twilight.filtering(twilight)
twilight.filtering()所属R语言包：twilight
Permutation filtering
置换过滤
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function call invokes the filtering for permutations of class labels that produce a set of complete null scores. Depending on the test setting, the algorithm iteratively generates valid permutations of the class labels and computes scores. These are transformed to pooled p-values and each set of permutation p-values is tested for uniformity. Permutations with acceptable uniform p-value distributions are kept. The search stops if either the number num.perm of wanted permutations is reached or if the number of possible unique(!) permutations is smaller than num.perm. The default values are similar to function twilight.pval but please note the details below.
函数调用，调用类的标签，产生了一套完整的空分数排列的过滤。根据测试的设置，该算法迭代产生有效之类的标签排列，并计算分数。这些转化到汇集p值和均匀性测试每一组排列p值。接受统一的p值分布排列将被保留。如果数量num.perm通缉排列达到或可能唯一的（！）排列数，如果是小比num.perm搜索停止。默认值是类似的运作twilight.pval但请注意以下细节。
用法----------Usage----------
twilight.filtering(xin, yin, method = "fc", paired = FALSE, s0 = 0, verbose = TRUE, num.perm = 1000, num.take = 50)
参数----------Arguments----------
参数：xin
Either an expression set (ExpressionSet) or a data matrix with rows corresponding to features and columns corresponding to samples.
表达式集（ExpressionSet），或与相应样品的特点和列对应的行数据矩阵。
参数：yin
A numerical vector containing class labels. The higher label denotes the case, the lower label the control samples to test case vs. control. For correlation scores, yin can be any numerical vector of length equal to the number of samples.
数值向量类的标签。较高的标签表示的情况下，较低的标签控制样品测试的情况下与控制。相关分数，yin可以是任何长度等于样本数的数值向量。
参数：method
Character string: "fc" for fold change equivalent test (that is log ratio test), "t" for t-test, and "z" for Z-test. With "pearson" or "spearman", the test statistic is either Pearson's correlation coefficient or Spearman's rank correlation coefficient.
字符串："fc"倍等效试验（即log比试验），"t"t检验，"z"Z检验。 "pearson"或"spearman"，检验统计量，要么是Pearson相关系数或Spearman秩相关系数。
参数：paired
Logical value. Depends on whether the samples are paired. Ignored if method="pearson" or method="spearman".
逻辑值。取决于是否配对样本。如果method="pearson"或method="spearman"忽略。
参数：s0
Fudge factor for variance correction in the Z-test. Takes effect only if method="z". If s0=0: The fudge factor is set to the median of the pooled standard deviations.
在Z-测试校正方差忽悠因素。只有生效method="z"。如果s0=0：蒙混因素汇集的标准偏差的中位数。
参数：verbose
Logical value for message printing.
消息打印的逻辑值。
参数：num.perm
Number of permutations. Within twilight.pval, num.perm is set to B.
排列数。内twilight.pval，num.perm设置为B。
参数：num.take
Number of permutations kept in each step of the iterative filtering. Within twilight.pval, num.take is set to the minimum of 50 and ceiling(num.perm/20).
排列的数量保持在每一步的反复筛选。内twilight.pval，num.take设置为最小的50和ceiling(num.perm/20)。
Details
详情----------Details----------
See vignette.
看到小插曲。
值----------Value----------
Returns a matrix with permuted input labels yin as rows. Please note that this matrix is already translated into binary labels for two-sample testing or to ranks if Spearman's correlation was chosen. The resulting permutation matrix can be directly passed into function twilight.pval. Please note that the first row always contains the original input yin to be consistent with the other permutation functions in package twilight.
返回置换输入标签yin作为行矩阵。请注意这个矩阵已经Spearman相关被选为如果换算成两样本测试或职级的二进制标签。置换矩阵可以直接传递到功能twilight.pval。请注意，第一行始终包含原始输入yin是与其他包twilight置换功能一致。
作者（S）----------Author(s)----------
Stefanie Scheid <a href="http://www.molgen.mpg.de/~scheid">http://www.molgen.mpg.de/~scheid</a>
参考文献----------References----------
参见----------See Also----------
twilight.pval
twilight.pval
举例----------Examples----------
## Not run: [＃无法运行：]
### Leukemia data set of Golub et al. (1999).[＃白血病的数据Golub等。 （1999年）。]
library(golubEsets)
data(Golub_Train)
### Variance-stabilizing normalization of Huber et al. (2002).[＃方差稳定标准化胡贝尔等。 （2002年）。]
library(vsn)
golubNorm <- justvsn(Golub_Train)
### A binary vector of class labels.[＃＃一个二进制向量类的标签。]
id <- as.numeric(Golub_Train$ALL.AML)
### Do an unpaired t-test.[＃做配对t检验。]
### Let's have a quick example with 50 filtered permutations only.[＃＃让我们用50只过滤排列的简单的例子。]
### With num.take=10, we only need 5 iteration steps.[随着num.take = 10＃，我们只需要5迭代步骤。]
yperm <- twilight.filtering(golubNorm,id,method="t",num.perm=50,num.take=10)
dim(yperm)
### Let's check that the filtered permutations really produce uniform p-value distributions.[＃＃让我们检查，过滤排列真正产生统一的p值分布。]
### The first row is the original labeling, so we try the second permutation.[＃第一行是原来的标签，所以我们尝试第二次置换。]
yperm <- yperm[-1,]
b <- twilight.pval(golubNorm,yperm[1,],method="t",yperm=yperm)
hist(b$result$pvalue)
## End(Not run)[＃结束（不运行）]
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 15:51:48
