mlDNA (version 1.1)

plotROC: Plotting receiver operating characteristic(ROC) Curves

Description

This function plots ROC curves for estimating the performance of machine learning-based classification model in cross validation experiments.

Usage

plotROC(cvRes)

Arguments

cvRes

results from the "cross_validation" function.

Value

A ROC plot

Examples

 ## Not run: 
# 
#    ##generate expression feature matrix
#    sampleVec1 <- c(1, 2, 3, 4, 5, 6)
#    sampleVec2 <- c(1, 2, 3, 4, 5, 6)
#    featureMat <- expFeatureMatrix( expMat1 = ControlExpMat, sampleVec1 = sampleVec1, 
#                                    expMat2 = SaltExpMat, sampleVec2 = sampleVec2, 
#                                    logTransformed = TRUE, base = 2,
#                               features = c("zscore", "foldchange", "cv", "expression"))
# 
#    ##positive samples
#    positiveSamples <- as.character(sampleData$KnownSaltGenes)
#    ##unlabeled samples
#    unlabelSamples <- setdiff( rownames(featureMat), positiveSamples )
#    idx <- sample(length(unlabelSamples))
#    ##randomly selecting a set of unlabeled samples as negative samples
#    negativeSamples <- unlabelSamples[idx[1:length(positiveSamples)]]
# 
#    ##five-fold cross validation
#    seed <- randomSeed() #generate a random seed
#    cvRes <- cross_validation(seed = seed, method = "randomForest", 
#                              featureMat = featureMat, 
#                              positives = positiveSamples, negatives = negativeSamples, 
#                              cross = 5, cpus = 1,
#                              ntree = 100 ) ##parameters for random forest algorithm
# 
#    #plot ROC curve
#    plotROC(cvRes)
# ## End(Not run)