assessment-class(Rmagpie)
assessment-class()所属R语言包：Rmagpie
assessment: A central class to perform one and two layers of external cross-validation on microarray data
评估：一个中央级的芯片数据进行一至两层外部交叉验证
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This class stores the information relevant to a microarray classification assessment: data set, classifier and options are set
这个类存储到一个芯片分类评估相关的信息：一组数据，分类和选项设置
创建对象----------Creating objects----------
new("assessment", dataset noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", featureSelectionMethod="rfe", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions)
new("assessment", dataset noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", featureSelectionMethod="rfe", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions)
Creates an assessment to be performed on the data set dataset using the feature selection options defined by featureSelectionMethod on the feature selection method featureSelectionMethod and with the classifier classifierName. Once all the options have been selected one-layer and two-layers of cross-validation can be performed by calling runOneLayerExtCv and runTwoLayerExtCv respectively.
创建一个数据集进行评估dataset使用featureSelectionMethodfeatureSelectionMethod特征选择方法和分类classifierName的特征选择定义的选项。一旦所有的选项已被选中层和2层交叉验证，可以通过调用runOneLayerExtCv和runTwoLayerExtCv分别进行。
new("assessment", dataset noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", featureSelectionMethod="rfe", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2)
new("assessment", dataset noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", featureSelectionMethod="rfe", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2)
If featureSelectionOptions is not precised in the arguments then the options for the feature selection method are determined according to the dataset and the featureSelectionMethod. If RFE is selected as feature selection method then an object of class geneSubsets is automatically created. It defines sizes of subsets og genes for 1 to the number of features in the dataset by power of 2. If the feature selection method is NSC then the thresholds are taken to be the default thresholds generated by the function pamr.train from package pamr applied on dataset.
featureSelectionOptions如果不精细参数，然后根据特征选择方法的选择到dataset和featureSelectionMethod。如果RFE特征选择方法，然后选择一个类geneSubsets的对象将自动创建。它定义子集噩基因大小为1到dataset 2的乘方的功能。如果特征选择方法，然后是国科会的阈值，采取的是默认的阈值产生pamr.train包pamrdataset应用功能。
插槽----------Slots----------
dataset: Object of class "dataset". Microarray data set to be used for cross-validation
dataset类"dataset"的对象。微阵列数据集被用于交叉验证
noFolds1stLayer: numeric. Number of folds in the inner layee layer of cross-validation
noFolds1stLayer：numeric。在交叉验证的内在layee层褶皱的数量
noFolds2ndLayer: numeric. Number of folds in one-layer cross-validation and in the
noFolds2ndLayer：numeric。倍数数层交叉验证，并在
classifierName: character. Name of the classifier: 'svm' for Support Vector
classifierName：character。 SVM支持向量的分类名称：
featureSelectionMethod: Object of class "character" ~~
featureSelectionMethod：Object类的"character"~~
typeFoldCreation: character. Type of fold creation: 'original', 'simple' or 'naive'
typeFoldCreation：character。倍，创造的类型：“原始”，“简单”或“幼稚”
svmKernel: Object of class "character" ~~
svmKernel：Object类的"character"~~
noOfRepeats: numeric. Number of repeats to be performed for each cross-validation.
noOfRepeats：numeric。要为每个交叉验证进行重复。
featureSelectionOptions: Object of class "featureSelectionOptions". Sizes of subsets
featureSelectionOptions类"featureSelectionOptions"的对象。尺寸子集
resultRepeated1LayerCV: Object of class "resultRepeated1LayerCVOrNULL" NULL is the external one layer CV has not been run yet, resultRepeated1LayerCV containing the results
resultRepeated1LayerCV：Object类的"resultRepeated1LayerCVOrNULL"NULL外部的一层简历也还没有运行，resultRepeated1LayerCV包含的结果
resultRepeated2LayerCV: Object of class "result2LayerCVorNULL" NULL is the external one layer CV has not been run yet, result2LayerCV containing the results
resultRepeated2LayerCV：Object类的"result2LayerCVorNULL"NULL外部的一层简历也还没有运行，result2LayerCV包含的结果
finalClassifier: Object of class "finalClassifierOrNULL" NULL is the final classifier has not been determined yet, finalClassifier containing the final Classifier for each feature selection option.
finalClassifier：Object类的"finalClassifierOrNULL"NULL是还没有被确定最终分类，finalClassifier包含每个功能选择选项的最后分类。
方法----------Methods----------
classifyNewSamples(assessment) Classify new samples using the final
classifyNewSamples(assessment)使用的最终分类的新样本
findFinalClassifier(assessment) Train the final classifier related
findFinalClassifier(assessment)培养相关的最终分类
getClassifierName(assessment), getClassifierName(assessment)<- Retrieve
getClassifierName(assessment), getClassifierName(assessment)<-检索
getDataset(assessment), getDataset(assessment)<- Retrieve and Modify the dataset associated to the current assessment (slot dataset),
getDataset(assessment), getDataset(assessment)<-检索和修改目前的评估（槽集）关联的数据集，
getFeatureSelectionOptions(assessment), getFeatureSelectionOptions(assessment)<- Retrieve
getFeatureSelectionOptions(assessment), getFeatureSelectionOptions(assessment)<-检索
getFinalClassifier(assessment) Retreive the final classifier associated with
getFinalClassifier(assessment)Retreive与最终的分类
getNoFolds1stLayer(assessment), getNoFolds1stLayer(assessment)<- Retrieve
getNoFolds1stLayer(assessment), getNoFolds1stLayer(assessment)<-检索
getNoFolds2ndLayer(assessment), getNoFolds2ndLayer(assessment)<- Retrieve
getNoFolds2ndLayer(assessment), getNoFolds2ndLayer(assessment)<-检索
getNoOfRepeats(assessment), getNoOfRepeats(assessment)<- Retrieve
getNoOfRepeats(assessment), getNoOfRepeats(assessment)<-检索
getResult1LayerCV(assessment) Retrieve the results of the one-layer cross validation (slot resultRepeated1LayerCV). An easier access to this data is
getResult1LayerCV(assessment)一个层交叉验证（插槽resultRepeated1LayerCV）检索的结果。更容易获得这个数据是
getResult2LayerCV(assessment) Retrieve the results of the two-layers cross validation (slot result2LayerCV). An easier access to this data is
getResult2LayerCV(assessment)两个层交叉验证（插槽result2LayerCV）检索的结果。更容易获得这个数据是
getResults User-friendly methods to retreive data in the results of one-layer and two-layers of cross-validation. See related documentation
getResults用户友好的方法，在1层和交叉验证2层的结果中检索数据。请参阅相关文档
getSvmKernel(assessment), getSvmKernel(assessment)<- Retrieve and Modify the svm kernel used as a final classifier if svm is the concerned
getSvmKernel(assessment), getSvmKernel(assessment)<-检索和修改内核作为最终分类的SVM SVM是如果有关
getTypeFoldCreation(assessment), getTypeFoldCreation(assessment)<- Retrieve and Modify the type of folds creation to use for each cross-validation
getTypeFoldCreation(assessment), getTypeFoldCreation(assessment)<-检索和修改褶皱创造的类型，使用的每个交叉验证
runOneLayerExtCV Run one-layer cross-validation,
runOneLayerExtCV运行层交叉验证，
runTwoLayerExtCV Run two-layer cross-validation,
runTwoLayerExtCV运行两层交叉验证，
作者（S）----------Author(s)----------
Camille Maumet
参见----------See Also----------
geneSubsets, getResults-methods, runOneLayerExtCV-methods, runTwoLayerExtCV-methods
geneSubsets，getResults-methods，runOneLayerExtCV-methods，runTwoLayerExtCV-methods
举例----------Examples----------
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "data")[数据通路（< - file.path的“C：”，“文件和设置”，“c.maumet”，“我的文档”，“Programmation”，“资料”）]
#myDataset <- new("dataset", dataId="vantVeer_70", dataPath=file.path(dataPath, "vantVeer_70"))[myDataset < - 新（“数据集”，“dataId =”vantVeer_70，数据通路= file.path（数据通路，“vantVeer_70”））]
# myDataset<-loadData(myDataset)[myDataset <的LoadData（myDataset）]
data('vV70genesDataset')
# assessment with RFE and SVM[RFE和SVM与评估]
myExpe <- new("assessment", dataset=vV70genes,
noFolds1stLayer=10,
noFolds2ndLayer=9,
classifierName="svm",
typeFoldCreation="original",
svmKernel="linear",
noOfRepeat=2,
featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6)))
# Another assessment where the subsets are computed automatically[另一个子集自动计算评估]
anotherExpe <- new("assessment", dataset=vV70genes,
noFolds1stLayer=10,
noFolds2ndLayer=9,
classifierName="svm",
typeFoldCreation="original",
svmKernel="linear",
noOfRepeat=2)
getFeatureSelectionOptions(anotherExpe, topic='maxSubsetSize')
getFeatureSelectionOptions(anotherExpe, topic='subsetsSizes')
# assessment with NSC[评估与NSC]
expeWithNSC <- new("assessment",dataset=vV70genes,
noFolds1stLayer=10,
noFolds2ndLayer=9,
classifierName="nsc",
featureSelectionMethod='nsc',
typeFoldCreation="original",
svmKernel="linear",
noOfRepeat=2)
getFeatureSelectionOptions(expeWithNSC, topic='thresholds')
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-26 13:00:15
