fdr1d(OCplus)
fdr1d()所属R语言包：OCplus
Compute classical local false discovery rate
计算古典当地虚假发现率
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculates the classical local false discovery rate for multiple parallel t-statistics.
计算多个并行t-统计的古典当地虚假的发现率。
用法----------Usage----------
fdr1d(xdat, grp, test, p0, nperm = 100, nr = 50, seed = NULL, null = NULL,
zlim = 1, sv2 = 0.01, err = 1e-04, verb = TRUE, ...)
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
分组给每个样品的类成员变量，即xdat的每列
参数：test
a function that takes xdat and grp as the first two arguments and returns the test statistic; by default, two-sample t-statistics are calculated.
一个函数，它xdat和grp前两个参数并返回测试统计，默认情况下，两样本t-统计量的计算。
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：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：nr
the number of equidistant breaks into which the range of test statistics is broken for calculating the fdr.
进入测试统计的范围为计算FDR打破等距离截断。
参数：seed
if specified, the random seed from which the permuations are started
如果指定，从中随机种子permuations启动
参数：null
optional argument for passing in a pre-calculated null distribution, see Details.
通过在预先计算的空分布的可选参数，查看详情。
参数：zlim
if no p0 is specified, the ratio of densities in the range of test statistics between -zlim and zlim will be used to estimate the proportion of non-differentially expressed genes; ignored if p0 is specified.
如果没有p0指定-zlim和zlim将用于估计非差异表达基因的比例之间的测试统计的范围比密度;忽略p0指定。
参数：sv2
positive number controlling the initial degree of smoothing for the densities involved, with smaller values indicating more smoothing; see Details.
正数控制参与密度较小值显示更加平滑，平滑的初始程度;查看详细信息。
参数：err
positive number controlling the convergence of the smoothing procedure, with smaller values implying more iterations; see Details.
正数控制的平滑过程的收敛性，数值较小，这意味着更多的迭代;见详情。
参数：verb
logical value indicating whether provide extra information.
逻辑值，该值指示是否提供额外的信息。
参数：...
extra arguments to function test.
额外的参数给函数test。
Details
详情----------Details----------
This function calculates the local false discovery rate (fdr, as opposed to global FDR) introduced by Efron et al., 2001. The underlying model assumes that for a given grouping of samples, we study a mixture of differentially expressed (DE) and non-DE genes, and that consequently, the observed distribution of test statistics is a mixture of test statistics under the alternative and the null statistic, respectively. The densities involved are estimated nonparametrically and smoothed, using a permutation argument for the null distribution.
此函数计算当地的假发现率（FDR，反对全球FDR）埃弗龙等人介绍，2001年。底层模型的假设，对于给定的样本分组，我们研究的混合物差异表达（DE）和非-DE的基因，因此，观察测试统计分布是一个检验统计量的替代和空下的混合物统计，分别。涉及的密度估计nonparametrically平滑，使用空分布排列参数。
By default, the null distribution is generated and stored only within the function, and is not available outside. If someone wants to study the null distribution, or wants to re-use the same null distribution efficiently while e.g. varying the smoothing parameter, the argument null allows the use of an externally generated null distribution, created e.g. using the PermNull function.
默认情况下，空分布的生成和存储只在函数内，外是不可使用。如果有人想学习空分布，或想重新使用相同的空分布有效而如不同的平滑参数，参数null允许使用外部生成的空分布，如创建使用PermNull功能。
Theoretically, the function should support any kind of function along the lines of tstatistics, however, this has not been tested, and the current setup is very much geared towards t-tests.
从理论上讲，该功能应支持任何一种线沿线的功能tstatistics，但是，这并没有经过测试，目前的设置是非常适合对t-检验。
We use non-Gaussian mixed model smoothing for Possion counts for smoothing the density estimates, see Pawitan, 2001, and smooth1d.
我们使用非高斯混合模型的平滑，平滑的密度估计泊松计数看到Pawitan，2001年，和smooth1d的。
值----------Value----------
Basically, a data frame with one row per gene and two columns: tstat, the test statistic, and fdr.local, the local false discovery rate. This data frame has the additional class attributes fdr1d.result and fdr.result, see Examples. This is the bad old S3 class mechanism employed to provide plot and summary functions.
基本上，每个基因的一行两列的数据框：tstat，测试统计，和fdr.local，当地假发现率。此数据框有额外的类属性fdr1d.result和fdr.result，看到的例子。这是坏的老S3类的机制，以提供图和汇总功能。
Additional information is provided by a param attribute, which is a list with the following entries:
param属性，这是一个具有以下条目列表提供更多的信息：
参数：p0
the proportion of non-differentially expressed genes used when calculating the fdr.
非差异表达基因的比例计算FDR时使用。
参数：p0.est
a logical value indicating whether p0 was estimated from the data or supplied by the user.
一个逻辑值，指明是否p0估计的数据，或由用户提供。
参数：fdr
the smoothed fdr values calculated for the original intervals.
平滑FDR值计算为原来的时间间隔。
参数：xbreaks
vector of breaks for the test statistic defining the interval for calculation.
定义计算的时间间隔为测试统计的中断向量。
作者（S）----------Author(s)----------
A. Ploner
参考文献----------References----------
参见----------See Also----------
plot.fdr1d.result, summary.fdr.result, OCshow, tstatistics, smooth1d, fdr2d, PermNull
plot.fdr1d.result，summary.fdr.result，OCshow，tstatistics，smooth1d，fdr2d，PermNull
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(2000)
xdat = matrix(rnorm(50000), nrow=1000)
xdat[1:25, 1:25] = xdat[1:25, 1:25] - 1
xdat[26:50, 1:25] = xdat[26:50, 1:25] + 1
grp = rep(c("Sample A","Sample B"), c(25,25))
# A default run[默认的运行]
res1d = fdr1d(xdat, grp)
res1d[1:20,]
# Looking at the results[看结果]
summary(res1d)
plot(res1d)
res1d[res1d$fdr<0.05, ]
# Averaging estimates the global FDR for a set of genes[平均估计为一组基因的全球FDR]
ndx = abs(res1d$tstat) > 3
mean(res1d$fdr[ndx])
# Extra information[额外信息]
class(res1d)
attr(res1d,"param")
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 08:01:52
