local.model.prior(nem)
local.model.prior()所属R语言包：nem
Computes a prior to be used for edge-wise model inference
计算为用于边缘明智模型推理的前
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function pairwise.posterior infers a phenotypic hierarchy edge by edge by choosing between four models (unconnected, subset, superset, undistinguishable). For each edge, local.model.prior computes a prior distribution over the four models. It can be used to ensure sparsity of the graph and high confidence in results.
功能pairwise.posterior推断四种模式（悬空，子集，超集，无法区分）之间进行选择的表型层次结构由边缘的边缘。对于每个边缘，local.model.prior计算超过四种模式的先验分布。它可以用来确保稀疏的图形和高可信度的结果。
用法----------Usage----------
local.model.prior(size,n,bias)
参数----------Arguments----------
参数：size
expected number of edges in the graph.
预计在图边数。
参数：n
number of perturbed genes in the dataset, number of nodes in the graph
数摄动基因数据集，节点的数量，在图
参数：bias
the factor by which the double-headed edge is preferred over the single-headed edges
双头边缘是在单亲边缘的首选因素
Details
详情----------Details----------
A graph on n nodes has N=n*(n-1)/2 possible directed edges (one- or bi-directional). If each edge occurs with probability p, we expect to see Np edges in the graph. The function local.model.prior takes the number of genes (n) and the expected number of edges (size) as an input and computes a prior distribution for edge occurrence: no edge with probability size/N, and the probability for edge existence being split over the three edge models with a bias towards the conservative double-headed model specified by bias. To ensure sparsity, the size should be chosen small compared to the number of possible edges.
一个n节点图N=n*(n-1)/2可能向边（或双向）。如果每边发生的概率p，我们希望看到Np在图形的边缘。功能local.model.prior的基因数目（n）和边的预期数量（size）作为输入和计算边缘发生的先验分布：无边缘的概率size/N，被分成三个边缘模型与双头bias指定的模型对保守的偏见的边缘存在的可能性。以确保稀疏，size应该选择比较小可能的边缘。
值----------Value----------
a distribution over four states: a vector of four positive real numbers summing to one
超过四个州的分布：4个正实数向量的一个总结
作者（S）----------Author(s)----------
Florian Markowetz <URL: http://genomics.princeton.edu/~florian>
参见----------See Also----------
pairwise.posterior, nem
pairwise.posterior，nem
举例----------Examples----------
# uniform over the 3 edge models[制服了3边缘模型]
local.model.prior(4,4,1)
# bias towards <->[偏向< - >]
local.model.prior(4,4,2)
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 2012-2-26 07:46:04
