Data set: Artificial cross-validation data for use with ROCR

Description

A mock data set containing 10 sets of predictions and corresponding labels as would be obtained from 10-fold cross-validation.

Usage

data(ROCR.xval)

Format

A two element list. The first element, ROCR.xval$predictions, is itself a 10 element list. Each of these 10 elements is a vector of numerical predictions for each cross-validation run. Likewise, the second list entry, ROCR.xval$labels is a 10 element list in which each element is a vector of true class labels corresponding to the predictions.

Examples

# plot ROC curves for several cross-validation runs (dotted
# in grey), overlaid by the vertical average curve and boxplots
# showing the vertical spread around the average.
data(ROCR.xval)
pred <- prediction(ROCR.xval$predictions, ROCR.xval$labels)
perf <- performance(pred,"tpr","fpr")
plot(perf,col="grey82",lty=3)
plot(perf,lwd=3,avg="vertical",spread.estimate="boxplot",add=TRUE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ROCR)
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ROCR/ROCR.xval.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ROCR.xval
> ### Title: Data set: Artificial cross-validation data for use with ROCR
> ### Aliases: ROCR.xval
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # plot ROC curves for several cross-validation runs (dotted
> # in grey), overlaid by the vertical average curve and boxplots
> # showing the vertical spread around the average.
> data(ROCR.xval)
> pred <- prediction(ROCR.xval$predictions, ROCR.xval$labels)
> perf <- performance(pred,"tpr","fpr")
> plot(perf,col="grey82",lty=3)
> plot(perf,lwd=3,avg="vertical",spread.estimate="boxplot",add=TRUE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>