EOC(OCplus)
EOC()所属R语言包：OCplus
Estimated or empirical FDR, sensitivity, etc as a function of cutoff level
FDR估计或经验，灵敏度等作为截止水平的功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
EOC computes and optionally plots the estimated operating characteristics for data from a microarray experiment with two groups of subjects. The false discovery rate (FDR) is estimated based on random permutations of the data and plotted against the cutoff level on the t-statistic; a curve for the classical sensitivity can be added. Different curves for different proportions of non-differentially expressed genes can be compared in the same plot, and the sample size per group can be varied between plots.
EOC计算和选择性图估计从两组受试者的基因芯片实验数据的经营特色。估计错误发现率（FDR）的基础上随机排列的数据绘制对t-统计的截止水平;可以增加为古典灵敏度曲线。可以为不同比例的非差异表达的基因的不同曲线相比，在相同的图，每个组的样本大小图之间可以多种多样。
FDRp is the function that does the underlying hard work and requires package multtest.
FDRp是做底层的辛勤工作，并要求包multtest的功能。
用法----------Usage----------
EOC(xdat, grp, p0, paired = FALSE, nperm = 25, seed = NULL, plot = TRUE, ...)
FDRp(xdat, grp, test = "t.equalvar", p0, nperm, seed)
参数----------Arguments----------
参数：xdat
the matrix of expression values, with genes as rows and samples as columns
矩阵表达式的值，行和列的样本的基因
参数：grp
a grouping variable giving the class membership of each sample, i.e. each column in xdat; for EOC, this can be any type of variable, as long as it has exactly two distinct values, whereas FDRp expects to see only 0s and 1s, see Details.
分组给每个样品的类成员变量，即每列xdat;EOC，这可以是任何类型的变量，只要具有完全两个不同的值，而<X >希望看到只有0和1，查看详情。
参数：p0
if supplied, an estimate for the proportion of non-differentially expressed genes; if not supplied, the routine will estimate it, see Details.
如果提供，非差异表达基因的比例估计，如果不提供，常规估计，查看详细信息。
参数：paired
logical value indicating whether this is independent sample situation (default) or a paired sample situation. Note that paired samples need to follow each other in the data matrix (as in 010101... </table>
逻辑值，指出这是否是独立样本的情况（默认）或配对样本的情况。请注意，配对样本需要按照彼此的数据矩阵（在010101 ... </ TABLE>
参数：nperm
number of permutations for establishing the null distribution of the t-statistic
建立空分布的t-统计数排列
参数：test
the type of test to use, see mt.teststat; when called from EOC, this is always the default.
测试使用的类型，看到mt.teststat;当调用EOC，这始终是默认。
参数：seed
the random seed from which the permutations are started
从开始排列的随机种子
参数：plot
logical value indicating whether to do the plot
逻辑值，指明是否做的图
参数：...
graphical parameters, passed to plot.FDR.result
图形参数，传递plot.FDR.result的
Details
详情----------Details----------
EOC is the empirical counterpart of the function TOC. It estimates the FDR and sensitivity for a given data set of expression values measured on subjects in two groups. The FDR is estimated locally based on the empirical Bayes approach outlined by Efron et al., see References. FDRp implements the details of this method; this requires among other things the permutation distribution of the t-statistic, which is calculated via a call to function mt.teststat of package multtest. This explains why both functions barf at missing values in the expression data.
EOC功能TOC的经验对口。据估计FDR和灵敏度两组科目衡量的表达式的值设置一个给定的数据。FDR是估计本地埃弗龙等人所述的经验Bayes方法的基础上。，见参考文献。 FDRp实现这种方法的细节，这在其他的事情需要t-统计量的计算，这是通过调用函数mt.teststat包multtest排列分布。这就解释了为什么这两个函数值在表达数据丢失的BARF。
Note that p0 is by default estimated from the data, as originally suggested by Efron et al. so as to make ratio between the densities of the observed distribution of t-statistics and the permutation distribution smaller than 1; alternatively, the user can supply his own guesstimate of the proportion of non-differentially expressed genes in the data.
注意p0默认是从数据估计，原先建议埃弗龙等。从而使t-统计量和小于1的排列分布的观测分布密度之间的比例;或者，用户可以提供自己的猜测他的非差异表达的基因数据的比例。
Note also that FDRp keeps all permuations in the memory during compuations. For a large number of genes, this will limit the number of possible permuations.
还请注意，FDRp保持在所有在compuations内存permuations。对于大量的基因，这将限制数量可能permuations。
值----------Value----------
For EOC, an object of class FDR.result, which inherits from class data.frame. The three columns list for each gene its t-statistic, the estimated FDR (two-sided), and the estimated sensitivity. Additionally, the object carries an attribute param, which is a list with four entries: p0, the assumed proportion of non-differentially expressed genes used in calculating the FDR; p0.est, a logical value indicating whether p0 was estimated or user-supplied; statistic indicates how the t-statistic was computed, i.e. how its sign should be interpreted in terms of relative over- or under expression, and a logical flag paired to indicate whether a paired t-statistic was used.
EOC类FDR.result类data.frame继承的对象。三列列表，为每一个基因的t-统计，估计FDR（双面），估计灵敏度。此外，进行对象的属性param，这四个项目的列表：p0，承担的比例计算FDR使用的非差异表达的基因; p0.est，逻辑值，该值指示是否p0估计或用户提供的;statistic表示t-统计量的计算，即相对过高或过低表达其标志应如何解释，逻辑标志paired表明是否采用配对t-统计。
FDRp returns a list with essentially the same elements, plus additionally the values of the observed and permuted distribution of the t-statistics for each gene.
FDRp本质上是相同的元素返回一个列表，加上额外的每一个基因的t-统计的观察和置换分布的值。
注意----------Note----------
Both the curve labels and the legend may be squashed if the plotting device is too small. Increasing the size of the device and re-plotting should improve readability.
曲线标签和图例可能是压扁的，如果打印设备太小。增加设备的大小和重新绘制应提高可读性。
作者（S）----------Author(s)----------
Y. Pawitan and A. Ploner
参考文献----------References----------
参见----------See Also----------
plot.FDR.result, OCshow, mt.teststat
plot.FDR.result，OCshow，mt.teststat
举例----------Examples----------
# We simulate a small example with 5 percent regulated genes and[我们模拟一个小例子，用5％的调节基因和]
# a rather large effect size[一个相当大的规模效应]
set.seed(2003)
xdat = matrix(rnorm(50000), nrow=1000)
xdat[1:25, 1:25] = xdat[1:25, 1:25] - 2
xdat[26:50, 1:25] = xdat[26:50, 1:25] + 2
grp = rep(c("Sample A","Sample B"), c(25,25))
# The default, with legend[默认情况下，与传说]
ret = EOC(xdat, grp, legend=TRUE)
# Look at the results: yes[看看结果：是]
ret[1:10,]
which(ret$FDR<0.05)
# Extra information[额外信息]
attr(ret,"param")
# Run the same data with different permutations: fairly stable, but with[运行不同的排列相同的数据相当稳定，但]
# different p0[不同的P0]
ret = EOC(xdat, grp, seed=2000)
which(ret$FDR<0.07)
# Misspecify the p0: not too bad here[Misspecify P0：这里不是太糟糕]
ret = EOC(xdat, grp, p0=0.99)
which(ret$FDR<0.01)
# We simulate data in a paired setting[我们模拟配对设置中的数据]
# Note the arrangement of the columns[请注意列的安排]
set.seed(2004)
xdat = matrix(rnorm(50000), nrow=1000)
ndx1 = seq(1,50, by=2)
xdat[1:25, ndx1] = xdat[1:25, ndx1] - 2
xdat[26:50, ndx1] = xdat[26:50, ndx1] + 2
grp = rep(c("Sample A","Sample B"), 25)
ret = EOC(xdat, grp, paired=TRUE)
which(ret$FDR<0.05)
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 08:01:27
