dispCoxReidSplineTrend(edgeR)
dispCoxReidSplineTrend()所属R语言包：edgeR
Estimate Dispersion Trend for Negative Binomial GLMs
估计负二项式GLMs的分散趋势
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate trended common dispersion parameters across multiple negative binomial generalized linear models using Cox-Reid adjusted profile likelihood.
跨多个负二项式广义线性模型，使用COX-里德调整后的个人资料的可能性，估计趋于常见的色散参数。
用法----------Usage----------
dispCoxReidSplineTrend(y, design, offset=NULL, df = 5, subset=10000, method.optim="Nelder-Mead", trace=0)
dispCoxReidPowerTrend(y, design, offset=NULL, subset=10000, method.optim="Nelder-Mead", trace=0)
参数----------Arguments----------
参数：y
numeric matrix of counts
数字矩阵的计数
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：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.
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。
参数：df
integer giving the degrees of freedom of the spline function. The number of knots used for the spline function is df - 1.
整数，样条函数的自由程度。用于样条函数的节数是df - 1。
参数：subset
integer, number of rows to use in the calculation. Rows used are chosen evenly spaced by abundance.
整数，在计算中使用的行数。使用行选择均匀分布，丰度。
参数：method.optim
the method to be used in optim. See optim for more detail.
该方法可用于在optim。看到optim更多细节。
参数：trace
logical, should iteration information be output?
逻辑，应该迭代信息输出呢？
Details
详情----------Details----------
In the edgeR context, these are low-level functions called by estimateGLMTrendedDisp.
在edgeR背景下，这些都是由estimateGLMTrendedDisp称为低级别的功能。
dispCoxReidSplineTrend maximizes the Cox-Reid adjusted profile likelihood (Cox and Reid, 1987) by fitting spline interpolation. dispCoxReidPowerTrend models the dispersion trend by a power function. The parameters of the power function are estimated by maximizing the Cox-Reid adjusted profile likelihood.
dispCoxReidSplineTrend最大限度地拟合样条插值的COX-里德调整后的个人资料的可能性（Cox和里德，1987年）。 dispCoxReidPowerTrend模拟电源功能的分散趋势。幂函数的参数估计COX-里德调整后的个人资料的可能性最大化。
值----------Value----------
Numeric vector giving the estimated trended common dispersions. It is of the same length as the number of tags in the count data.
数字矢量估计趋势化的共同分散。它是作为标记计数数据的长度相同。
作者（S）----------Author(s)----------
Yunshun Chen, Davis McCarthy, Gordon Smyth
参考文献----------References----------
参见----------See Also----------
estimateGLMTrendedDisp, optim
estimateGLMTrendedDisp，optim
举例----------Examples----------
design <- matrix(1,4,1)
y <- matrix((rnbinom(400,mu=100,size=2)),100,4)
dispCoxReidSplineTrend(y, design, df
=3)
dispCoxReidPowerTrend(y, design)
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 17:06:02
