getResults-methods(Rmagpie)
getResults-methods()所属R语言包：Rmagpie
getResults Method to access the result of one-layer and two-layers cross-validation from an assessment
getResults访问从1层和2层交叉验证的结果进行评估的方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This method provides an easy interface to access the results of one-layer and two-layers of cross-validation directly from an object assessment.
这种方法提供了一个简单的接口来访问层和交叉验证2层，直接从对象评估结果。
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
参数：layer
numeric. Indice that states which layer of cross-validation must be accessed. Set to 1 to acces the one-layer cross-validation, Set to c(1,i) to acces the ith repeat of the one-layer cross-validation, Set to 2 to acces to the two-layers cross-validation, Set to c(2,i) to access the ith repeat of the two-layers cross-validation, Set to c(2,i,j) to access the jth inner layer of ith repeat of the two-layers cross-validation, Set to c(2,i,j,k) to access the kth repeat of the jth inner layer of ith repeat of the two-layers cross-validation
numeric。指数的各国必须访问层交叉验证。设置为1到ACCES一个层交叉验证，设置c(1,i)到ACCES一个层交叉验证的第i个重复，设置2成为两个ACCES层交叉验证，设置c(2,i)访问第i层交叉验证的重复，设置c(2,i,j)访问两个层第j i个重复的内层交叉验证，设置c(2,i,j,k)访问的第j个内层两个层交叉验证的第i个重复的第k个重复
参数：topic
character. Argument that specifies which kind of result is requested, the possible values are "errorRate": Access to cross-validation error rate, standard error on cross-validated error rate, error rate per fold, number of samples per fold and error rate per class, "selectedGenes": Access to the genes selected for each fold or their frequency of selection among the folds and the repeats, "bestOptionValue": For one-layer of cross-validation, access to the best option value (size of gene subset for SVM-RFE or thresholds for NSC) corresponding to the best value of the cross-validated error rate. For the two-layers of cross-validation, access the average best option value (over the repeats and folds). "executionTime": Time used to run the selected layer in seconds.
字符。请求参数，指定哪一种结果，可能的值是"errorRate"：访问交叉验证错误率，交叉验证错误率，错误率每倍标准误差，每倍和错误率的样本数每班"selectedGenes"：访问每个倍或他们之间的褶皱和重复频率的选择，选择的基因，"bestOptionValue"：对于一个层交叉验证，获得最佳的选项值（SVM-RFE或国科会的阈值大小的基因子集）对应的交叉验证错误率的最佳值。为两层交叉验证，访问的平均最好的选择值（在重复和褶皱）。 "executionTime"：用于运行在选定图层秒的时间。
参数：errorType
character. Optional, ignored if topic is not "errorRate". Specify the type of error rate requested, the possible values are: missing or "all" to access all the following error rates "cv" to access the cross-validated error rate, "se" to access the standard error on the cross validated error rate, "fold" to access the error rate per fold (not available in certain cases see section value for more details), "noSamplesPerFold" to access the number of samples in each fols (not available in certain cases see section value for more details), "class" to acces the error rate per class
字符。可选的，被忽略，如果主题是不是"errorRate"。指定的错误率要求的类型，可能的值是：missing或"all"访问以下所有的错误率"cv"访问的交叉验证错误率，"se"访问交叉验证错误率的标准错误，"fold"访问每倍的错误率（不可用在某些情况下看到更多的细节部分价值），"noSamplesPerFold"访问的样本数量每类错误率（在某些情况下没有看到更多的细节部分价值），每一个前方作战"class"ACCES
参数：genesType
character. Optional, ignored if topic is not "selectedGenes". Specify the type of display of genes selected, the possible values are: missing "fold" to access the genes selected for each fold (not available in certain case see section value for more details), "frequ" to access the genes order by their frequency among the folds(not available in certain case see section value for more details)
字符。可选的，被忽略，如果主题是不是"selectedGenes"。指定显示选定的基因类型，可能的值是："fold"访问每个倍（在某些情况下可看到更多的细节部分值）选择的基因，"frequ"访问基因顺序（而不是在某些情况下，可以看到更多的细节部分值）之间的褶皱，其频率
值----------Value----------
if there is no error, the value returned by the method depends on the arguments namely, layer, topic, errorType and genesType.
如果没有错误，该方法返回的值取决于参数，即layer，topic，errorType和genesType。
If layer is 1
如果layer1
参数：General
Get the results of the repeated one-layer cross-validation corresponding to the object of class assessment. If the one-layer cross-validation has not been performed and the user try to access it then the function return an error indicating that he must call runOneLayerExtCV first.
得到重复层交叉验证相应的object类评估结果。如果一个层交叉验证没有被执行，用户尝试访问它，则该函数返回一个错误，指示他必须调用runOneLayerExtCV第一。
参数：if topic is <code>"errorRate"</code>
参数：If errorType=<code>"all"</code> or is <code>missing</code>
All the following error rates
以下所有的错误率
参数：If errorType=<code>"cv"</code>
numeric. Cross-validated error-rate for each value of option tried obtained by one-layer of cross-validation (1 value per value of option).
numeric。交叉验证错误率，每个值试图层交叉验证（1％的期权价值价值）获得的选项。
参数：If errorType=<code>"se"</code>
numeric. Standard error on cross-validated error-rate for each value of option tried obtained by one-layer of cross-validation (1 value per value of option).
numeric。交叉验证错误率的标准误差为每个选项的值试图获得一个层交叉验证（1％的期权价值价值）。
参数：If errorType=<code>"class"</code>
numeric. Class cross-validated error rate error for each value of option tried obtained by one-layer of cross-validation (1 value per class and value of option).
数字。类交叉验证错误率误差为每个选项的值试图获得一个层交叉验证（1每班价值和期权价值）。
参数：Else
Error signaling that the topic is not appropriate.
错误的信号，是不恰当的话题。
参数：if topic is <code>"genesSelected"</code>
参数：If genesType=<code>"freq"</code> or is missing
list. Each elelement of the list corresponds to the genes selected for each model ordered by frequency.
list。列表中的每个elelement对应频率下令每个模型选择的基因。
参数：Else
Error signaling that the topic is not appropriate.
错误的信号，是不恰当的话题。
参数：if topic is <code>"bestOptionValue"</code>
Size of subset (for RFE-SVM) or threshold (for NSC) corresponding to the minimum cross-validated error rate.
大小（RFE-SVM）的子集或阈值（NSC）的相应的交叉验证错误率最低。
参数：if topic is <code>"executionTime"</code>
Time in second to perform this one-layer cross-validation.
在第二次执行层交叉验证。
If layer is c(1,i)
如果layerC（1，I）
参数：General
Get the results of the ith repeat of the one-layer cross-validation corresponding to the object of class assessment. If the one-layer cross-validation has not been performed and the user try to access it then the function return an error indicating that he must call runOneLayerExtCV first.
得到的第i层交叉验证相应的object类评估重复的结果。如果一个层交叉验证没有被执行，用户尝试访问它，则该函数返回一个错误，指示他必须调用runOneLayerExtCV第一。
参数：if topic is <code>"errorRate"</code>
参数：If errorType=<code>"all"</code> or is <code>missing</code>
All the following error rates
以下所有的错误率
参数：If errorType=<code>"cv"</code>
numeric. Cross-validated error-rate for each value of option tried obtained by one-layer of cross-validation on the ith repeat(1 value per subset).
数字。交叉验证错误率每个值试图层交叉验证在第i个重复，每个子集（1值）得到的选项。
参数：If errorType=<code>"se"</code>
numeric. Standard error on cross-validated error-rate for each value of option tried obtained by one-layer of cross-validation on the ith repeat (1 value per value of option).
数字。交叉验证错误率的标准误差为每个选项的值试图通过交叉验证的一个层上的第i个重复（1％的期权价值价值）。
参数：If errorType=<code>"class"</code>
numeric. Class cross-validated error rate error for each value of option tried obtained by one-layer of cross-validation on the ith repeat (1 value per class and value of option).
数字。类交叉验证错误率误差为每个选项的值试图通过交叉验证的一个层上的第i个重复（1每班价值和期权价值）。
参数：If errorType=<code>"fold"</code>
numeric. Class cross-validated error rate error for each fold and each value of option tried obtained by one-layer of cross-validation on the ith repeat (1 value per class and value of option).
数字。一类交叉验证错误率每个倍的错误和每个选项的值试图获得交叉验证的一个层上的第i个重复（1每班价值和期权价值）。
参数：Else
Error signaling that the topic is not appropriate.
错误的信号，是不恰当的话题。
参数：if topic is <code>"genesSelected"</code>
参数：If genesType=<code>"freq"</code> or is missing
list. Each elelement of the list corresponds to the genes selected for each model ordered by frequency.
列表。列表中的每个elelement对应频率下令每个模型选择的基因。
参数：If genesType=<code>"fold"</code>
list. Each elelement of the list corresponds to a model and contains a list of which one element correspond to the genes selected in a particular fold.
列表。列表中的每个elelement对应一个模型，并包含一个列表，其中一个元素对应的基因在一个特定的倍数选择。
参数：Else
Error signaling that the topic is not appropriate.
错误的信号，是不恰当的话题。
参数：if topic is <code>"bestOptionValue"</code>
numeric. Size of subset (for RFE) or threshold (for NSC) corresponding to the minimum cross-validated error rate in the ith repeat of the one-layer cross-validation.
数字。 （RFE）的子集的大小或阈值（NSC）的相应最低在一个层交叉验证的第i个重复交叉验证错误率。
参数：if topic is <code>"executionTime"</code>
Time in second to perform this repeat of one-layer cross-validation.
在第二次执行此一层交叉验证的重复。
If layer is 2
如果layer2
参数：General
Get the results of the repeated two-layers cross-validation corresponding to the object of class assessment. If the two-layer cross-validation has not been performed and the user try to access it then the function return an error indicating that he must call runTwoLayerExtCV first.
得到重复的两个层交叉验证相应的object类评估结果。两层交叉验证，如果没有被执行，用户尝试访问它，那么该函数返回一个错误，指示他必须调用runTwoLayerExtCV第一。
参数：if topic is 'errorRate'
参数：If errorType=<code>"all"</code> or is <code>missing</code>
All the following error rates
以下所有的错误率
参数：If errorType=<code>"cv"</code>
numeric. Cross-validated error-rate obtained by two-layers of cross-validation (1 value).
数字。交叉验证错误率两个交叉验证层（值为1）获得。
参数：If errorType=<code>"se"</code>
numeric. Standard error on cross-validated error-rate obtained by two-layers of cross-validation (1 value).
数字。交叉验证标准误差误码率得到交叉验证层（值为1）。
参数：If errorType=<code>"class"</code>
numeric. Class cross-validated error rate obtained by two-layers (1 value per class)
数字。类得到交叉验证错误率，由两个层（每班1值）
参数：Else
Error signaling that the topic is not appropriate.
错误的信号，是不恰当的话题。
参数：if topic is <code>"bestOptionValue"</code>
numeric. Average best number of genes for SVM-RFE of threshold for NSc obtained among the folds.
数字。褶皱之间取得最好的SVM-RFE平均基因NSC阈值。
参数：if topic is <code>"executionTime"</code>
Time in second to perform this two-layers cross-validation.
在第二次执行这两个层交叉验证。
If layer is c(2,i)
layer如果C（2，我）
参数：General
Get the results of the ith repeated of the two-layers cross-validation corresponding to the object of class assessment. If the two-layer cross-validation has not been performed and the user try to access it then the function return an error indicating that he must call runTwoLayerExtCV first.
得到重复第i层交叉验证对应object类评估的结果。两层交叉验证，如果没有被执行，用户尝试访问它，那么该函数返回一个错误，指示他必须调用runTwoLayerExtCV第一。
参数：if topic is 'errorRate'
参数：If errorType=<code>"all"</code> or is <code>missing</code>
All the following error rates
以下所有的错误率
参数：If errorType=<code>"cv"</code>
numeric. Cross-validated error-rate obtained by two-layers of cross-validation in this repeat. (1 value).
数字。交叉验证错误率两个层交叉验证在此重复获得。 （值为1）。
参数：If errorType=<code>"se"</code>
numeric. Standard error on cross-validated error-rate obtained by two-layers of cross-validation in this repeat (1 value).
数字。标准错误交叉验证错误率两个层在此重复交叉验证（值为1）获得。
参数：If errorType=<code>"class"</code>
numeric. Class cross-validated error rate obtained by two-layers in this repeat
数字。类交叉验证错误率，由两个层在此重复获得
参数：If errorType=<code>"fold"</code>
numeric. Error rate obtained on each of the folds in the second layer in this repeat(1 value per fold). of cross-validation (value per class).
数字。在这重复的第二层（1％倍的值），错误率得到每个褶皱。交叉验证的（每班价值）。
参数：Else
Error signaling that the topic is not appropriate.
错误的信号，是不恰当的话题。
参数：if topic is <code>"genesSelected"</code>
参数：If genesType=<code>"fold"</code> or is missing
list. Each elelement of the list corresponds to a fold and contains a list of the genes selected in this particular fold.
列表。列表中的每个elelement对应倍，并包含在这个特别的倍数选定的基因列表。
参数：Else
Error signaling that the topic is not appropriate.
错误的信号，是不恰当的话题。
参数：if topic is <code>"bestOptionValue"</code>
numeric. Average best number of genes obtained among the folds in this repeat.
数字。平均在此重复褶皱中获得的基因数目。
参数：if topic is <code>"executionTime"</code>
Time in second to perform this repeat of two-layers cross-validation.
在第二次执行这两个层交叉验证的重复。
参数：If <code>layer</code> is c(2,i,j)
This layer corresponds to the jth inner layer of one-layer cross-validation performed inside the ith repeat of the two-layers cross-validation. The returned values are similar to the one returned by a repeated one-layer cross-validation.
这一层对应一个层内的两个层交叉验证的第i个重复执行交叉验证的第j个内层。重复层交叉验证返回一个返回值是类似的。
参数：If <code>layer</code> is c(2,i,j,k)
This layer corresponds to the kth repeat of the jth inner layer of one-layer cross-validation performed inside the ith repeat. The returned values are similar to the one returned by a repeat of one-layer cross-validation.
这一层对应一个层内的第i个重复执行交叉验证的第j个内层的第k个重复。层交叉验证的重复返回一个返回值是类似的。
方法----------Methods----------
object = "assessment" The method is only applicable on objects of class
对象=“评估”的方法只适用于类对象
作者（S）----------Author(s)----------
Camille Maumet
参见----------See Also----------
assessment
assessment
举例----------Examples----------
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data")[数据通路（< - file.path的“C：”，“文件和设置”，“c.maumet”，“我的文档”，“Programmation”，“源”，“SVN的”，“R包”，“数据” ）]
#aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath)[aDataset < - 新（“数据集”，“dataId =”vantVeer_70，数据通路=数据通路）]
#aDataset <- loadData(aDataset)[aDataset < - 的LoadData（aDataset）]
data('vV70genesDataset')
mySubsets <- new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70))
myassessment <- new("assessment", dataset=vV70genes,
noFolds1stLayer=5,
noFolds2ndLayer=4,
classifierName="svm",
typeFoldCreation="original",
svmKernel="linear",
noOfRepeat=2,
featureSelectionOptions=mySubsets)
myassessment <- runOneLayerExtCV(myassessment)
myassessment <- runTwoLayerExtCV(myassessment)
# --- Access to one-layer CV ---[---访问到一个层CV - ]
# errorRate[errorRate]
# 1-layer CV: error Rates[1层的简历：错误率]
getResults(myassessment, 1, 'errorRate')
# 1-layer CV: error Rates - all")[1层CV：错误率 - 所有“）]
getResults(myassessment, 1, 'errorRate', errorType='all')
# 1-layer CV: error Rates - cv[1层CV：错误率 - CV]
getResults(myassessment, 1, 'errorRate', errorType='cv')
# 1-layer CV: error Rates - se[1层简历：错误率 - SE]
getResults(myassessment, 1, 'errorRate', errorType='se')
# 1-layer CV: error Rates - class[1层CV：错误率 - 类]
getResults(myassessment, 1, 'errorRate', errorType='class')
# genesSelected[genesSelected]
# 1-layer CV: genes Selected[1层CV：基因选择]
getResults(myassessment, 1, 'genesSelected')
# 1-layer CV: genes Selected - frequ[1层CV：基因选择 - frequ]
getResults(myassessment, 1, 'genesSelected', genesType='frequ')
# 1-layer CV: genes Selected - model 7[1层CV：基因选择 - 模型7]
getResults(myassessment, 1, 'genesSelected', genesType='frequ')[[7]]
getResults(myassessment, 1, 'genesSelected')[[7]]
# bestOptionValue[bestOptionValue]
# 1-layer CV: best number of genes[1层CV：最好的基因数目]
getResults(myassessment, 1, 'bestOptionValue')
# executionTime[executionTime]
# 1-layer CV: execution time[1层简历：执行时间]
getResults(myassessment, 1, 'executionTime')
# --- Access to 2nd repeat of one-layer CV ---[---访问到一个层CV第二次重复 - ]
# Error rates[错误率]
# 1-layer CV repeat 2: error Rates[1层CV重复2：错误率]
getResults(myassessment, c(1,2), 'errorRate')
# 1-layer CV repeat 2: error Rates - all[1层CV重复2：错误率 - 所有]
getResults(myassessment, c(1,2), 'errorRate', errorType='all')
# 1-layer CV repeat 2: error Rates - cv[1层CV重复2：错误率 - CV]
getResults(myassessment, c(1,2), 'errorRate', errorType='cv')
# 1-layer CV repeat 2: error Rates - se[1层CV重复2：错误率 - SE]
getResults(myassessment, c(1,2), 'errorRate', errorType='se')
# 1-layer CV repeat 2: error Rates - fold[1层CV重复2：错误率 - 倍]
getResults(myassessment, c(1,2), 'errorRate', errorType='fold')
# 1-layer CV repeat 2: error Rates - noSamplesPerFold[1层CV重复2：错误率 - noSamplesPerFold]
getResults(myassessment, c(1,2), 'errorRate', errorType='noSamplesPerFold')
# 1-layer CV repeat 2: error Rates - class[1层CV重复2：错误率 - 类]
getResults(myassessment, c(1,2), 'errorRate', errorType='class')
# genesSelected[genesSelected]
# 1-layer CV repeat 2: genes Selected[1层CV重复2：基因选择]
getResults(myassessment, c(1,2), 'genesSelected')
# 1-layer CV repeat 2: genes Selected - frequ[1层CV重复2：基因选择 - frequ]
getResults(myassessment, c(1,2), 'genesSelected', genesType='frequ')
# 1-layer CV repeat 2: genes Selected - model 7 (twice)[1层CV重复2：基因选择 - 模型7（两次）]
getResults(myassessment, c(1,2), 'genesSelected', genesType='frequ')[[7]]
getResults(myassessment, c(1,2), 'genesSelected')[[7]]
# 1-layer CV repeat 2: genes Selected - fold[1层CV重复2：基因选择 - 倍]
getResults(myassessment, c(1,2), 'genesSelected', genesType='fold')
# 1-layer CV repeat 2: best number of genes[1层CV重复2：最好的基因数目]
getResults(myassessment, c(1,2), 'bestOptionValue')
# 1-layer CV repeat 2: execution time[1层CV重复2：执行时间]
getResults(myassessment, c(1,2), 'executionTime')
# --- Access to two-layers CV ---[---访问到两个层CV - ]
# Error rates[错误率]
# 2-layer CV: error Rates[2层的简历：错误率]
getResults(myassessment, 2, 'errorRate')
# 2-layer CV: error Rates - all[简历：2层 - 所有的错误率]
getResults(myassessment, 2, 'errorRate', errorType='all')
# 2-layer CV: error Rates - cv[2层CV：错误率 - CV]
getResults(myassessment, 2, 'errorRate', errorType='cv')
# 2-layer CV: error Rates - se[简历：2层错误率 - SE]
getResults(myassessment, 2, 'errorRate', errorType='se')
# 2-layer CV: error Rates - class[2层CV：错误率 - 类]
getResults(myassessment, 2, 'errorRate', errorType='class')
# bestOptionValue[bestOptionValue]
# 2-layer CV: best number of genes (avg)[2层CV：最好的基因数量（AVG）]
getResults(myassessment, 2, 'bestOptionValue')
# executionTime[executionTime]
# 2-layer CV: execution time[2层的简历：执行时间]
getResults(myassessment, 2, 'executionTime')
# --- Access to two-layers CV access to repeats ---[---访问两个层次的简历到重复的访问---]
# Error rates[错误率]
# 2-layer CV repeat 1: error Rates[2层CV重复1：错误率]
getResults(myassessment, c(2,1), 'errorRate')
# 2-layer CV repeat 1: error Rates - all[2层CV重复1：错误率 - 所有]
getResults(myassessment, c(2,1), 'errorRate', errorType='all')
# 2-layer CV repeat 1: error Rates - cv[2层CV重复1：错误率 - CV]
getResults(myassessment, c(2,1), 'errorRate', errorType='cv')
# 2-layer CV repeat 1: error Rates - se[2层CV重复1：错误率 - SE]
getResults(myassessment, c(2,1), 'errorRate', errorType='se')
# 2-layer CV repeat 1: error Rates - fold[2层CV重复1：错误率 - 倍]
getResults(myassessment, c(2,1), 'errorRate', errorType='fold')
# 2-layer CV repeat 1: error Rates - noSamplesPerFold[2层CV重复1：错误率 - noSamplesPerFold]
getResults(myassessment, c(2,1), 'errorRate', errorType='noSamplesPerFold')
# 2-layer CV repeat 1: error Rates - class[2层CV重复1：错误率 - 类]
getResults(myassessment, c(2,1), 'errorRate', errorType='class')
# genesSelected[genesSelected]
# 2-layer CV repeat 1: genes Selected[2层CV重复1：选定的基因]
getResults(myassessment, c(2,1), 'genesSelected')
# 2-layer CV repeat 1: genes Selected - fold[2层CV重复1：基因选择 - 倍]
getResults(myassessment, c(2,1), 'genesSelected', genesType='fold')
# 2-layer CV repeat 1: best number of genes[2层CV重复1：最好的基因数目]
getResults(myassessment, c(2,1), 'bestOptionValue')
# 2-layer CV repeat 1: execution time[2层CV重复1：执行时间]
getResults(myassessment, c(2,1), 'executionTime')
# --- Access to one-layer CV inside two-layers CV ---[---访问一个层CV内两个层次的简历 - ]
# errorRate[errorRate]
# 2-layer CV repeat 1 inner layer 3: error Rates[2层CV重复1内层3：错误率]
getResults(myassessment, c(2,1,3), 'errorRate')
# 2-layer CV repeat 1 inner layer 3: error Rates - all[2层CV重复1内层3：错误率 - 所有]
getResults(myassessment, c(2,1,3), 'errorRate', errorType='all')
# 2-layer CV repeat 1 inner layer 3: error Rates - cv[2层CV重复1内层3：错误率 - CV]
getResults(myassessment, c(2,1,3), 'errorRate', errorType='cv')
# 2-layer CV repeat 1 inner layer 3: error Rates - se[2层CV重复1内层3：错误率 - SE]
getResults(myassessment, c(2,1,3), 'errorRate', errorType='se')
# 2-layer CV repeat 1 inner layer 3: error Rates - class[2层CV重复1内层3：错误率 - 类]
getResults(myassessment, c(2,1,3), 'errorRate', errorType='class')
# genesSelected[genesSelected]
# 2-layer CV repeat 1 inner layer 3: genes Selected[2层CV重复1内层3：选择基因]
getResults(myassessment, c(2,1,3), 'genesSelected')
# 2-layer CV repeat 1 inner layer 3: genes Selected - frequ[2层CV重复1内层3：基因选择 - frequ]
getResults(myassessment, c(2,1,3), 'genesSelected', genesType='frequ')
# 2-layer CV repeat 1 inner layer 3: genes Selected - model 7[2层CV重复1内层3：基因选择 - 模型7]
getResults(myassessment, c(2,1,3), 'genesSelected', genesType='frequ')[[7]]
getResults(myassessment, c(2,1,3), 'genesSelected')[[7]]
# bestOptionValue[bestOptionValue]
# 2-layer CV repeat 1 inner layer 3: best number of genes[2层CV重复1内层3：最好的基因数目]
getResults(myassessment, c(2,1,3), 'bestOptionValue')
# executionTime[executionTime]
# 2-layer CV repeat 1 inner layer 3: execution time[2层CV重复1内层3：执行时间]
getResults(myassessment, c(2,1,3), 'executionTime')
# --- two-layers CV access to repeat 1, inner layer 2 repeat 2 ---[--- 2层CV访问，重复1，内层2重复2 ---]
# Error rates[错误率]
# 2-layer CV inner layer 3 repeat 2: error Rates[2层CV内层3重复2：错误率]
getResults(myassessment, c(2,1,3,1), 'errorRate')
# 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - all[2层CV重复1内层3重复1：错误率 - 所有]
getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='all')
# 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - cv[2层CV重复1内层3重复1：错误率 - CV]
getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='cv')
# 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - se[2层CV重复1内层3重复1：错误率 - SE]
getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='se')
# 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - class[2层CV重复1内层3重复1：错误率 - 类]
getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='class')
# 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - fold[2层CV重复1内层3重复1：错误率 - 倍]
getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='fold')
# 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - noSamplesPerFold[2层CV重复1内层3重复1：错误率 - noSamplesPerFold]
getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='noSamplesPerFold')
# genesSelected[genesSelected]
# 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected[2层CV重复1内层3重复1：基因选择]
getResults(myassessment, c(2,1,3,1), 'genesSelected')
# 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected - fold[2层CV重复1内层3重复1：基因选择 - 倍]
getResults(myassessment, c(2,1,3,1), 'genesSelected', genesType='fold')
# 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected - model 3 fold 1(twice)[2层CV重复1内层3重复1：基因选择 - 模型3倍（两次）]
getResults(myassessment, c(2,1,3,1), 'genesSelected', genesType='fold')[[3]][[1]]
# 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected frequ - model 3[2层CV重复1内层3重复1：基因选择frequ - 模型3]
getResults(myassessment, c(2,1,3,1), 'genesSelected')[[3]]
# 2-layer CV repeat 1 inner layer 3 repeat 1: best number of genes[2层CV重复1内层3重复1：最好的基因数目]
getResults(myassessment, c(2,1,3,1), 'bestOptionValue')
# 2-layer CV repeat 1 inner layer 3 repeat 1: execution time[2层CV重复1内层3重复1：执行时间]
getResults(myassessment, c(2,1,3,1), 'executionTime')
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-2-26 13:01:30
