estimateGLMTagwiseDisp(edgeR)
estimateGLMTagwiseDisp()所属R语言包：edgeR
Estimate Empirical Bayes Tagwise Dispersions for Negative Binomial GLMs
估计负二项式GLMs的经验Bayes Tagwise分散
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the dispersion parameter for a DGE dataset for general experimental designs by using Cox-Reid approximate conditional inference for a negative binomial generalized linear model for each transcript (tag) with the unadjusted counts and design matrix provided.
估计负二项式的广义线性模型为每个成绩单未经调整的数量和提供设计矩阵（标签）使用COX-里德条件近似推理的一般实验设计胃排空集分散参数。
用法----------Usage----------
## S3 method for class 'DGEList'
estimateGLMTagwiseDisp(y, design, offset=NULL, trend=!is.null(y$trended.dispersion), ...)
## Default S3 method:[默认方法]
estimateGLMTagwiseDisp(y, design, offset=NULL, dispersion, trend=TRUE, ...)
参数----------Arguments----------
参数：y
an object that contains the raw counts for each library (the measure of expression level); it can either be a matrix of counts, or a DGEList object with (at least) elements counts (table of unadjusted counts) and samples (data frame containing information about experimental group, library size and normalization factor for the library size)
一个对象，它包含每个库（表达水平的措施）的原始计数;它可以是一个计数的矩阵，或DGEList对象元素（至少）counts（未经调整表计数）和samples（数据框包含有关实验组，库的大小和归一化因子的资料库的大小）
参数：design
numeric matrix giving the design matrix for the GLM that is to be fit.
数字矩阵提供的GLM是适合的设计矩阵。
参数：trend
logical, should an abundance trend be applied to the grid of dispersion values over which the tagwise dispersion estimation is done? Generally this should be TRUE if a trended dispersion has been estimated and FALSE otherwise.
逻辑，应该被应用到网格色散值的tagwise分散估计完成丰趋势？一般来说，这应该是TRUE如果一个趋势化的分散估计已FALSE否则。
参数：offset
numeric scalar, vector or matrix giving the offset (in addition to the log of the effective library size) that is to be included in the NB GLM for the transcripts. If a scalar, then this value will be used as an offset for all transcripts and libraries. If a vector, it should be have length equal to the number of libraries, and the same vector of offsets will be used for each transcript. If a matrix, then each library for each transcript can have a unique offset, if desired. Default is NULL; if object is a DGEList and offset is NULL then offset will be calculated automatically from codey$samples.
数字标量，向量或矩阵给抵消（除了的有效库容量的log）是被包括在NB的GLM的成绩单。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，然后每个谈话的每个库可以有独特的偏移，如果需要的话。默认是NULL如果对象是一个DGEList和偏移是NULL然后偏移将寇迪为样本计算自动。
参数：dispersion
vector or scalar giving the dispersion value(s) to be used to set the grip of points for computation of the tagwise dispersion in dispCoxReidInterpolateTagwise.
向量或标量色散值（S）被用来计算为dispCoxReidInterpolateTagwisetagwise分散设置控制点。
参数：...
other arguments are passed to lower-level functions. See dispCoxReidInterpolateTagwise for details.
其他的参数被传递到较低级别的功能。看到dispCoxReidInterpolateTagwise详情。
Details
详情----------Details----------
This generic function is essentially a wrapper for dispCoxReidInterpolateTagwise. To obtain estimates of the tagwise dispersion parameters for negative binomial GLMs we use Cox-Reid approximate conditional inference as implemented in dispCoxReidInterpolateTagwise. The approach is to maximize the adjusted profile likelihood over the dispersion value, for the tagwise models and use these values as the tagwise dispersion parameters for differential signal testing in downstream analysis.
这个泛型函数本质上是一个dispCoxReidInterpolateTagwise的包装。为了获得负二项式GLMs我们使用COX-里德近似的条件推断，在dispCoxReidInterpolateTagwise实施的tagwise色散参数的估计。该方法是调整后的个人资料的可能性最大化的色散值，为tagwise车型和使用这些值，作为下游分析的差分信号测试tagwise色散参数。
值----------Value----------
estimateGLMTagwiseDisp.DGEList produces a DGEList object, which contains the tagwise dispersion parameter estimate for each tag for the negative binomial model that maximizes the Cox-Reid adjusted profile likelihood. The tagwise dispersions are simply added to the DGEList object provided as the argument to the function.
estimateGLMTagwiseDisp.DGEList生产DGEList对象，这包含的tagwise的色散负二项式模型，COX-里德调整后的个人资料的可能性最大化为每个标签的参数估计。 tagwise分散只需添加DGEList作为函数的参数提供的对象。
estimateGLMTagwiseDisp.default returns a vector of the tagwise dispersion estimates.
estimateGLMTagwiseDisp.default返回的tagwise分散估计向量。
作者（S）----------Author(s)----------
Gordon Smyth, Davis McCarthy
参考文献----------References----------
参见----------See Also----------
estimateGLMCommonDisp for common dispersion and estimateGLMTrendedDisp for trended dispersion in the context of a generalized linear model.
estimateGLMCommonDisp共同分散和estimateGLMTrendedDisp趋势化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----------
y <- matrix(rnbinom(1000,mu=10,size=10),ncol=4)
d <- DGEList(counts=y,group=c(1,1,2,2),lib.size=c(1000:1003))
design <- model.matrix(~group, data=d$samples) # Define the design matrix for the full model[定义完整的模型设计矩阵]
d <- estimateGLMTrendedDisp(d, design, min.n=10)
d <- estimateGLMTagwiseDisp(d, design)
summary(d$tagwise.dispersion)
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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 17:06:44
