MCRestimate(MCRestimate)
MCRestimate()所属R语言包：MCRestimate
Estimation of misclassification error by cross-validation
通过交叉验证的误判错误的估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Several repetitions of a cross-validation are performed to get 'votes' how stable a method is against different partitions into
重复几次进行了交叉验证得到票，如何稳定的一个方法是针对不同的分区
用法----------Usage----------
MCRestimate(eset,
class.column,
reference.class=NULL,
classification.fun,
variableSel.fun="identity",
cluster.fun="identity",
poss.parameters=list(),
cross.outer=10,
cross.repeat=3,
cross.inner=cross.outer,
plot.label=NULL,
rand=123,
stratify=FALSE,
information=TRUE,
block.column=NULL,
参数----------Arguments----------
参数：eset
an object of class ExpressionSet
一个对象类ExpressionSet
参数：class.column
a number or a character string which indicates the column of the expression set's phenodata containing the class label
一个数字或字符串，表示表达式集的phenodata，列包含类的标签
参数：reference.class
a character string - the name of one class - if specified, the class will form the first class and all the other classes will form the second class
字符串 - 一类的名称 - 如果指定的类将成为第一类和其他类，将形成第二类
参数：classification.fun
character string which names the function that should be used for the classification
字符串命名应使用功能分类
参数：variableSel.fun
character string which names the function that should be used for the variable selection
字符串命名变量的选择，应使用功能
参数：cluster.fun
character string which names the function that should be used for the clustering of variables
字符串命名应为聚类变量的使用功能
参数：thePreprocessingMethods
vector of character with the names of all preprocessing functions - can be used instead of 'variableSel.fun' and 'cluster.fun' - see details
向量预处理功能的所有字符的名称 - 可以用来代替的“variableSel.fun和cluster.fun - 见详情
参数：poss.parameters
a list of possible values for the parameter of the classification, variable selection, and cluster methods
分类的参数，变量选择和聚类方法，为可能值的列表
参数：cross.outer
integer - the number of nearly equal sized parts the sample set should be divided into (outer cross-validation)
整数 - 应分为几乎相等的样本集的大小部分（外交叉验证）
参数：cross.repeat
integer - the number of repetitions of the cross-validation procedure
整数 - 重复交叉验证程序
参数：cross.inner
integer - the number of nearly equal sized parts the train set should be divided into (inner cross-validation)
整数 - 列车组的人数几乎相等大小的零件应分为（内部交叉验证）
参数：plot.label
name of one column of the phenodata- if specified, the content of this column will form the labels of the x-axis if the 'votematrix' will be plotted with plot.MCRestimate
名称的一列phenodata如果指定，本专栏的内容将形成x轴的标签，如果“votematrix”将绘制plot.MCRestimate
参数：rand
integer - the random number generator will be put in a reproducible state
整数 - 随机数发生器将重现状态
参数：stratify
should a stratified version be used for the cross validation?
应分层版本可用于交叉验证？
参数：block.column
a character string which indicates the column of the expression set's phenodata containing the blocking covariate, which sets a constrain on the cross-validation splits: each block is either completely assigned to the test or to the training set
这表明表达组的阻塞协变量，设置一个限制的交叉验证phenodata列字符串分割：每块被完全分配给测试或训练集
参数：information
information - should classifier specific data be given(depends on the wrapper for the classification method)
信息 - 应分类的具体数据（取决于包装的分类方法）
Details
详情----------Details----------
The argument 'thePreprocessingMethods' can be used instead of 'variableSel.fun' and 'cluster.fun'. In the first versions of MCRestimate it was only possible to have one variable selection and one cluster functions. Now it is possible to have more than two functions and the ordering is arbitrary, e.g. you can have a variable selection function, then a cluster function and then a second variable selection function.
参数thePreprocessingMethods“可以用来代替”variableSel.fun和cluster.fun。在MCRestimate的第一个版本，它是唯一可能有一个变量选择和一个聚类功能。现在可以有两个以上的功能和顺序是任意的，例如：你可以有一个变量的选择功能，然后聚类功能，然后第二个变量的选择功能。
If MCRestimate is used with an object of class exprSetRG-class, the preprocessing steps can use the green and the red channel separately but the classification methods works with green channel - red channel.
，如果MCRestimate一个类的对象使用exprSetRG-class，预处理步骤可以使用绿色和红色通道分开，但与绿色通道工程的分类方法 - 红色通道。
Note: 'correct prediction' means that a sample was predicted to be a member of the correct class at least as often as it was predicted to be a member of each other class. So in the two class problem a sample is also 'correct' if it has been predicted correctly half of the time.
注：“正确预测”是指一个样本进行了预测，至少在经常有人预言是其他类的每个成员是一个正确的类成员。所以样品在两个类的问题，也是“正确的”，如果它已被正确预测半的时间。
值----------Value----------
an object of class MCRestimate which is a list with fourteen arguments:
一个对象类MCRestimate这是1list有14个参数：
参数：votes
a matrix consisting of the different votes for each sample
每个样品的不同票组成一个矩阵
参数：classes
the class of each sample
每个样品的类
参数：table
a 'confusion' table, shows the number of 'correct prediction' for each class
“混乱”表，“正确预测”显示，每班人数
参数：correct.prediction
a logical vector - indicates if a sample was predicted to be a member of the correct class at least as often as it was predicted to be a member of each other class.
逻辑的向量 - 表示如果一个样本进行了预测，至少在经常有人预言是其他类的每个成员是一个正确的类成员。
参数：correct.class.vote
vector that contains for every sample the vote for it's correct class
向量，每个样本包含了它的正确的类的投票
参数：parameter
a list consisting of the estimated 'best' parameter for each cross-validation part
每个交叉验证的一部分组成的估计最好的参数列表
参数：class.method
string which names the function used for the classification
字符串命名的分类使用的功能
参数：thePreprocessingMethods
character string - name of the preprocessing functions that have been used
字符串 - 已使用的预处理功能的名称
参数：cross.outer
number of blocks for a the outer cross-validation
数块的外部交叉验证
参数：cross.repeat
number of outer cross-validation repetitions
数外重复交叉验证
参数：cross.inner
number of blocks for a the inner cross-validation
数块的内部交叉验证
参数：sample.names
names of the sample
样品名称
参数：information
classifier specific data (if information is TRUE)
具体的数据分类（如果信息是真实的）
作者（S）----------Author(s)----------
Markus Ruschhaupt <a href="mailto:m.ruschhaupt@dkfz.de">mailto:m.ruschhaupt@dkfz.de</a>,
contributions from Andreas Buness and Patrick Warnat
举例----------Examples----------
library(golubEsets)
data(Golub_Test)
G2 <- Golub_Test[1:500,]
result <- MCRestimate(G2, "ALL.AML", classification.fun="RF.wrap",
cross.outer=4, cross.repeat=3)
result
if (interactive()) {
x11(width=9, height=4)}
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。
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发表于 2012-2-26 00:35:21
