summary.wgaim(wgaim)
summary.wgaim()所属R语言包：wgaim
Summary and print methods for the class "wgaim"
摘要和print方法类“wgaim”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Prints a summary of the "wgaim" object in a presentable format
在一个像样的格式打印摘要“wgaim”对象
用法----------Usage----------
## S3 method for class 'wgaim'
summary(object, intervalObj, LOD = TRUE, ...)
## S3 method for class 'wgaim'
print(x, intervalObj, ...)
参数----------Arguments----------
参数：object
an object of class "wgaim"
一个类的对象“wgaim。”
参数：x
an object of class "wgaim" (see Details)
类的一个对象“wgaim”（见详情）
参数：intervalObj
a data structure of class "cross" or "interval" containing the genotypic data
类的数据结构“cross”或者“interval”包含的基因型数据
参数：LOD
logical value. If TRUE LOD scores for QTL are calculated, defaults to TRUE
逻辑值。如果是TRUE QTL的LOD值计算，则默认为TRUE
参数：...
further arguments passed to or from other methods
进一步的参数传递给其他方法
Details
详细信息----------Details----------
It is important that the intervalObj is not missing in summary.wgaim or print.wgaim as it contains vital summary information about each of the QTL detected.
重要的是，intervalObj不缺少的summary.wgaim或print.wgaim，因为它包含重要的的摘要信息有关的QTL检测。
As the WGAIM algorithm now places the selected QTL effects in a separate random component term of the model, they are summarised appropriately using a probablistic argument based on the conditional distribution of the QTL sizes given the data (see Verbyla & Taylor, 2011 in References) Thus, for each QTL, a value is calculated that represents the probability that the QTL size is greater than zero (or less than zero if the effect is negative).
，由于WGAIM算法将选定的QTL效应的模型，在一个单独的随机成分术语总结适当的使用机率性参数的基础上给出的数据大小的QTL的条件分布（见Verbyla泰勒，2011年12月1日在参考文献）因此，为每个QTL，一个值的计算，表示的QTL的大小是大于零（或小于零，如果是负的效果）的概率。
值----------Value----------
A summary of the QTL component of the "wgaim" object is printed to the screen. For each QTL detected, if an "interval" analysis was performed then summary.wgaim prints which chromosome, name and distance of each flanking marker, size, probability and LOD score (based on the random coefficient) if desired. If a "marker" analysis was performed then the chromosome, name and distance of the associated marker, size, probability and LOD score are printed. print.wgaim provides a narrative brief of the QTL's detected.
概要的的QTL组成部分的“wgaim”对象被打印到屏幕上。对于检测到的QTL，如果一个“间隔”进行分析，然后summary.wgaim印刷品每个侧翼标记，大小，概率和LOD值（随机系数基于）如果需要的话的染色体，名称和距离。如果一个“标记”进行分析，然后在染色体，姓名和相关联的标记，大小，概率和LOD值的距离打印。 print.wgaim提供了一个叙述简短的QTL的检测。
（作者）----------Author(s)----------
Julian Taylor, Simon Diffey, Ari Verbyla and Brian Cullis
参考文献----------References----------
average interval mapping and a random effects formulation. Theoretical and Applied Genetics. Submitted.
Bi-Parental Populations Using Linear Mixed Models. Journal of Statistical Software, 40(7), 1-18. URL http://www.jstatsoft.org/v40/i07/.
by simultaneous use of the full linkage map. Theoretical And Applied Genetics, 116, 195-211.
参见----------See Also----------
wgaim.asreml
wgaim.asreml
实例----------Examples----------
## Not run: [＃不运行：]
# read in data[读入数据]
data(zinc, package = "wgaim")
data(raccas, package = "wgaim")
# subset linkage map and convert to "interval" object[子集遗传连锁图谱，并将其转换为“间隔”对象]
raccas <- subset(raccas, chr = c("1A1", "2D1", "4D2", "6A1"))
raccas <- cross2int(raccas, missgeno = "Martinez")
## base model[＃示范碱基]
zn.fm <- asreml(znconc ~ Type, random = ~ Block + id, data = zinc)
# find QTL[发现QTL]
zn.qtl <- wgaim(zn.fm, phenoData = zinc, intervalObj = raccas,
merge.by = "id", trace = "trace.txt", na.method.X = "include")
# summarise[总结]
print(zn.qtl, raccasM)
summary(zn.qtl, raccasM)
## End(Not run)[＃（不执行）]
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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 2012-10-1 21:04:46
