wilcox.split(WilcoxCV)
wilcox.split()所属R语言包：WilcoxCV
Wilcoxon rank sum statistic in cross-validation (CV) and Monte-Carlo cross-validation (MCCV)
Wilcoxon秩和统计数据的交叉验证（CV）和蒙特卡洛交叉验证（MCCV）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function wilcox.split computes the Wilcoxon rank sum statistic for all niter CV or MCCV iterations defined by the matrix split.
函数wilcox.split Wilcoxon秩和统计计算所有的niter CV或MCCV的迭代定义的矩阵split。
用法----------Usage----------
wilcox.split(x,y,split,algo="new")
参数----------Arguments----------
参数：x
a numeric vector of length n giving the expression levels of a gene for the n arrays.
一个数值向量长度为n的给予的n阵列的基因的表达水平。
参数：y
a vector of length n giving the class membership for the n arrays. y can be either a factor or a numeric and must be coded as 0,1.
一个向量长度为n的给类成员为n的数组。 y可以是一个因素或一个数字，并且必须被编码为0,1。
参数：split
A niter x ntest matrix giving the indices of the ntest observations included in each of the niter test sets, as generated by the functions generate.split or generate.cv. The i-th row of split gives the indices of the observations included in the test data set for the i-th iteration.
AniterXntest矩阵给ntest意见包括niter测试集，每个指标，所产生的功能generate.split或 X>。的第i个行generate.cv给人的指数，其中包括设置为测试数据中的第i次迭代的观测。
参数：algo
either "new" or "naive". If algo="new", the new fast method described in Boulesteix (2007) is used to compute the Wilcoxon rank statistic. If algo="naive", the Wilcoxon rank sum statistics are obtained by running the function wilcox.test niter times.
是"new"或"naive"。如果algo="new"，在Boulesteix新的快速方法（2007年）来计算的Wilcoxon符号秩统计。如果algo="naive"，Wilcoxon秩和统计，得到运行的功能wilcox.testniter倍。
Details
详细信息----------Details----------
The Wilcoxon rank sum statistic is defined as the sum of the X-ranks of the observations with y=0. The Wilcoxon rank sum test is equivalent to the Mann-Whitney test. It is implemented in the function wilcox.test.
Wilcoxon秩和统计定义的意见与y=0X-行列的总和。 Wilcoxon秩和检验Mann-Whitney检验。这是实施的功能wilcox.test。
In the context of cross-validation (CV) or Monte-Carlo cross-validation (MCCV), wilcox.selection.split computes the Wilcoxon rank sum statistic for each iteration. At each iteration, a subset of the n observations is excluded from the data set and considered as test data set. The indices of the observations considered as test set for each of the niter iterations are given in the niter x ntest matrix split.
在交叉验证（CV）或蒙特卡罗交叉验证（MCCV）的情况下，wilcox.selection.split每一次迭代计算Wilcoxon秩和统计。在每次迭代中，一个子集的n观测排除从作为测试数据集的数据集，并认为。该指数的意见视为测试的每个niter的迭代在niterXntest矩阵split。
值----------Value----------
A list with the following components: <table summary="R valueblock"> <tr valign="top"><td>wilcox.split</td> <td> a numeric vector of length niter whose i-th component gives the Wilcoxon rank sum statistic obtained in the i-th iteration.</td></tr>
有以下组件的列表：<table summary="R valueblock"> <tr valign="top"> <TD> wilcox.split </ TD> <td>一个数字矢量的长度niter的第i个分量给出Wilcoxon秩和统计量中得到的第i次迭代。</ TD> </ TR>
</table>
</ TABLE>
（作者）----------Author(s)----------
Anne-Laure Boulesteix (<a href="http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/index.html">http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/index.html</a>)
参考文献----------References----------
A. L. Boulesteix (2007). WilcoxCV: an R package for fast variable selection in cross-validation. Bioinformatics 23:1702-1704.
参见----------See Also----------
wilcox.test, generate.split, generate.cv, wilcox.selection.split
wilcox.test，generate.split，generate.cv，wilcox.selection.split
实例----------Examples----------
# load WilcoxCV library[加载WilcoxCV库]
library(WilcoxCV)
# Generate data[生成数据]
x<-rnorm(100)
y<-sample(c(0,1),100,replace=TRUE)
# Generate 50 MCCV splits with ratio 2:1 for a data set including 90 observations[50 MCCV比为2:1分割的数据集包括90个观察]
my.split<-generate.split(niter=50,n=90,ntest=30)
# Compute the Wilcoxon rank sum statistic for the 50 iterations.[Wilcoxon秩和统计计算的50次。]
wilcox.split(x=x,y=y,split=my.split,algo="new")
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。
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发表于 2012-10-1 22:31:23
