fdr.adjust(LPE)
fdr.adjust()所属R语言包：LPE
FDR adjustment procedures
FDR校正程序
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Based on the type of adjustment, eg: resampling, BH, BY, etc, calls appropriate functions for fdr adjustment
基于类型的调整，例如：重采样，BH，BY等，要求FDR校正相应的功能
用法----------Usage----------
fdr.adjust(lpe.result,adjp="resamp",target.fdr=c(10^-3 ,seq(0.01,0.10,0.01), 0.15, 0.20, 0.50),iterations=5,ALL=FALSE )
参数----------Arguments----------
参数：lpe.result
Data frame obtained from calling lpe function
数据框从调用LPE功能
参数：adjp
Type of adjustment procedure. Can be "resamp", "BH", "BY", "Bonferroni" or "mix.all"
调整程序的类型。可以“resamp”，“波黑”，“靠”，“邦弗朗尼”或“mix.all”
参数：target.fdr
Desired FDR level (used only for resampling based adjustment)
所需的FDR水平（仅用于重采样为基础调整）
参数：iterations
Number of iterations for stable z-critical.
Z-临界稳定的迭代数。
参数：ALL
If TRUE, the FDR corresponding to all the z-statistics, i.e. for every gene intensity is given.
如果是TRUE，FDR相应的Z-统计，即每一个基因的强度。
Details
详情----------Details----------
Returns the output similar to lpe function, including adjusted FDR. BH and BY give Benjamini-Hochberg and Benjamini-Yekutieli adjusted FDRs (adopted from multtest procedure), Bonferroni adjusted p-values and "mix.all" gives SAM-like FDR adjustment. For further details on the comparisons of each of these methods, please see the reference paper (Rank-invariant resampling...) mentioned below. Users are encouraged to use FDR instead of Bonferrni adjsusted p-value as initial cutoffs while selecting the significant genes. Bonferroni adjusted p-values are provided under Bonferroni method here just for the sake of completion for the users who want it.
返回类似LPE功能，包括调整后的FDR的输出。 BH和给Benjamini Hochberg和Benjamini Yekutieli调整FDRs“（从multtest过程通过）的，邦弗朗尼调整p值和”mix.all“给萨姆像FDR校正。对于这些方法的比较进一步的细节，请参阅下面的参考文件（等级不变的重采样...）提到。鼓励用户使用作为初始截止FDR的Bonferrni adjsusted p值，而不是同时选择的重大基因。调整p值邦弗朗尼规定Bonferroni方法在这里只是为了完成谁想要它为用户着想。
作者（S）----------Author(s)----------
Nitin Jain<a href="mailto:nitin.jain@pfizer.com">nitin.jain@pfizer.com</a>
参考文献----------References----------
differentially expressed genes with a small number of replicated microarrays, Bioinformatics, 1945-1951.
举例----------Examples----------
# Loading the library and the data[载入库和数据]
library(LPE)
data(Ley)
dim(Ley)
# Gives 12488*7 [给12488 * 7]
# First column is ID.[第一列是ID。]
Ley[,2:7] <- preprocess(Ley[,2:7],data.type="MAS5")
# Subsetting the data[子集的数据]
subset.Ley <- Ley[1:1000,]
# Finding the baseline distribution of condition 1 and 2.[寻找条件1和2的基准分配。]
var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
var.2 <- baseOlig.error(subset.Ley[,5:7], q=0.01)
# Applying LPE[应用液相外延]
lpe.result <- lpe(subset.Ley[,2:4],subset.Ley[,5:7], var.1, var.2,
probe.set.name=subset.Ley[,1])
final.result <- fdr.adjust(lpe.result, adjp="resamp", target.fdr=c(0.01,0.05), iterations=1)
final.result
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 2012-2-25 23:39:57
