This is the method to plot all objects of class performance.
# S4 method for performance,missing
plot(x, y, ..., avg="none", spread.estimate="none",
spread.scale=1, show.spread.at=c(), colorize=FALSE,
colorize.palette=rev(rainbow(256,start=0, end=4/6)),
colorkey=colorize, colorkey.relwidth=0.25, colorkey.pos="right",
print.cutoffs.at=c(), cutoff.label.function=function(x) { round(x,2) },
downsampling=0, add=FALSE )an object of class performance
not used
Optional graphical parameters to adjust different components of
the performance plot. Parameters are directed to their target component by
prefixing them with the name of the component
(component.parameter, e.g. text.cex). The following
components are available: xaxis, yaxis,
coloraxis, box (around the plotting region),
points, text, plotCI (error bars),
boxplot. The names of these components are influenced by the R
functions that are used to create them. Thus, par(component)
can be used to see which parameters are available for a
given component (with the expection of the three axes; use
par(axis) here). To adjust the canvas or the performance
curve(s), the standard plot parameters can be used without any prefix.
If the performance object describes several curves
(from cross-validation runs or bootstrap evaluations of one
particular method), the curves from each of the runs can be
averaged. Allowed values are none (plot all curves separately), horizontal
(horizontal averaging), vertical (vertical averaging), and
threshold (threshold (=cutoff) averaging). Note that while
threshold averaging is always feasible, vertical and horizontal
averaging are not well-defined if the graph cannot be represented as
a function x->y and y->x, respectively.
When curve averaging is enabled, the variation
around the average curve can be visualized as standard error bars
(stderror), standard deviation bars (stddev), or by using
box plots (boxplot). Note that the function plotCI,
which is used internally by ROCR to draw error bars, might raise a
warning if the spread of the curves at certain positions is 0.
For stderror or stddev, this is a
scalar factor to be multiplied with the length of the standard
error/deviation bar. For example, under normal assumptions,
spread.scale=2 can be used to get approximate 95% confidence
intervals.
For vertical averaging, this vector determines the x positions for which the spread estimates should be visualized. In contrast, for horizontal and threshold averaging, the y positions and cutoffs are determined, respectively. By default, spread estimates are shown at 11 equally spaced positions.
This logical determines whether the curve(s) should be colorized according to cutoff.
If curve colorizing is enabled, this determines the color palette onto which the cutoff range is mapped.
If true, a color key is drawn into the 4% border
region (default of par(xaxs) and par(yaxs)) of the
plot. The color key visualizes the mapping from cutoffs to colors.
Scalar between 0 and 1 that determines the fraction of the 4% border region that is occupied by the colorkey.
Determines if the colorkey is drawn vertically at
the right side, or horizontally at the top of the
plot.
This vector specifies the cutoffs which should be printed as text along the curve at the corresponding curve positions.
By default, cutoff annotations along the
curve or at the color key are rounded to two decimal places
before printing. Using a custom cutoff.label.function, any other
transformation can be performed on the cutoffs instead (e.g. rounding with
different precision or taking the logarithm).
ROCR can efficiently compute most performance measures even for data sets with millions of elements. However, plotting of large data sets can be slow and lead to PS/PDF documents of considerable size. In that case, performance curves that are indistinguishable from the original can be obtained by using only a fraction of the computed performance values. Values for downsampling between 0 and 1 indicate the fraction of the original data set size to which the performance object should be downsampled, integers above 1 are interpreted as the actual number of performance values to which the curve(s) should be downsampled.
If TRUE, the curve(s) is/are added to an already
existing plot; otherwise a new plot is drawn.
A detailed list of references can be found on the ROCn'COST homepage at http://rocr.bioinf.mpi-sb.mpg.de.
prediction, performance,
prediction-class, performance-class
# NOT RUN {
# plotting a ROC curve:
library(ROCR)
data(ROCR.simple)
pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels )
perf <- performance( pred, "tpr", "fpr" )
plot( perf )
# To entertain your children, make your plots nicer
# using ROCR's flexible parameter passing mechanisms
# (much cheaper than a finger painting set)
par(bg="lightblue", mai=c(1.2,1.5,1,1))
plot(perf, main="ROCR fingerpainting toolkit", colorize=TRUE,
xlab="Mary's axis", ylab="", box.lty=7, box.lwd=5,
box.col="gold", lwd=17, colorkey.relwidth=0.5, xaxis.cex.axis=2,
xaxis.col='blue', xaxis.col.axis="blue", yaxis.col='green', yaxis.cex.axis=2,
yaxis.at=c(0,0.5,0.8,0.85,0.9,1), yaxis.las=1, xaxis.lwd=2, yaxis.lwd=3,
yaxis.col.axis="orange", cex.lab=2, cex.main=2)
# }
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