dispCoxReidInterpolateTagwise(edgeR)
dispCoxReidInterpolateTagwise()所属R语言包：edgeR
Estimate Tagwise Dispersion for Negative Binomial GLMs by Cox-Reid Adjusted Profile Likelihood
估计负二项分布GLMs的COX-里德调整可能性Tagwise色散
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate tagwise dispersion parameters across multiple negative binomial generalized linear models using weighted Cox-Reid Adjusted Profile-likelihood and cubic spline interpolation over a tagwise grid.
跨多个负二项分布使用超过tagwise电网加权COX-里德调整的可能性和三次样条插值的广义线性模型估计tagwise色散参数。
用法----------Usage----------
dispCoxReidInterpolateTagwise(y, design, offset=NULL, dispersion, trend=TRUE, abundance=NULL, min.row.sum=5, prior.n=getPriorN(y, design), span=2/3, grid.npts=11, grid.range=c(-6,6))
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：dispersion
numeric scalar or vector giving the dispersion(s) towards which the tagwise dispersion parameters are shrunk.
数字标量或矢量色散（S），对其中的tagwise色散参数缩水。
参数：trend
logical, whether abundance-dispersion trend is used for smoothing.
逻辑，是否使用平滑丰分散趋势。
参数：abundance
numeric scalar or vector giving the tagwise log-abundance measure for each tag. If null, the abundance is then evaluated by mglmOneGroup
给每个标签tagwiselog丰度测量的数值标量或矢量。如果为null，丰度，然后评估mglmOneGroup
参数：min.row.sum
numeric scalar giving a value for the filtering out of low abundance tags. Only tags with total sum of counts above this value are used. Low abundance tags can adversely affect the estimation of the common dispersion, so this argument allows the user to select an appropriate filter threshold for the tag abundance.
数字标筛选出低丰度的标签值。高于此值的计数总和的唯一标签。低丰度的标签，可以产生不利影响常见的色散估计，所以这个参数允许用户选择适当的过滤器标签丰度阈值。
参数：prior.n
numeric scalar, smoothing parameter that indicates the weight to give to the common likelihood compared to the individual tag's likelihood; default getPriorN(object) gives a value for prior.n that is equivalent to giving the common likelihood 20 prior degrees of freedom in the estimation of the tag/genewise dispersion.
数字标量，平滑参数表明的重量相比，个别标签的可能性，给普通的可能性;默认的getPriorN(object)给出了prior.n这相当于给普通的可能性20前度自由的价值在标签/ 2-6。分散的估计。
参数：span
numeric parameter between 0 and 1 specifying proportion of data to be used in the local regression moving window. Larger numbers give smoother fits.
介于0和1指定将用于在当地的回归移动窗口的数据比例的数字参数。较大的数字给流畅配合。
参数：grid.npts
numeric scalar, the number of points at which to place knots for the spline-based estimation of the tagwise dispersion estimates.
数字标量，点的数量，要放置节的tagwise分散估计的样条估计。
参数：grid.range
numeric vector of length 2, giving relative range, in terms of log2(dispersion), on either side of trendline for each tag for spline grid points.
长度为2给予相对的范围，术语，数字矢量log2(dispersion)，一边标记每个样条的网格点或趋势线。
Details
详情----------Details----------
In the edgeR context, dispCoxReidInterpolateTagwise is a low-level function called by estimateGLMTagwiseDisp.
在edgeR背景下，dispCoxReidInterpolateTagwise是一个低级别的功能称为estimateGLMTagwiseDisp。
dispCoxReidInterpolateTagwise calls the function maximizeInterpolant to fit cubic spline interpolation over a tagwise grid.
dispCoxReidInterpolateTagwise调用功能maximizeInterpolant适合超过tagwise电网的三次样条插值。
值----------Value----------
dispCoxReidInterpolateTagwise produces a vector of tagwise dispersions having the same length as the number of genes in the count data.
dispCoxReidInterpolateTagwise产生一个计数数据中的基因数目相同长度的tagwise分散的向量。
作者（S）----------Author(s)----------
Yunshun Chen, Gordon Smyth
参考文献----------References----------
参见----------See Also----------
estimateGLMTagwiseDisp, maximizeInterpolant
estimateGLMTagwiseDisp，maximizeInterpolant
举例----------Examples----------
y <- matrix(rnbinom(1000, mu=10, size=2), ncol=4)
design <- matrix(1, 4, 1)
dispersion <- 0.5
d <- dispCoxReidInterpolateTagwise(y, design, dispersion=dispersion)
d
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。
|
发表于 2012-2-25 17:05:54
