<tool id="ROC Curve" name="Plot ROC Curve">
	<description>using kmerSVM predictions</description>
	<command interpreter="sh">r_wrapper.sh $script_file</command>
	<inputs>
		<param format="tabular" name="cvpred_data" type="data" label="CV Predictions"/>
	</inputs>
	<outputs>
		<data format="png" name="roccurve.png" from_work_dir="roccurve.png" />
	</outputs>

	<configfiles>
		<configfile name="script_file">

			rm(list = objects() )

			########## plot ROC and PR-Curve #########
			roccurve &lt;- function(x) {
				sink(NULL,type="message")
				options(warn=-1)
				suppressMessages(suppressWarnings(library('ROCR')))
				svmresult &lt;- data.frame(x)
				colnames(svmresult) &lt;- c("Seqid","Pred","Label", "CV")

				linewd &lt;- 1
				wd &lt;- 4
				ht &lt;- 4
				fig.nrows &lt;- 1 
				fig.ncols &lt;- 1
				pt &lt;- 10
				cex.general &lt;- 1 
				cex.lab &lt;- 0.9
				cex.axis &lt;- 0.9
				cex.main &lt;- 1.2
				cex.legend &lt;- 0.8

				png("roccurve.png", width=wd*fig.ncols, height=ht*fig.nrows, unit="in", res=100)

				par(xaxs="i", yaxs="i", mar=c(3.5,3.5,2,2)+0.1, mgp=c(2,0.8,0), mfrow=c(fig.nrows, fig.ncols))

				CVs &lt;- unique(svmresult[["CV"]])
				preds &lt;- list()
				labs &lt;- list()
				auc &lt;- c()
				for(i in 1:length(CVs)) {
					preds[i] &lt;- subset(svmresult, CV==(i-1), select=c(Pred))
					labs[i] &lt;- subset(svmresult, CV==(i-1), select=c(Label))
				}

				pred &lt;- prediction(preds, labs)
				perf_roc &lt;- performance(pred, 'tpr', 'fpr')
				perf_auc &lt;- performance(pred, 'auc')

				avgauc &lt;- 0

				for(j in 1:length(CVs)) {
					avgauc &lt;- avgauc + perf_auc@y.values[[j]]
				}        

				avgauc &lt;- avgauc/length(CVs)

				plot(perf_roc, colorize=T, main="ROC curve", spread.estimate="stderror",
				xlab="1-Specificity", ylab="Sensitivity", cex.lab=1.2)
				text(0.2, 0.1, paste("AUC=", format(avgauc, digits=3, nsmall=3)))

				dev.off()
			}

			############## main function #################
			d &lt;- read.table("${cvpred_data}")

			roccurve(d)

		</configfile>
	</configfiles>

	<help>
	
**Note**

This tool is based on the ROCR library.  If you use this tool please cite:
		
Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer.
ROCR: visualizing classifier performance in R.
Bioinformatics 21(20):3940-3941 (2005).  
        
----

**What it does**

Takes as input cross-validation predictions and calculates ROC Curve and its area under curve (AUC).

----

**Results**

ROC Curve: Receiver Operating Characteristic Curve. Compares true positive rate (sensitivity) to false positive rate (1 - specificity).  

Area Under the ROC Curve (AUC): Probability that of a randomly selected positive/negative pair, the positive will be scored more highly by the trained SVM than a negative.

.. class:: infomark

ROC curves can be inaccurate if there is a large skew in class distribution.  For more information see:

Jesse Davis, Mark Goadrich.
The Relationship Between Precision-Recall and ROC Curves.
Proceedings of the 23rd Annual Internation Conference on Machine Learning.
Pittsburgh, PA, 2006.

<!--
**Example**

.. image:: ./static/images/sample_roc_chen.png
-->
	</help>
</tool>