SWAP.KTSP.LOO {switchBox} | R Documentation |
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.
SWAP.KTSP.LOO(inputMat, Groups, classes = NULL, ...)
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 |
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. |
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. |
Bahman Afsari bahman.afsari@gmail.com, Luigi Marchionni marchion@jhu.edu, Wikum Dinalankara wdinala1@jhmi.edu
See switchBox for the references.
### 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