structure.statistics(sna)
structure.statistics()所属R语言包：sna
Compute Network Structure Statistics
计算网络结构统计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the structure statistics for the graph(s) in dat.
计算结构统计图（S）dat。
用法----------Usage----------
structure.statistics(dat, geodist.precomp = NULL)
参数----------Arguments----------
参数：dat
one or more input graphs.
一个或多个输入图表。
参数：geodist.precomp
a geodist object (optional).
geodist对象（可选）。
Details
详细信息----------Details----------
Let G=(V,E) be a graph of order N, and let d(i,j) be the geodesic distance from vertex i to vertex j in G. The "structure statistics" of G are then given by the series s_0,…,s_{N-1}, where s_i = sum(sum(I(d(j,k)<=i, k in V), j in V)/N^2 and I is the standard indicator function. Intuitively, s_i is the expected fraction of G which lies within distance i of a randomly chosen vertex. As such, the structure statistics provide an index of global connectivity.
让我们G=(V,E)图的为了N，让我们d(i,j)是测地距离顶点i的顶点jG。的“结构统计”G的，然后由该系列s_0,…,s_{N-1}，其中s_i = sum(sum(I(d(j,k)<=i, k in V), j in V)/N^2和I是标准的指标功能。直观地说，s_iG是预期的分数范围内距离i的一个随机选择的顶点。因此，结构的统计数据提供了一个全球连通性指数。
Structure statistics have been of particular importance to biased net theorists, because of the link with Rapoport's original tracing model. They may also be used along with component distributions or connectedness scores as descriptive indices of connectivity at the graph-level.
结构统计数据偏颇的净理论家特别重要的意义，因为拉波波特的原始跟踪模型的链路。它们也可以一起使用作为描述性的指数，其中连接在图形级的组件分布或连通分数。
值----------Value----------
A vector, matrix, or list (depending on dat) containing the structure statistics.
一个向量，矩阵或列表（取决于上dat），其中包含的结构统计。
注意----------Note----------
The term "structure statistics" has been used somewhat loosely in the literature, a trend which seems to be accelerating. Users should carefully check references before comparing results generated by this routine with those appearing in published work.
“结构统计”已有些松散的文献中，这似乎是加速的趋势。用户应该仔细检查引用以前发表的作品中出现的这个程序产生的结果进行比较。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Fararo, T.J. (1981). “Biased networks and social structure theorems. Part I.” Social Networks, 3, 137-159.
Fararo, T.J. (1984). “Biased networks and social structure theorems. Part II.” Social Networks, 6, 223-258.
Fararo, T.J. and Sunshine, M.H. (1964). “A study of a biased friendship net.” Syracuse, NY: Youth Development Center.
参见----------See Also----------
geodist, component.dist, connectedness, bn
geodist，component.dist，connectedness，bn
实例----------Examples----------
#Generate a moderately sparse Bernoulli graph[生成适度稀疏的伯努利图“]
g<-rgraph(100,tp=1.5/99)
#Compute the structure statistics for g[结构计算统计的g]
ss<-structure.statistics(g)
plot(0:99,ss,xlab="Mean Coverage",ylab="Distance")
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 2012-9-30 11:02:52
