estimateGLMCommonDisp(edgeR)
estimateGLMCommonDisp()所属R语言包：edgeR
Estimate Common Dispersion for Negative Binomial GLMs
估计为负二项分布GLMs常见色散
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates a common negative binomial dispersion parameter for a DGE dataset with a general experimental design.
估计为胃排空集与一般实验设计的共同负二项分布色散参数。
用法----------Usage----------
## S3 method for class 'DGEList'
estimateGLMCommonDisp(y, design=NULL, offset=NULL, method="CoxReid", ...)
## Default S3 method:[默认方法]
estimateGLMCommonDisp(y, design=NULL, offset=NULL, method="CoxReid", ...)
参数----------Arguments----------
参数：y
an object that contains the raw counts for each library (the measure of expression level); it can either be a matrix of counts, or 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)
一个对象，它包含每个库（表达水平的措施）的原始计数;它可以是一个计数的矩阵，或DGEList对象元素（至少）counts（未经调整表计数）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 giving the design matrix for the GLM that is to be fit. Must be of full column rank. Defaults to a single column of ones, equivalent to treating the columns as replicate libraries.
数字矩阵提供的GLM是适合的设计矩阵。必须是列满秩。一个单一的列的默认值，相当于复制库当作列。
参数：method
method for estimating the dispersion. Possible values are "CoxReid", "Pearson" or "deviance".
色散估算方法。可能值"CoxReid"，"Pearson"或"deviance"。
参数：offset
numeric scalar, vector or matrix giving the offsets for the log-linear models. If a scalar, then this value will be used as an offset for all transcripts and libraries. If a vector, it should be have length equal to the number of libraries, and the same vector of offsets will be used for each transcript. If a matrix, then it should have the same row and column dimensions as y. In the DGEList method, the offset is calculated by default from the library sizes and normalization factors found in y$samples.
数字标量，向量或矩阵的对数线性模型给偏移。如果一个标量，那么这个值将被用作所有成绩单和库中的偏移量。如果一个向量，它应该有长度等于数字图书馆，将每个成绩单使用相同的偏移向量。如果一个矩阵，那么它应该有相同的行和列y尺寸。在DGEList方法，偏移量计算默认情况下，发现在y$samples从库的大小和标准化的因素。
参数：...
other arguments are passed to lower-level functions. See dispCoxReid, dispPearson and dispDeviance for details.
其他的参数被传递到较低级别的功能。看到dispCoxReid，dispPearson和dispDeviance详情。
Details
详情----------Details----------
This function calls dispCoxReid, dispPearson or dispDeviance depending on the method specified. See dispCoxReid for details of the three methods and a discussion of their relative performance.
此函数调用dispCoxReid，dispPearson或dispDeviancemethod指定。看到dispCoxReid三种方法的细节，并讨论它们的相对性能。
值----------Value----------
The default method returns a numeric vector of length 1 containing the estimated dispersion.
默认方法返回一个长度为1的数字估计分散向量。
The DGEList method returns the same DGEList y as input but with common.dispersion as an added component.
DGEList方法返回相同的DGEListy作为输入，但common.dispersion作为一个附加组件。
作者（S）----------Author(s)----------
Gordon Smyth
参考文献----------References----------
binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332
参见----------See Also----------
dispCoxReid, dispPearson, dispDeviance
dispCoxReid，dispPearson，dispDeviance
estimateGLMTrendedDisp for trended dispersion and estimateGLMTagwiseDisp for tagwise dispersions in the context of a generalized linear model.
estimateGLMTrendedDisp趋势化分散和estimateGLMTagwiseDisptagwise分散在广义线性模型中。
estimateCommonDisp for common dispersion or estimateTagwiseDisp for tagwise dispersion in the context of a multiple group experiment (one-way layout).
estimateCommonDisp共同分散或estimateTagwiseDisptagwise分散在多组实验（单程布局）中。
举例----------Examples----------
# True dispersion is 1/size=0.1[真正的分散是1/size = 0.1]
y <- matrix(rnbinom(1000,mu=10,size=10),ncol=4)
d <- DGEList(counts=y,group=c(1,1,2,2))
design <- model.matrix(~group, data=d$samples)
d1 <- estimateGLMCommonDisp(d, design)
d1$common.disp
# Compare with classic CML estimator:[比较经典的慢性粒单元白血病估计：]
d2 <- estimateCommonDisp(d)
d2$common.disp
# See example(glmFit) for a different example[看到一个不同的例子（例如glmFit）]
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 17:06:35
