regionGoodnessOfFit-methods(Genominator)
regionGoodnessOfFit-methods()所属R语言包：Genominator
Calculate goodness-of-fit statistics
计算拟合优度统计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A generic method for calculating chi-squared goodness-of-fit statistics (See details). Dispatches on either a data.frame or and ExpData object.
一个通用的方法计算卡方善良的拟合统计（见详情）。上一个data.frame或ExpData对象的调度。
用法----------Usage----------
## S4 method for signature 'data.frame'
regionGoodnessOfFit(obj,
denominator = colSums(obj),
groups = rep("A", ncol(obj)))
## S4 method for signature 'ExpData'
regionGoodnessOfFit(obj, annoData,
groups = rep("A", length(what)),
what = getColnames(obj, all = FALSE),
denominator = c("regions", "lanes"),
verbose = getOption("verbose"))
参数----------Arguments----------
参数：obj
data.frame or ExpData
data.frame或ExpData
参数：annoData
A data.frame of annotation.
一个数据框的注释。
参数：groups
A factor or character vector describing which are the replicates.
描述这一个因素或特征向量的复制。
参数：denominator
How to scale the columns to take into account sequencing depth.
如何扩展列，要考虑到测序深度。
参数：what
Which columns to choose from the database. Default is all data columns.
从数据库中选择哪些列。默认为所有数据列。
参数：verbose
Whether or not debugging / timing info should be printed.
应印与否调试/定时信息。
Details
详情----------Details----------
This function implements the homogenous Poisson model across lanes as described in the article cited below. This model corresponds to common expression parameter across lanes scaled by a lane-specific offset. Goodness of fit to this model across replicates is a good indication of Poisson variation across lanes. Deviation from this is an indication of overdispersion between replicate lanes.
此功能实现跨均匀泊松模型在下面的文章中所述的车道。这个模型对应到对面车道缩减车道特定偏移的共同表达参数。善适合整个复制到这个模型是泊松对面车道变化的一个很好的迹象。从这个偏差是复制车道之间的偏大离差的迹象。
值----------Value----------
An list containing the statistics and degrees of freedom. See details. Technically, an S3 object with class genominator.goodness.of.fit
一个列表，其中包含的统计和自由度。查看详情。在技术上，S3对象与类genominator.goodness.of.fit
方法----------Methods----------
Here obj represents the results of a call to summarizeByAnnotation or a data.frame with columns representing samples and rows representing regions, i.e. genes. Denominator is how we scale each column, therefore it this must be true: length(denominator) == ncol(obj). Finally, groups determines how columns are aggregated
这里obj代表一个summarizeByAnnotation来电或代表代表区域，即基因的样品和行的列的数据框的结果。分母是我们如何扩展每一列，因此它必须是真实的：length(denominator) == ncol(obj)。最后，小组决定如何列汇总
Here annoData is an annotation data frame. groups is as above. what represents the columns to select choose. denominator is either the total lane counts, or the lane counts restricted to annoData, or a vector of length length(groups)
这里annoData是一个注释的数据框。 groups是如上。 what代表列选择选择。 denominator无论是总车道数，车道数限制annoData，或向量的长度length(groups)
参考文献----------References----------
Durinck, and Sandrine Dudoit, "Statistical Inference in mRNA-Seq: Exploratory Data Analysis and Differential Expression" (April 2009). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 247. http://www.bepress.com/ucbbiostat/paper247
举例----------Examples----------
ed <- ExpData(system.file(package = "Genominator", "sample.db"),
tablename = "raw")
data("yeastAnno")
names(regionGoodnessOfFit(ed, yeastAnno))
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 19:32:32
