classifyTests(limma)
classifyTests()所属R语言包：limma
Multiple Testing Genewise Across Contrasts
跨多对比测试2-6。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For each gene, classify a series of related t-statistics as up, down or not significant.
对于每一个基因，列为了一系列相关的t-统计，或不显着。
用法----------Usage----------
classifyTestsF(object, cor.matrix=NULL, df=Inf, p.value=0.01, fstat.only=FALSE)
classifyTestsT(object, t1=4, t2=3)
classifyTestsP(object, df=Inf, p.value=0.05, method="holm")
FStat(object, cor.matrix=NULL)
参数----------Arguments----------
参数：object
numeric matrix of t-statistics or an MArrayLM object from which the t-statistics may be extracted.
t-统计量或一个MArrayLMt-统计对象，从中提取的数字矩阵。
参数：cor.matrix
covariance matrix of each row of t-statistics. Defaults to the identity matrix.
t-统计量的每一行的协方差矩阵。默认的身份矩阵。
参数：df
numeric vector giving the degrees of freedom for the t-statistics. May have length 1 or length equal to the number of rows of tstat.
提供数字矢量为t-统计量的自由度。可能有长度1或长度等于tstat行的数目。
参数：p.value
numeric value between 0 and 1 giving the desired size of the test
0和1之间的数值，测试所需的大小
参数：fstat.only
logical, if TRUE then return the overall F-statistic as for FStat instead of classifying the test results
逻辑，如果TRUE然后FStat，而不是分类测试结果返回F-统计的整体
参数：t1
first critical value for absolute t-statistics
第一绝对t-统计量的临界值
参数：t2
second critical value for absolute t-statistics
第二绝对t-统计量的临界值
参数：method
character string specifying p-value adjustment method. See p.adjust for possible values.
字符串指定的P-值调整的方法。看到p.adjust可能的值。
Details
详情----------Details----------
Note that these functions do not adjust for multiple testing across genes. The adjustment for multiple testing is across the contrasts rather than the more usual control across genes. The functions described here are called by decideTests. Most users should use decideTests rather than using these functions directly.
请注意，这些功能不调整整个基因的多个测试。的调整是跨多个测试，而不是更常见的整个基因控制的对比。这里所描述的功能，被称为decideTests。大多数用户应该使用decideTests，而不是直接使用这些功能。
These functions implement multiple testing procedures for determining whether each statistic in a matrix of t-statistics should be considered significantly different from zero. Rows of tstat correspond to genes and columns to coefficients or contrasts.
这些功能实现多个测试程序，确定每一个矩阵t-统计量的统计是否应该被认为是显着异于零。 tstat行对应的基因和列系数或对比。
FStat computes the gene-wise F-statistics for testing all the contrasts equal to zero. It is equivalent to classifyTestsF with fstat.only=TRUE.
FStat计算基因明智的F-统计等于零的所有对比测试。它是相当于classifyTestsF与fstat.only=TRUE。
classifyTestsF uses a nested F-test approach giving particular attention to correctly classifying genes which have two or more significant t-statistics, i.e., are differential expressed under two or more conditions. For each row of tstat, the overall F-statistics is constructed from the t-statistics as for FStat. At least one constrast will be classified as significant if and only if the overall F-statistic is significant. If the overall F-statistic is significant, then the function makes a best choice as to which t-statistics contributed to this result. The methodology is based on the principle that any t-statistic should be called significant if the F-test is still significant for that row when all the larger t-statistics are set to the same absolute size as the t-statistic in question.
classifyTestsF使用一个嵌套的F-检验的方法，特别注意正确分类的基因有两个或两个以上的显着t-统计量，即两个或两个以上的条件下，都差表示。对于每一个行tstat，整体构造F统计FStatt-统计。至少有一个constrast将被列为显著，当且仅当整体的F-统计显着。如果整体的F-统计显着，则该函数为t-统计促成这个结果的最佳选择。该方法是基于对任何t-统计应该被称为重要的，如果F检验仍是所有较大的t-统计时被设置为t-统计的问题，以相同的绝对大小的行具有重要意义的原则。
classifyTestsT and classifyTestsP implement simpler classification schemes based on threshold or critical values for the individual t-statistics in the case of classifyTestsT or p-values obtained from the t-statistics in the case of classifyTestsP. For classifyTestsT, classifies any t-statistic with absolute greater than t2 as significant provided that at least one t-statistic for that gene is at least t1 in absolute value. classifyTestsP applied p-value adjustment from p.adjust to the p-values for each gene.
classifyTestsT和classifyTestsP实现简单的分类的基础上的在classifyTestsT或从t-统计量的情况下取得的p值的情况下，个别t-统计的阈值或临界值计划classifyTestsP。 classifyTestsT，分类与任何t-统计量绝对比t2提供，该基因至少有一个t-统计是重要的，至少t1绝对值。 classifyTestsP应用p值从p.adjust调整每个基因的P-值。
If tstat is an MArrayLM object, then all arguments except for p.value are extracted from it.
如果tstatMArrayLM对象，然后除p.value从中提取所有参数。
cor.matrix is the same as the correlation matrix of the coefficients from which the t-statistics are calculated. If cor.matrix is not specified, then it is calculated from design and contrasts if at least design is specified or else defaults to the identity matrix. In terms of design and contrasts, cor.matrix is obtained by standardizing the matrix t(contrasts) %*% solve(t(design) %*% design) %*% contrasts to a correlation matrix.
cor.matrix从t-统计量的计算系数的相关系数矩阵是相同的。如果cor.matrix没有被指定，那么它的计算design和contrasts如果至少design指定或其他默认的身份矩阵。 design和contrasts，cor.matrix规范相关矩阵的矩阵 t(contrasts) %*% solve(t(design) %*% design) %*% contrasts 。
值----------Value----------
An object of class TestResults. This is essentially a numeric matrix with elements -1, 0 or 1 depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive respectively.
对象类TestResults。这实质上是一种数字矩阵元素-1，0或1取决于是否每个t-统计显着的负向，不显着或显着的正向分别归类。
FStat produces a numeric vector of F-statistics with attributes df1 and df2 giving the corresponding degrees of freedom.
FStat生产的F-统计的数字矢量与属性df1和df2给予相应程度的自由。
作者（S）----------Author(s)----------
Gordon Smyth
参见----------See Also----------
An overview of multiple testing functions is given in 08.Tests.
多种测试功能概述在08.Tests给出。
举例----------Examples----------
tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
classifyTestsF(tstat)
# See also the examples for contrasts.fit and vennDiagram[看到也为contrasts.fit和vennDiagram的例子]
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 2012-2-25 23:13:01
