mlDNA (version 1.1)

predictor: Prediction

Description

Score each sample with the machine learning-based classification prediction model alreadly trained with training dataset.

Usage

predictor(method = c("randomForest", "svm", "nnet" ), classifier, featureMat)

Arguments

method

character string specifying the machine learning algorithm used to buld classification model.

classifier

trained prediction model obtained from the classifier function.

featureMat

a numeric matrix; feature matrix containing samples to be scored and their feature values.

Value

value

A numeric vector containing the prediction score of each sample.

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$KnownSaltGenes)
#    ##unlabeled samples
#    unlabelSamples <- setdiff( rownames(featureMat), positiveSamples )
#    idx <- sample(length(unlabelSamples))
#    ##randomly selecting a set of unlabeled samples as negative samples
#    negativeSamples <- unlabelSamples[idx[1:length(positiveSamples)]]
# 
#    ##for random forest, and using five-fold cross validation 
#    ##for obtaining optimal parameters
#    cl <- classifier( method = "randomForest", featureMat = featureMat, 
#         positiveSamples = positiveSamples, negativeSamples = negativeSamples,
#         tunecontrol = tune.control(sampling = "cross", cross = 5), 
#         ntree = 100 ) #build 100 trees for the forest
# 
# 
#    ##constructed prediction model
#    predModel <- cl$best.model
# 
#    ##perform prediction
#    predResult <- predictor(method = "randomForest", 
#                            classifier = predModel, 
#                            featureMat = featureMat)
# 
# ## End(Not run)