performance {BioSeqClass} | R Documentation |
Evaluate the performance of classification model.
performance(predictClass,factClass)
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". |
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).
Hong Li
## 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)])