estimateTagwiseDisp(edgeR)
estimateTagwiseDisp()所属R语言包：edgeR
Estimate Empirical Bayes Tagwise Dispersion Values
估计经验Bayes Tagwise色散值
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates tagwise dispersion values by an empirical Bayes method based on weighted conditional maximum likelihood.
经验Bayes方法基于加权条件最大似然估计tagwise色散值。
用法----------Usage----------
estimateTagwiseDisp(object, prior.n=getPriorN(object), trend="movingave", prop.used=0.3, method="grid", grid.length=200, tol=1e-06, verbose=FALSE)
参数----------Arguments----------
参数：object
object of class DGEList containing (at least) the elements counts (table of raw counts), group (factor indicating group), lib.size (numeric vector of library sizes) and pseudo.alt (numeric matrix of quantile-adjusted pseudocounts calculated under the alternative hypothesis of a true difference between groups; recommended to use the DGEList object provided as the output of estimateCommonDisp
对象类DGEList包含的元素（至少）counts（原始计数表），group（因子组），lib.size（数字图书馆大小的矢量） pseudo.alt（数字矩阵下的一个群体之间的真正差异的对立假设计算位数调整pseudocounts的;建议使用DGEList对象作为estimateCommonDisp输出提供
参数：prior.n
numeric scalar, smoothing parameter that indicates the weight to give to the common likelihood compared to the individual tag's likelihood; default getPriorN(object) gives a value for prior.n that is equivalent to giving the common likelihood 20 prior degrees of freedom in the estimation of the tag/genewise dispersion.
数字标量，平滑参数表明的重量相比，个别标签的可能性，给普通的可能性;默认的getPriorN(object)给出了prior.n这相当于给普通的可能性20前度自由的价值在标签/ 2-6。分散的估计。
参数：trend
method for allowing the prior distribution for the dispersion to be abundance-dependent. Possible values are "none", "movingave" and "tricube". "none" means no trend. "movingave" applies a moving average smoother to the local likelihood values. "tricube" applies tricube weighting to locally smooth the common likelihood.
为使分散的先验分布是丰富依赖的方法。可能值"none"，"movingave"和"tricube"。 "none"意味着没有趋势。 "movingave"适用于当地的可能性值均线平滑。 "tricube"适用tricube加权本地平滑共同可能性。
参数：prop.used
optional scalar giving the proportion of all tags/genes to be used for the locally weighted estimation of the tagwise dispersion, allowing the dispersion estimates to vary with abundance (expression level). For each tag/gene the estimate of its dispersion is based on the closest prop.used of all of the genes to that gene, where 'closeness' is based on similarity in expression level.
局部加权估计的tagwise分散使用的所有标签/基因的比例可选标，让分散估计随丰度表达水平。每个标签/基因，其分散的估计是基于最近的prop.used所有的基因，该基因表达水平的相似性为基础，其中“亲近”的。
参数：method
method for maximizing the posterior likelihood. Possible values are "grid" for grid search or "optimize" to call the function of the same name.
方法后的可能性最大化。可能的值是"grid"网格搜索或"optimize"调用同名的函数。
参数：grid.length
for method="grid", the number of points at which the likelihood is evaluated for each tag. Larger values improve the accuracy of the dispersion estimates.
method="grid"，点的数量在评估每个标签的可能性。较大的值，改善分散估计的准确性。
参数：tol
for method="optimize", the tolerance for Newton-Rhapson iterations.
method="optimize"，宽容的牛顿Rhapson的迭代。
参数：verbose
logical, if TRUE then diagnostic ouput is produced during the estimation process.
逻辑，如果TRUE然后诊断输出中估计过程中产生的。
Details
详情----------Details----------
Maximizes the negative binomial weighted likelihood (a weighted version using the common likelihood given weight according the the smoothing parameter prior.n and the individual tag/gene likelihood) for each tag from the pseudocounts provided (i.e. assuming library sizes are equal), to give an estimate of the dispersion parameter for each tag (i.e. tagwise dispersion estimation).
每个标签的pseudocounts（即假定库的大小是相等的）最大化负二项式加权的可能性（加权版本使用普通的可能性重量根据的平滑参数prior.n的和个人的标记/基因的可能性），给予色散参数的估计每个标签（即tagwise分散估计）。
"tricube" local weighting is similar to that used by lowess. "movingave" is much faster than "tricube" and gives similar results.
"tricube"本地的比重是类似lowess的。 "movingave"是远远比"tricube"快，并给出了类似的结果。
"optimize" is very slow if there is a large number of tags/genes to be analysed (i.e., more than 5000).
"optimize"很慢，如果有大量的标签/基因进行分析（即超过5000）。
值----------Value----------
An object of class DGEList with the same components as for estimateCommonDisp plus the following:
一个类的对象DGEListestimateCommonDisp加上以下相同的组件：
参数：prior.n
estimate of the prior weight, i.e. the smoothing parameter that indicates the weight to put on the common likelihood compared to the individual tag's likelihood; prior.n of 10 means that the common likelihood is given 10 times the weight of the individual tag/gene's likelihood in the estimation of the tag/genewise dispersion
前重量的估计，即在平滑参数表明的重量到了共同的可能性提出的个人标记的可能性相比，10意味着prior.n说，常见的可能性是给予10倍重量的个人标记/基因的可能性在标签/ 2-6。分散估计
参数：tagwise.dispersion
tag- or gene-wise estimates of the dispersion parameter
色散参数标记或基因明智估计
作者（S）----------Author(s)----------
Mark Robinson, Davis McCarthy and Gordon Smyth
参考文献----------References----------
assessing differences in tag abundance. Bioinformatics 23, 2881-2887
参见----------See Also----------
estimateCommonDisp estimates a common value for the dispersion parameter for all tags/genes - should generally be run before estimateTagwiseDisp.
estimateCommonDisp估计所有标记/基因的扩散参数的一个共同的价值 - 一般应运行前estimateTagwiseDisp。
举例----------Examples----------
y<-matrix(rnbinom(1000,mu=10,size=2),ncol=4)
d<-DGEList(counts=y,group=c(1,1,2,2),lib.size=c(1000:1003))
d<-estimateCommonDisp(d)
tgwdisp<-estimateTagwiseDisp(d, prior.n=10)
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 17:07:18
