runOneLayerExtCV-methods: runOneLayerExtCV: Method to run an external one-layer...
In Rmagpie: MicroArray Gene-expression-based Program In Error rate estimation

Description Arguments Value Methods References See Also Examples

This method run an external one-layer cross-validation according to the options stored in an object of class assessment. The concept of external cross-validation has been introduced by G.J. McLachlan and C. Ambroise in 'Selection bias in gene extraction on the basis of microarray gene-expression data' (cf. section References). This technique of cross-validation is used to determine an unbiased estimate of the error rate when feature selection is involved.

object

Object of class assessment. Object assessment of interest

object of class assessment in which the one-layer external cross-validation has been computed, therfore, the slot resultRepeated1LayerCV is no more NULL. This methods print out the key results of the assessment, to access the full detail of the results, the user must call the method getResults.

object = "assessment": This method is only applicable on objects of class assessment.

C. Amboise and G.J. McLachlan 2002. selection bias in gene extraction on the basis of microarray gene-expression data. PNAS, 99(10):6562-6566

assessment, getResults, runTwoLayerExtCV-methods

data('vV70genesDataset')

# assessment with RFE and SVM
myExpe <- new("assessment", dataset=vV70genes,
                   noFolds1stLayer=9,
                   noFolds2ndLayer=10,
                   classifierName="svm",
                   typeFoldCreation="original",
                   svmKernel="linear",
                   noOfRepeat=2,
                   featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6)))

myExpe <- runOneLayerExtCV(myExpe)