estimateGLMTrendedDisp(edgeR)
estimateGLMTrendedDisp()所属R语言包：edgeR
Estimate Trended Dispersion for Negative Binomial GLMs
估计负二项式GLMs的趋于分散
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the dispersion parameter for each transcript (tag) with a trend that depends on the overall level of expression for the transcript 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.
估计色散参数为每个依赖为负二项式的广义线性模型为每个成绩单使用COX-里德条件近似推理的为胃排空集为一般实验设计的成绩单上表达的整体水平与趋势的谈话内容（标签） （标签），未经调整的数量和提供设计矩阵。
用法----------Usage----------
## S3 method for class 'DGEList'
estimateGLMTrendedDisp(y, design, offset=NULL, method="bin.spline", ...)
## Default S3 method:[默认方法]
estimateGLMTrendedDisp(y, design, offset=NULL, method="bin.spline", ...)
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：method
method (low-level function) used to estimated the trended dispersions. Possible values are "bin.spline" (default), "bin.loess" (which both result in a call to dispBinTrend), "power" (call to dispCoxReidPowerTrend), or "spline" (call to dispCoxReidSplineTrend).
方法（低级函数）用于估计的趋势化分散。可能的值是"bin.spline"（默认），"bin.loess"，dispBinTrend（"power"），或<打检测（这两个在调用dispCoxReidPowerTrend） X>（"spline"）打检测。
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. In adjustedProfileLik the offset must be a matrix with the same dimension as the table of counts. 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。在adjustedProfileLikoffset必须与计数表的同一维度的矩阵。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：...
other arguments are passed to lower-level functions. See dispBinTrend, dispCoxReidPowerTrend and dispCoxReidSplineTrend for details.
其他的参数被传递到较低级别的功能。看到dispBinTrend，dispCoxReidPowerTrend和dispCoxReidSplineTrend详情。
Details
详情----------Details----------
This is a wrapper function for the lower-level functions that actually carry out the dispersion estimation calculations. Provide a convenient, object-oriented interface for users.
这是一个较低级别的功能，实际上开展的色散估计计算的包装函数。为用户提供了方便，面向对象的接口。
值----------Value----------
When the input object is a DGEList, estimateGLMTrendedDisp produces a DGEList object, which contains the estimates of the trended dispersion parameter for the negative binomial model according to the method applied.
当输入的对象是一个DGEList，estimateGLMTrendedDisp生产DGEList对象，其中包含趋势化的色散参数为负二项式模型方法估计。
When the input object is a numeric matrix, the output of one of the lower-level functions dispBinTrend, dispCoxReidPowerTrend of dispCoxReidSplineTrend is returned.
当输入对象是数字矩阵，输出低级别的功能之一dispBinTrend，dispCoxReidPowerTrenddispCoxReidSplineTrend返回。
作者（S）----------Author(s)----------
Gordon Smyth, Davis McCarthy
参考文献----------References----------
参见----------See Also----------
dispBinTrend, dispCoxReidPowerTrend and dispCoxReidSplineTrend for details on how the calculations are done.
dispBinTrend，dispCoxReidPowerTrend和dispCoxReidSplineTrend如何计算的细节。
estimateGLMCommonDisp for common dispersion and estimateGLMTagwiseDisp for (trended) tagwise dispersion in the context of generalized linear models.
estimateGLMCommonDisp共同分散和estimateGLMTagwiseDisp（趋势化）在广义线性模型中tagwise分散。
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]
disp <- estimateGLMTrendedDisp(d, design, min.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 17:06:58
