SWAP.KTSP.LOO: Performs leave one out cross validation.

In switchBox: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm

Description

Performs leave one out cross validation; then it combines prediction votes by dividing the vote sums by the number of TSPs in each fold to produce an overall cross-validation result.

Usage

SWAP.KTSP.LOO(inputMat, Groups, classes = NULL, ...)

Arguments

inputMat	is a matrix of data with rows being the features (such as gene names, if the matrix if gene expression data) and columns being the samples.
Groups	is a factor or a vector providing the phenotype class each sample belongs to. It should correspond to the order of samples given by the columns of inputMat.
classes	is a vetor of length 2 providing the two phenotype or class labels.
...	any further arguments to be passed on for k-TSP training.

Value

A list with items:

loo	A list containing the classifier, training performance and testing performance for each fold.
decision_values	Decision values obtained for each left-out sample.
predictions	Predicted classes for each left-out sample.
stats	Overall peformance results.
roc	ROC curve object for overall performance.

Author(s)

Bahman Afsari [e-mail], Luigi Marchionni [e-mail], Wikum Dinalankara [e-mail]

References

See switchBox for the references.

See Also

SWAP.KTSP.CV

Examples

### Load gene expression data
data(trainingData)
data(testingData)

require(pROC)

### perform leave one out cross-validation
result = SWAP.KTSP.LOO(matTraining, trainingGroup, featureNo=100)

### print results
result$stats

switchBox documentation built on Nov. 8, 2020, 5:43 p.m.