R: Data set: Support vector machines and neural networks applied...
ROCR.hiv
R Documentation
Data set: Support vector machines and neural networks applied to the
prediction of HIV-1 coreceptor usage
Description
Linear support vector machines (libsvm) and neural networks (R package
nnet) were applied to predict usage of the coreceptors CCR5 and CXCR4
based on sequence data of the third variable loop of the HIV envelope
protein.
Usage
data(ROCR.hiv)
Format
A list consisting of the SVM (ROCR.hiv$hiv.svm) and NN
(ROCR.hiv$hiv.nn) classification data. Each of those is in turn a list
consisting of the two elements $predictions and $labels
(10 element list representing cross-validation data).
References
Sing, T. & Beerenwinkel, N. & Lengauer, T. "Learning mixtures
of localized rules by maximizing the area under the ROC curve". 1st
International Workshop on ROC Analysis in AI, 89-96, 2004.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(ROCR)
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ROCR/ROCR.hiv.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ROCR.hiv
> ### Title: Data set: Support vector machines and neural networks applied to
> ### the prediction of HIV-1 coreceptor usage
> ### Aliases: ROCR.hiv
> ### Keywords: datasets
>
> ### ** Examples
>
> data(ROCR.hiv)
> attach(ROCR.hiv)
> pred.svm <- prediction(hiv.svm$predictions, hiv.svm$labels)
> perf.svm <- performance(pred.svm, 'tpr', 'fpr')
> pred.nn <- prediction(hiv.nn$predictions, hiv.svm$labels)
> perf.nn <- performance(pred.nn, 'tpr', 'fpr')
> plot(perf.svm, lty=3, col="red",main="SVMs and NNs for prediction of
+ HIV-1 coreceptor usage")
> plot(perf.nn, lty=3, col="blue",add=TRUE)
> plot(perf.svm, avg="vertical", lwd=3, col="red",
+ spread.estimate="stderror",plotCI.lwd=2,add=TRUE)
> plot(perf.nn, avg="vertical", lwd=3, col="blue",
+ spread.estimate="stderror",plotCI.lwd=2,add=TRUE)
> legend(0.6,0.6,c('SVM','NN'),col=c('red','blue'),lwd=3)
>
>
>
>
>
> dev.off()
null device
1
>