\name{runOneLayerExtCV-methods} \docType{methods} \alias{runOneLayerExtCV} \alias{runOneLayerExtCV-methods} \alias{runOneLayerExtCV,assessment-method} \title{runOneLayerExtCV: Method to run an external one-layer cross-validation} \description{ 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. } \section{Methods}{ \describe{ \item{object = "assessment"}{This method is only applicable on objects of class assessment.} }} \arguments{ \item{object}{\code{Object of class assessment}. Object assessment of interest} } \value{ \code{object of class assessment} in which the one-layer external cross-validation has been computed, therfore, the slot \code{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 \code{getResults}. } \seealso{ \code{\linkS4class{assessment}}, \code{\link{getResults}}, \code{\link{runTwoLayerExtCV-methods}} } \references{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} \examples{ 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) } \keyword{methods}