- x
an object of class performance
- 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 (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.
- 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
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.
- spread.estimate
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.
- spread.scale
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.
- 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 par(xaxs) and par(yaxs)) of the
plot. The color key visualizes the mapping from cutoffs to colors.
- 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 right side, or horizontally at the top of the
plot.
- 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 cutoff.label.function, any other transformation can
be performed on the cutoffs instead (e.g. rounding with different precision
or taking the logarithm).
- 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 TRUE, the curve(s) is/are added to an already existing
plot; otherwise a new plot is drawn.