R语言 Rmagpie包 runTwoLayerExtCV-methods()函数中文帮助文档(中英文对照)

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runTwoLayerExtCV-methods(Rmagpie)
runTwoLayerExtCV-methods()所属R语言包：Rmagpie

                                        runTwoLayerExtCV: Method to run an external two-layers cross-validation
                                         runTwoLayerExtCV：运行外部两个层交叉验证的方法

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This method run an external two-layers cross-validation according to the options stored in an object of class assessment. The concept of two-layers cross-validation has been introduced by J.X. Zhu,G.J. McLachlan, L. Ben-Tovim Jonesa, I.A.Wood in 'On selection biases with prediction rules formed from gene expression data' and by I. A. Wood, P. M. Visscher, and K. L. Mengersen in 'Classification based upon gene expression data: bias and precision of error rates' (cf. section References). This technique of cross-validation is used to determine an unbiased estimate of the best error rate (using the best size of subset for RFE-SVM, of the best threshold for NSC) when feature selection is involved.
这种方法运行外部两个层次的交叉验证，根据评估类的对象中存储的选项。两个层交叉验证的概念已经推出剑侠朱GJ麦克拉克伦，L。的本Tovim Jonesa IAWood在“选择与基因表达数据形成的预测规则”和IA木材，PM比塞彻，基于基因表达数据的分类和KL Mengersen的偏见：偏见和误差精度率“（见参考文献）。这种交叉验证技术被用来确定一个最好误码率（RFE-SVM子集的最佳大小，使用国科会的最佳阈值）时，功能选择参与的无偏估计。


参数----------Arguments----------

参数：object
Object of class assessment. Object assessment of interest
Object of class assessment。感兴趣的对象评估


值----------Value----------

object of class assessment in which the one-layer external cross-validation has been computed, therfore, the slot resultRepeated2LayerCV 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 of class assessment在一个层外部交叉验证计算，故槽resultRepeated2LayerCV没有更多NULL。此方法打印出评估的主要成果，访问结果的全部细节，用户必须调用的方法getResults。


方法----------Methods----------




object = "assessment" This method is only applicable on objects of class
对象的“评估”这种方法只适用于类对象


参考文献----------References----------

"On selection biases with prediction rules formed from gene expression data", Journal of Statistical Planning and Inference, 38:374-386.
"Classification based upon gene expression data: bias and precision of error rates"

参见----------See Also----------

assessment, getResults, runOneLayerExtCV-methods
assessment，getResults，runOneLayerExtCV-methods


举例----------Examples----------


data('vV70genesDataset')

# assessment with RFE and SVM[RFE和SVM与评估]
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 <- runTwoLayerExtCV(myExpe)

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