performance {BioSeqClass} | R Documentation

## Performance Evaluation

### Description

Evaluate the performance of classification model.

### Usage

      performance(predictClass,factClass)

### Arguments

`predictClass` | a factor of predicted classifications of training set, comprising of "-1" or "+1".
`factClass` | a vector of true classifications of training set, comprising of "-1" or "+1".

### Details

`performance` evaluates the performance of classification model. It cacluates: tp (true positive), tn(ture negative), fp(false positive), fn(false negative), prc(precision), sn(sensitivity), sp(specificity), acc(accuracy), mcc(Matthews Correlation Coefficient), pc(Performance Coefficient).

### Author(s)

Hong Li

### Examples

      ## read positive/negative sequence from files.
      tmpfile1 = file.path(path.package("BioSeqClass"), "example", "acetylation_K.pos40.pep")
      tmpfile2 = file.path(path.package("BioSeqClass"), "example", "acetylation_K.neg40.pep")
      posSeq = as.matrix(read.csv(tmpfile1,header=FALSE,sep="\t",row.names=1))[,1]
      negSeq = as.matrix(read.csv(tmpfile2,header=FALSE,sep="\t",row.names=1))[,1]
      data = data.frame(rbind(featureBinary(posSeq,elements("aminoacid")),
           featureBinary(negSeq,elements("aminoacid")) ),
           class=c(rep("+1",length(posSeq)),
           rep("-1",length(negSeq))) )

      ## sample train and test data
      tmp = c(sample(1:length(posSeq),length(posSeq)*0.8),
        sample(length(posSeq)+(1:length(negSeq)),length(negSeq)*0.8))
      train = data[tmp,]
      test = data[-tmp,]

      ## Build classification model using training data
      model1 = classifyModelLIBSVM(train,svm.kernel="linear",svm.scale=FALSE)
      ## Predict test data by classification model
      testClass = predict(model1, test[,-ncol(test)])
      ## Evaluate the performance of classification model
      performance(testClass,test[,ncol(test)])

[Package BioSeqClass version 1.40.0 Index]