## File: plot-methods.Rd

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r-cran-rocr 1.0-11-2
 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/zzz.R \name{plot-methods} \alias{plot-methods} \alias{plot,performance,missing-method} \alias{plot.performance} \title{Plot method for performance objects} \usage{ \S4method{plot}{performance,missing}( 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 ) \method{plot}{performance}(...) } \arguments{ \item{x}{an object of class \code{performance}} \item{y}{not used} \item{...}{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 (\code{component.parameter}, e.g. \code{text.cex}). The following components are available: \code{xaxis}, \code{yaxis}, \code{coloraxis}, \code{box} (around the plotting region), \code{points}, \code{text}, \code{plotCI} (error bars), \code{boxplot}. The names of these components are influenced by the R functions that are used to create them. Thus, \code{par(component)} can be used to see which parameters are available for a given component (with the expection of the three axes; use \code{par(axis)} here). To adjust the canvas or the performance curve(s), the standard \code{plot} parameters can be used without any prefix.} \item{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 \code{none} (plot all curves separately), \code{horizontal} (horizontal averaging), \code{vertical} (vertical averaging), and \code{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.} \item{spread.estimate}{When curve averaging is enabled, the variation around the average curve can be visualized as standard error bars (\code{stderror}), standard deviation bars (\code{stddev}), or by using box plots (\code{boxplot}). Note that the function \code{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.} \item{spread.scale}{For \code{stderror} or \code{stddev}, this is a scalar factor to be multiplied with the length of the standard error/deviation bar. For example, under normal assumptions, \code{spread.scale=2} can be used to get approximate 95\% confidence intervals.} \item{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.} \item{colorize}{This logical determines whether the curve(s) should be colorized according to cutoff.} \item{colorize.palette}{If curve colorizing is enabled, this determines the color palette onto which the cutoff range is mapped.} \item{colorkey}{If true, a color key is drawn into the 4\% border region (default of \code{par(xaxs)} and \code{par(yaxs)}) of the plot. The color key visualizes the mapping from cutoffs to colors.} \item{colorkey.relwidth}{Scalar between 0 and 1 that determines the fraction of the 4\% border region that is occupied by the colorkey.} \item{colorkey.pos}{Determines if the colorkey is drawn vertically at the \code{right} side, or horizontally at the \code{top} of the plot.} \item{print.cutoffs.at}{This vector specifies the cutoffs which should be printed as text along the curve at the corresponding curve positions.} \item{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 \code{cutoff.label.function}, any other transformation can be performed on the cutoffs instead (e.g. rounding with different precision or taking the logarithm).} \item{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.} \item{add}{If \code{TRUE}, the curve(s) is/are added to an already existing plot; otherwise a new plot is drawn.} } \description{ This is the method to plot all objects of class performance. } \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) } \references{ A detailed list of references can be found on the ROCR homepage at \url{http://rocr.bioinf.mpi-sb.mpg.de}. } \seealso{ \code{\link{prediction}}, \code{\link{performance}}, \code{\link{prediction-class}}, \code{\link{performance-class}} } \author{ Tobias Sing \email{tobias.sing@gmail.com}, Oliver Sander \email{osander@gmail.com} }