getPriorN(edgeR)
getPriorN()所属R语言包：edgeR
Get a Recommended Value for Prior N from DGEList Object
之前列印获取从DGEList对象的推荐值
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns the lib.size component of the samples component of DGEList object multiplied by the norm.factors component
返回的lib.size的DGEList对象组件samples的分量乘以norm.factors组件
用法----------Usage----------
getPriorN(y, design=NULL, prior.df=20)
参数----------Arguments----------
参数：y
a DGEList object with (at least) elements counts (table of unadjusted counts) and samples (data frame containing information about experimental group, library size and normalization factor for the library size)
DGEList对象（至少）元素counts（未经调整数表）和samples（数据框包含有关实验组，库的大小和归一化因子的资料库的大小）
参数：design
numeric matrix (optional argument) giving the design matrix for the GLM that is to be fit. Must be of full column rank. If provided design is used to determine the number of parameters to be fit in the statistical model and therefore the residual degrees of freedom. If left as the default (NULL) then the y$samples$group element of the DGEList object is used to determine the residual degrees of freedom.
数字矩阵（可选参数），是适合的GLM的设计矩阵。必须是列满秩。如果提供design是用来确定参数的数量是合适的统计模型，因此自由的残留度。如果离开作为默认（NULL）y$samples$group元素DGEList对象是用来确定自由的残留度。
参数：prior.df
numeric scalar giving the weight, in terms of prior degrees of freedom, to be given to the common parameter likelihood when estimating tagwise dispersion estimates.
数字标重量，在之前的自由程度方面，估计tagwise分散估计时必须考虑到常见的参数的可能性。
Details
详情----------Details----------
When estimating tagwise dispersion values using estimateTagwiseDisp or estimateGLMTagwiseDisp we need to decide how much weight to give to the common parameter likelihood in order to smooth (or stabilize) the dispersion estimates. The best choice of value for the prior.n parameter varies between datasets depending on the number of samples in the dataset and the complexity of the model to be fit. The value of prior.n should be inversely proportional to the residual degrees of freedom. We have found that choosing a value for prior.n that is equivalent to giving the common parameter likelihood 20 degrees of freedom generally gives a good amount of smoothing for the tagwise dispersion estimates. This function simply recommends an appropriate value for prior.n—to be used as an argument for estimateTagwiseDisp or estimateGLMTagwiseDisp—given the experimental design at hand and the chosen prior degrees of freedom.
当估计使用estimateTagwiseDisp或estimateGLMTagwiseDisp，“我们需要来决定给多少重量的共同参数的可能性，以便顺利（或稳定）的色散估计tagwise色散值。之间的模型是合适的数据集和复杂的数据集的样本数量取决于不同的为prior.n参数值的最佳选择。值prior.n应该是自由的残留度成反比。我们发现，选择一个值prior.n也就是相当于给20个自由度的共同参数的可能性一般给人以良好的平滑的tagwise分散估计金额。此功能只是建议适当的值prior.n要作为一个参数estimateTagwiseDisp或estimateGLMTagwiseDisp-手头的实验设计和自由选择的前度。
值----------Value----------
getPriorN returns a numeric scalar
getPriorN返回一个数字标
作者（S）----------Author(s)----------
Davis McCarthy, Gordon Smyth
参见----------See Also----------
DGEList for more information about the DGEList class. as.matrix.DGEList.
DGEListDGEList类的更多信息。 as.matrix.DGEList。
举例----------Examples----------
# generate raw counts from NB, create list object[来自NB生成的原始计数，创建列表对象]
y<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
d<-DGEList(counts=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
getPriorN(d)
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 17:07:56
