wilcox.selection.split(WilcoxCV)
wilcox.selection.split()所属R语言包：WilcoxCV
Wilcoxon-based variable selection in cross-validation (CV) and Monte-Carlo cross-validation (MCCV)
的魏氏基于变量选择交叉验证（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.selection.split performs variable ordering based on the Wilcoxon rank sum test for all niter CV or MCCV iterations.
的功能wilcox.selection.split执行变量排序的基础上Wilcoxon秩和检验的所有niter的CV或MCCV迭代。
用法----------Usage----------
wilcox.selection.split(x,y,split,algo="new",pvalue=FALSE)
参数----------Arguments----------
参数：x
a matrix or a data frame of size n x p giving the expression levels of the p variables (genes) for the n observations (arrays). Variables correspond to columns, observations to rows.
一个矩阵或一个数据框的大小非负的n个观测（阵列）发出的p个变量（基因）的表达水平。变量对应的列，观测到行。
参数：y
a vector of length n giving the class membership for the n observations (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的给类成员的N个观测值（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 random splitting 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给出的意见的指数，其中包括设置为测试数据中的第i个随机分裂迭代。
参数：algo
either "new" or "naive". If type="new", the new fast method described in Boulesteix (2007) is used. If type="naive", results are obtained by running the function wilcox.test niter times.
是"new"或"naive"。如果type="new"，使用新的快速方法中描述的Boulesteix（2007）。如果type="naive"，结果，得到运行的功能wilcox.testniter倍。
参数：pvalue
Logical. Should p-values be returned?
逻辑。如果p值回来了吗？
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, for each variable. 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.
的背景下，交叉验证（CV）或蒙特卡罗交叉验证（MCCV），wilcox.selection.split每一次迭代计算Wilcoxon秩和统计，对于每个变量。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>ordering.split</td> <td> A niter x p matrix giving the indices of the genes ordered by pvalue. For example, the first column of ordering.split gives the index of the variable with lowest pvalue in each of the niter random splitting iterations, the second column of ordering.split gives the index of the variable with the second lowest pvalue in each of the niter random splitting iterations. For the i-th iteration, the indices of the 50 best variables are given in the 50 first columns of row i.</td></tr> <tr valign="top"><td>pvalue.split</td> <td> Returned only if pvalue=TRUE. A niter x p matrix of pvalues. The element in the i-th row and j-th column is the pvalue of variable j in the i-th iteration.</td></tr>
以下组件的列表：<table summary="R valueblock"> <tr valign="top"> <TD> ordering.split</ TD> <TD> Aniter XP矩阵给指数的基因排列的由P值。例如，第一列ordering.split给出最低的P值在每个niter随机分裂迭代ordering.split给出的索引的变量，第二列的索引的变量第二最低P值在每个niter随机劈裂迭代。对于第i个迭代的50个最好的变量，指数在50行的第一列。</ TD> </ TR> <tr valign="top"> <TD>pvalue.split< / TD> <TD>的只有pvalue=TRUE返回。 AniterX p矩阵的pvalues。变量j中的i次迭代的第i行和第j列中的元素是P值</ 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.split
wilcox.test，generate.split，generate.cv，wilcox.split
实例----------Examples----------
# load WilcoxCV library[加载WilcoxCV库]
library(WilcoxCV)
# Generate data[生成数据]
x<-matrix(rnorm(1000),100,10)
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.selection.split(x=x,y=y,split=my.split,algo="new",pvalue=TRUE)
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 22:31:14
