plot-methods {ROCR} | R Documentation |
Plot method for performance objects
Description
This is the method to plot all objects of class performance.
Usage
## S4 method for signature '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
)
## S3 method for class 'performance'
plot(...)
Arguments
x |
an object of class |
y |
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 ( |
avg |
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
|
spread.estimate |
When curve averaging is enabled, the variation around
the average curve can be visualized as standard error bars
( |
spread.scale |
For |
show.spread.at |
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. |
colorize |
This logical determines whether the curve(s) should be colorized according to cutoff. |
colorize.palette |
If curve colorizing is enabled, this determines the color palette onto which the cutoff range is mapped. |
colorkey |
If true, a color key is drawn into the 4% border
region (default of |
colorkey.relwidth |
Scalar between 0 and 1 that determines the fraction of the 4% border region that is occupied by the colorkey. |
colorkey.pos |
Determines if the colorkey is drawn vertically at
the |
print.cutoffs.at |
This vector specifies the cutoffs which should be printed as text along the curve at the corresponding curve positions. |
cutoff.label.function |
By default, cutoff annotations along the curve
or at the color key are rounded to two decimal places before printing.
Using a custom |
downsampling |
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. |
add |
If |
Author(s)
Tobias Sing tobias.sing@gmail.com, Oliver Sander osander@gmail.com
References
A detailed list of references can be found on the ROCR homepage at http://rocr.bioinf.mpi-sb.mpg.de.
See Also
prediction
,
performance
,
prediction-class
,
performance-class
Examples
# plotting a ROC curve:
library(ROCR)
data(ROCR.simple)
pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels )
pred
perf <- performance( pred, "tpr", "fpr" )
perf
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)