dispCoxReid(edgeR)
dispCoxReid()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----------
Estimate a common dispersion parameter across multiple negative binomial generalized linear models.
多个负二项式广义线性模型估计一个常见的色散参数。
用法----------Usage----------
dispCoxReid(y, design, offset=NULL, interval=c(0,4), tol=1e-5, min.row.sum=5, subset=10000)
dispDeviance(y, design, offset=NULL, interval=c(0,4), tol=1e-5, min.row.sum=5, subset=10000, robust=FALSE, trace=FALSE)
dispPearson(y, design, offset=NULL, interval=c(0,4), tol=1e-5, min.row.sum=5, subset=10000, robust=FALSE, trace=FALSE)
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. 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：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：interval
numeric vector of length 2 giving allowable values for the dispersion, passed to optimize.
数字矢量长度为2的允许值的分散，通过optimize。
参数：tol
the desired accuracy, see optimize or uniroot.
所需的精度，看到optimize或uniroot。
参数：min.row.sum
integer. Only rows with at least this number of counts are used.
整数。只有至少计数行。
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：trace
logical, should iteration information be output?
逻辑，应该迭代信息输出呢？
参数：robust
logical, should a robust estimator be used?
逻辑，应稳健估计？
Details
详情----------Details----------
These are low-level (non-object-orientated) functions called by estimateGLMCommonDisp.
这是由estimateGLMCommonDisp称为低级别（非面向对象）的功能。
dispCoxReid maximizes the Cox-Reid adjusted profile likelihood (Cox and Reid, 1987). dispPearson sets the average Pearson goodness of fit statistics to its (asymptotic) expected value. This is also known as the pseudo-likelihood estimator. dispDeviance sets the average residual deviance statistic to its (asymptotic) expected values. This is also known as the quasi-likelihood estimator.
dispCoxReid最大化的COX-里德调整后的个人资料的可能性（Cox和里德，1987年）。 dispPearson设置合适的统计平均皮尔森善良的（渐近）的预期值。这也被称为伪似然估计。 dispDeviance设置平均剩余（渐近）预期值的偏差统计。这也被称为拟似然估计。
Robinson and Smyth (2008) showed that the Pearson (pseudo-likelihood) estimator typically under-estimates the true dispersion. It can be seriously biased when the number of libraries (ncol(y) is small. On the other hand, the deviance (quasi-likelihood) estimator typically over-estimates the true dispersion when the number of libraries is small. Robinson and Smyth (2008) showed the Cox-Reid estimator to be the least biased of the three options.
罗宾逊和史密斯（2008年）表明，皮尔逊（伪似然）估计，通常根据估计，真正的分散。它可以严重的偏见时，图书馆的数量（ncol(y)小偏差（准的可能性）。另一方面，估计通常过高估计真正的分散时，图书馆的数量小，罗宾逊和史密斯（2008年）表明，COX-里德估计是三个选项中至少有偏见。
dispCoxReid uses optimize to maximize the adjusted profile likelihood, while dispDeviance and dispPearson use uniroot to solve the estimating equation. The robust options use an order statistic instead the mean statistic, and have the effect that a minority of tags with very large (outlier) dispersions should have limited influence on the estimated value.
dispCoxReid使用optimize最大限度地调整配置文件的可能性，而dispDeviance和dispPearson用uniroot解决估计方程。强劲的选项使用的次序统计量，而不是平均的统计，有效果的标签应该有非常大（离群）分散的少数民族的估计值的影响有限。
值----------Value----------
Numeric vector of length one giving the estimated common dispersion.
数字矢量长度，估计常见的色散之一。
作者（S）----------Author(s)----------
Gordon Smyth
参考文献----------References----------
Journal of the Royal Statistical Society Series B 49, 1-39.
binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332
参见----------See Also----------
estimateGLMCommonDisp, optimize, uniroot
estimateGLMCommonDisp，optimize，uniroot
举例----------Examples----------
ntags <- 100
nlibs <- 4
y <- matrix(rnbinom(ntags*nlibs,mu=10,size=10),nrow=ntags,ncol=nlibs)
group <- factor(c(1,1,2,2))
lib.size <- rowSums(y)
design <- model.matrix(~group) # Define the design matrix for the full model[定义完整的模型设计矩阵]
disp <- dispCoxReid(y, design, offset=log(lib.size), subset=100)
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 17:05:43
