computeROCandAUC: Compute Receiver Operating Characteristic And Area Under The...
In kebabs: Kernel-Based Analysis Of Biological Sequences

Description Usage Arguments Details Value Author(s) References See Also Examples

Compute the receiver operating characteristic (ROC) and area under the ROC curve (AUC) as performance measure for binary classification

1	computeROCandAUC(prediction, labels, allLabels = NULL)

`prediction`	prediction results in the form of decision values as returned by `predict` for predictionType="decision".
`labels`	label vector of same length as parameter 'prediction'.
`allLabels`	vector containing all occuring labels once. This parameter is required only if the labels parameter is not a factor. Default=NULL

For binary classfication this function computes the receiver operating curve (ROC) and the area under the ROC curve (AUC).

On successful completion the function returns an object of class ROCData containing the AUC, a numeric vector of TPR values and a numeric vector containing the FPR values. If the ROC and AUC cannot be computed because of missing positive or negative samples the function returns 3 NA values.

Johannes Palme <kebabs@bioinf.jku.at>

http://www.bioinf.jku.at/software/kebabs

J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics, 31(15):2574-2576, 2015. DOI: 10.1093/bioinformatics/btv176.

predict, ROCData

## load transcription factor binding site data
data(TFBS)
enhancerFB
## select 70% of the samples for training and the rest for test
train <- sample(1:length(enhancerFB), length(enhancerFB) * 0.7)
test <- c(1:length(enhancerFB))[-train]
## create the kernel object for gappy pair kernel with normalization
gappy <- gappyPairKernel(k=1, m=3)
## show details of kernel object
gappy

## run training with explicit representation
model <- kbsvm(x=enhancerFB[train], y=yFB[train], kernel=gappy,
               pkg="LiblineaR", svm="C-svc", cost=80, explicit="yes",
               featureWeights="no")

## predict the test sequences
pred <- predict(model, enhancerFB[test])
## print prediction performance
evaluatePrediction(pred, yFB[test], allLabels=unique(yFB))

## compute ROC and AUC
preddec <- predict(model, enhancerFB[test], predictionType="decision")
rocdata <- computeROCandAUC(preddec, yFB[test], allLabels=unique(yFB))

## show AUC value
rocdata

## Not run: 
## plot ROC
plot(rocdata)

## End(Not run)