kebabs (version 1.6.2)

evaluatePrediction: Evaluate Prediction

Description

Evaluate performance results of prediction on a testset based on given labels for binary classification

Usage

evaluatePrediction(prediction, label, allLabels = NULL, decValues = NULL,
  print = TRUE, confmatrix = TRUE, numPrecision = 3,
  numPosNegTrainSamples = numeric(0))

Arguments

prediction

prediction results as returned by predict for predictionType="response".

label

label vector of same length as parameter 'prediction'.

allLabels

vector containing all occuring labels once. This parameter is required only if the label vector is numeric. Default=NULL

decValues

numeric vector containing decision values for the predictions as returned by the predict method with predictionType set to decision. This parameter is needed for the determination of the AUC value which is currently only supported for binary classification. Default=NULL

print

This parameter indicates whether performance values should be printed or returned as data frame without printing (for details see below). Default=TRUE

confmatrix

When set to TRUE a confusion matrix is printed. The rows correspond to predictions, the columns to the true labels. Default=TRUE

numPrecision

minimum number of digits to the right of the decimal point. Values between 0 and 20 are allowed. Default=3

numPosNegTrainSamples

optional integer vector with two values giving the number of positive and negative training samples. When this parameter is set the balancedness of the training set is reported. Default=numeric(0)

Value

• When the parameter 'print' is set to FALSE the function returns a data frame containing the prediction performance values (for details see above).

Details

For binary classfication this function computes the performance measures accuracy, balanced accuracy, sensitivity, specificity, precision and the Matthews Correlation Coefficient(MCC). If decision values are passed in the parameter decValues the function additionally determines the AUC. When the number of positive and negative training samples is passed to the function it also shows the balancedness of the training set. The performance results are either printed by the routine directly or returned in a data frame. The columns of the data frame are:

ll{ column name performance measure -------------------- -------------- TP true positive FP false positive FN false negative TN true negative ACC accuracy BAL_ACC balanced accuracy SENS sensitivity SPEC specificity PREC precision MAT_CC Matthews correlation coefficient AUC area under ROC curve PBAL prediction balancedness (fraction of positive samples) TBAL training balancedness (fraction of positive samples) }

References

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: http://dx.doi.org/10.1093/bioinformatics/btv176{10.1093/bioinformatics/btv176}.

See Also

predict, kbsvm

Examples

## set seed for random generator, included here only to make results
## reproducable for this example
set.seed(456)
## 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))

## print prediction performance including AUC
## additionally determine decision values
preddec <- predict(model, enhancerFB[test], predictionType="decision")
evaluatePrediction(pred, yFB[test], allLabels=unique(yFB),
                   decValues=preddec)

## print prediction performance including training set balance
trainPosNeg <- c(length(which(yFB[train] == 1)),
                 length(which(yFB[train] == -1)))
evaluatePrediction(pred, yFB[test], allLabels=unique(yFB),
                   numPosNegTrainSamples=trainPosNeg)

## or get prediction performance as data frame
perf <- evaluatePrediction(pred, yFB[test], allLabels=unique(yFB),
                           print=FALSE)

## show performance values in data frame
perf