rgnm(sna)
rgnm()所属R语言包：sna
Draw Density-Conditioned Random Graphs
绘制密度的空调随机图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
rgnm generates random draws from a density-conditioned uniform random graph distribution.
rgnm产生，随机从均匀分布的随机图分布密度的空调。
用法----------Usage----------
rgnm(n, nv, m, mode = "digraph", diag = FALSE,
return.as.edgelist = FALSE)
参数----------Arguments----------
参数：n
the number of graphs to generate.
的图形的数目来生成。
参数：nv
the size of the vertex set (|V(G)|) for the random graphs.
的顶点集合的大小（|V(G)|）的随机图形。
参数：m
the number of edges on which to condition.
的边的数量，在其上的条件。
参数：mode
"digraph" for directed graphs, or "graph" for undirected graphs.
"digraph"对于有向图，或"graph"无向图。
参数：diag
logical; should loops be allowed?
逻辑;循环应该被允许吗？
参数：return.as.edgelist
logical; should the resulting graphs be returned in edgelist form?
逻辑，生成的图表在EdgeList，在该列表的形式返回？
Details
详细信息----------Details----------
rgnm returns draws from the density-conditioned uniform random graph first popularized by the famous work of Erdos and Renyi (the G(N,M) process). In particular, the pmf of a G(N,M) process is given by
rgnm回报吸引了来自空调，密度均匀分布的随机图第一个由著名的鄂尔多斯和Renyi（G(N,M)过程）的工作推广。特别是，一个G(N,M)过程的pmf，由下式给出
</i>
</ P>
where E_m is the maximum number of edges in the graph. (E_m is equal to nv*(nv-diag)/(1+(mode=="graph")).)
E_m的最大数量是图中的边。 （E_m是等于nv*(nv-diag)/(1+(mode=="graph"))。）
The G(N,M) process is one of several process which are used as baseline models of social structure. Other well-known baseline models include the Bernoulli graph (the G(N,p) model of Erdos and Renyi) and the U|MAN model of dyadic independence. These are implemented within sna as rgraph and rgnm, respectively.
G(N,M)过程的几个过程，作为社会结构的基本模型之一。其他知名的基线机型包括的伯努利图（G(N,p)模型的鄂尔多斯和Renyi）和U | MAN模型的二元独立。这些内实施snargraph和rgnm，分别。
值----------Value----------
A matrix or array containing the drawn adjacency matrices
矩阵或数组，包含绘制的邻接矩阵
注意----------Note----------
The famous mathematicians referenced in this man page now have misspelled names, due to R's difficulty with accent marks.
现在这名男子页面中引用的著名数学家有拼写错误的名字，由于R的带重音符号的难度。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Erdos, P. and Renyi, A. (1960). “On the Evolution of Random Graphs.” Public Mathematical Institute of Hungary Academy of Sciences, 5:17-61.
参见----------See Also----------
rgraph, rguman
rgraph，rguman
实例----------Examples----------
#Draw 5 random graphs of order 10 [随机抽取5为了图10]
all(gden(rgnm(5,10,9,mode="graph"))==0.2) #Density 0.2[密度0.2]
all(gden(rgnm(5,10,9))==0.1) #Density 0.1[密度为0.1]
#Plot a random graph[绘制一个随机图]
gplot(rgnm(1,10,20))
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 11:01:07
