mt.rawp2adjp(multtest)
mt.rawp2adjp()所属R语言包：multtest
Adjusted p-values for simple multiple testing procedures
调整后的P-值简单的多个测试程序
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes adjusted p-values for simple multiple testing procedures from a vector of raw (unadjusted) p-values. The procedures include the Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for strong control of the family-wise Type I error rate (FWER), and the Benjamini & Hochberg (1995) and Benjamini & Yekutieli (2001) procedures for (strong) control of the false discovery rate (FDR). The less conservative adaptive Benjamini & Hochberg (2000) and two-stage Benjamini & Hochberg (2006) FDR-controlling procedures are also included.
此函数计算调整p多个测试程序简单，从原料（未经调整）p的值的向量值。该程序包括的邦弗朗尼，霍尔姆（1979），Hochberg（1988），和控制能力强的家庭明智的类型错误率（FWER），Benjamini的Hochberg（1995年）和Benjamini及Yekutieli（Sidak程序2001年）的程序（强）的错误发现率（FDR）的控制权。不太保守的自适应Benjamini＆Hochberg（2000）和两阶段Benjamini＆Hochberg（2006）FDR的控制程序也包括在内。
用法----------Usage----------
mt.rawp2adjp(rawp, proc=c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD",
"BH", "BY","ABH","TSBH"), alpha = 0.05, na.rm = FALSE)
参数----------Arguments----------
参数：rawp
A vector of raw (unadjusted) p-values for each hypothesis under consideration. These could be nominal p-values, for example, from t-tables, or permutation p-values as given in mt.maxT and mt.minP. If the mt.maxT or mt.minP functions are used, raw p-values should be given in the original data order, rawp[order(index)].
原料（未经调整）p假设所考虑的每个值向量。这可能是名义p值，例如从t表，或置换p值mt.maxT和mt.minP的。如果mt.maxT或mt.minP使用功能，原料p值应在原始数据的顺序，rawp[order(index)]。
参数：proc
A vector of character strings containing the names of the multiple testing procedures for which adjusted p-values are to be computed. This vector should include any of the following: "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH", "TSBH".<br> Adjusted p-values are computed for simple FWER- and FDR- controlling procedures based on a vector of raw (unadjusted) p-values by one or more of the following methods:
一个矢量字符的字符串包含多个测试程序调整p值计算的名称。这个向量应包括以下任何："Bonferroni"，"Holm"，"Hochberg"，"SidakSS"，"SidakSD"，"BH"，"BY" "ABH"，"TSBH"调整参考p值计算简单FWER的和FDR的控制程序的基础上的矢量原料（未经调整）p - 值由一个或多个下列方法：
BonferroniBonferroni single-step adjusted p-values for strong control of the FWER.
BonferroniBonferroni单步调整p的FWER强大的控制值。
HolmHolm (1979) step-down adjusted p-values for strong control of the FWER.
HolmHolm（1979）降压调整p的FWER强大的控制值。
Hochberg Hochberg (1988) step-up adjusted p-values for strong control of the FWER (for raw (unadjusted) p-values satisfying the Simes inequality).
Hochberg hochberg（1988）步的调整p的FWER的控制能力强值（原始（未经调整）p值满足的Simes不平等）。
SidakSSSidak single-step adjusted p-values for strong control of the FWER (for positive orthant dependent test statistics).
SidakSSSidak单步调整p的FWER的控制能力强值（积极orthant依赖测试统计）。
SidakSDSidak step-down adjusted p-values for strong control of the FWER (for positive orthant dependent test statistics).
SidakSDSidak降压调整p的FWER强大的控制值（积极orthant依赖测试统计）。
BHAdjusted p-values for the Benjamini & Hochberg (1995) step-up FDR-controlling procedure (independent and positive regression dependent test statistics).
BHAdjustedp值的Benjamini Hochberg（1995）升压FDR控制程序（独立和积极的回归依赖测试统计）。
BYAdjusted p-values for the Benjamini & Yekutieli (2001) step-up FDR-controlling procedure (general dependency structures).
BYAdjustedp值的Benjamini Yekutieli升压FDR控制程序（一般依赖结构）（2001）。
ABHAdjusted p-values for the adaptive Benjamini & Hochberg (2000) step-up FDR-controlling procedure. This method ammends the original step-up procedure using an estimate of the number of true null hypotheses obtained from p-values.
ABHAdjustedp值的自适应Benjamini Hochberg（2000）升压FDR控制程序。这种方法ammends原来的升压过程，使用的从p值获得真正的零假设的估计数。
TSBHAdjusted p-values for the two-stage Benjamini & Hochberg (2006) step-up FDR-controlling procedure. This method ammends the original step-up procedure using an estimate of the number of true null hypotheses obtained from a first-pass application of "BH". The adjusted p-values are a-dependent, therefore alpha must be set in the function arguments when using this procedure.
TSBHAdjustedp值的两阶段Benjamini Hochberg（2006）升压FDR控制程序。这种方法ammends原始步骤程序使用从"BH"第一遍应用获得真正的零假设的估计数。调整后的p值a依赖，因此alpha必须在函数的参数设置时，使用此程序。
参数：alpha
A nominal type I error rate, or a vector of error rates, used for estimating the number of true null hypotheses in the two-stage Benjamini & Hochberg procedure ("TSBH"). Default is 0.05.
名义I型错误率，或错误率的向量，真正的零假设的数量估计在两个阶段Benjamini Hochberg程序（"TSBH"）。默认值是0.05。
参数：na.rm
An option for handling NA values in a list of raw p-values. If FALSE, the number of hypotheses considered is the length of the vector of raw p-values. Otherwise, if TRUE, the number of hypotheses is the number of raw p-values which were not NAs.
处理NA值在原料p值的列表选项。如果FALSE，认为假说是原料p值向量的长度。否则，如果TRUE，假说是原材料p值NA的数量。
值----------Value----------
A list with components:
与组件列表：
参数：adjp
A matrix of adjusted p-values, with rows corresponding to hypotheses and columns to multiple testing procedures. Hypotheses are sorted in increasing order of their raw (unadjusted) p-values.
一个调整p值的矩阵，行相应的假设和列多个测试程序。假设是在越来越多的原料（未经调整）p值的顺序排序。
参数：index
A vector of row indices, between 1 and length(rawp), where rows are sorted according to their raw (unadjusted) p-values. To obtain the adjusted p-values in the original data order, use adjp[order(index),].
一个行指数的向量，介于1和length(rawp)，根据它们的原始（未经调整）p的值的行进行排序。要获得原始数据的顺序调整p值，使用adjp[order(index),]。
参数：h0.ABH
The estimate of the number of true null hypotheses as proposed by Benjamini & Hochberg (2000) used when computing adjusted p-values for the "ABH" procedure (see Dudoit et al., 2007).
真正的零假设的数量估计，由Benjamini Hochberg（2000）提出的用于计算时调整p"ABH"程序值（见Dudoit等，2007）。
参数：h0.TSBH
The estimate (or vector of estimates) of the number of true null hypotheses as proposed by Benjamini et al. (2006) when computing adjusted p-values for the "TSBH" procedure. (see Dudoit et al., 2007).
估计数量由Benjamini等提出了真正的零假设（或估计向量）。 （2006年）时，计算调整p"TSBH"程序值。 （见Dudoit等。，2007）。
作者（S）----------Author(s)----------
Sandrine Dudoit, <a href="http://www.stat.berkeley.edu/~sandrine">http://www.stat.berkeley.edu/~sandrine</a>,<br>
Yongchao Ge, <a href="mailto:yongchao.ge@mssm.edu">yongchao.ge@mssm.edu</a>,<br>
Houston Gilbert, <a href="http://www.stat.berkeley.edu/~houston">http://www.stat.berkeley.edu/~houston</a>.
参考文献----------References----------
rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B. Vol. 57: 289-300.<br>
hypothesis testing in microarray experiments. Statistical Science. Vol. 18: 71-103. <br>
Resampling-based empirical Bayes multiple testing procedures for controlling generalized tail probability and expected value error rates: Focus on the false discovery rate and simulation study. Biometrical Journal, 50(5):716-44. http://www.stat.berkeley.edu/~houston/BJMCPSupp/BJMCPSupp.html. <br>
procedure. Scand. J. Statist.. Vol. 6: 65-70.
参见----------See Also----------
mt.maxT, mt.minP,
mt.maxT，mt.minP
举例----------Examples----------
# Gene expression data from Golub et al. (1999)[Golub等基因表达数据。 （1999）]
# To reduce computation time and for illustrative purposes, we condider only[为了减少计算时间，并说明目的，我们只condider]
# the first 100 genes and use the default of B=10,000 permutations.[第100个基因，并使用默认的B =万排列。]
# In general, one would need a much larger number of permutations[在一般情况下，将需要一个更大的数目排列]
# for microarray data.[微阵列数据。]
data(golub)
smallgd<-golub[1:100,]
classlabel<-golub.cl
# Permutation unadjusted p-values and adjusted p-values for maxT procedure[未经调整的置换P-值和p值调整maxT过程]
res1<-mt.maxT(smallgd,classlabel)
rawp<-res1$rawp[order(res1$index)]
# Permutation adjusted p-values for simple multiple testing procedures[置换调整p值简单的多个测试程序]
procs<-c("Bonferroni","Holm","Hochberg","SidakSS","SidakSD","BH","BY","ABH","TSBH")
res2<-mt.rawp2adjp(rawp,procs)
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 2012-2-26 07:38:55
