );

# standard plot of PR curve
plot( pr );

# compute ROC curve
roc <- roc.curve( x, y, curve = TRUE );

# standard plot of ROC curve
plot( roc );

# create another set of scores
x.2 <- rnorm( 1000 );
y.2 <- rnorm( 1000, -2 );

# compute PR curve
pr.2 <- pr.curve( x.2, y.2, curve=TRUE );
# and ROC curve
roc.2 <- roc.curve( x.2, y.2, curve=TRUE );

# plot PR curve in red, without legend
plot( pr, color = "red", auc.main=FALSE );
# add second PR curve in green
plot( pr.2, color = 3, add = TRUE );


# plot ROC curve in red, without legend
plot( roc, color = "red", auc.main=FALSE);
# add second ROC curve in green
plot( roc.2, color = 3, add = TRUE );

# plot PR curve with legend below the main plot
plot( pr, legend=1 );

# compute PR curve with minimum and maximum curve, and random classifier
pr <- pr.curve( x, y, curve = TRUE, max.compute = TRUE, 
  min.compute = TRUE, rand.compute = TRUE);

# plot PR curve with area between minimum and 
# maximum curve in green and random classifier in blue
plot(pr, rand.plot = TRUE, fill.area = TRUE, fill.color = rgb(0.8,1,0.8), 
  maxminrand.col = "blue" );