rgbn(sna)
rgbn()所属R语言包：sna
Draw from a Skvoretz-Fararo Biased Net Process
绘制，从Skvoretz-Fararo有偏网过程
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Produces a series of draws from a Skvoretz-Fararo biased net process using a Gibbs sampler or exact sampling procedure.
产生了一系列的借鉴，从Skvoretz-Fararo偏颇网过程中使用的Gibbs采样器或精确的抽样程序。
用法----------Usage----------
rgbn(n, nv, param = list(pi=0, sigma=0, rho=0, d=0.5),
burn = nv*nv*5*100, thin = nv*nv*5, maxiter = 1e7,
method = c("mcmc","cftp"), return.as.edgelist = FALSE)
参数----------Arguments----------
参数：n
number of draws to take.
一些即将采取。
参数：nv
number of vertices in the graph to be simulated.
要模拟的曲线的顶点数目。
参数：param
a list containing the biased net parameters.
一个列表，其中包含偏颇的网络参数。
参数：burn
for the Gibbs sampler, the number of burn-in draws to take (and discard).
Gibbs抽样，烧画（丢弃）。
参数：thin
the thinning parameter for the Gibbs sampler.
Gibbs抽样的细化参数。
参数：maxiter
for the CFTP method, the number of iterations to try before giving up.
为CFTP方法，迭代次数的尝试之前放弃。
参数：method
"mcmc" for the Gibbs sampler, or "cftp" for the exact sampling procedure.
"mcmc"Gibbs抽样，或"cftp"确切的抽样程序。
参数：return.as.edgelist
logical; should the simulated draws be returned in edgelist format?
逻辑，模拟绘制EdgeList，在该列表中的格式返回呢？
Details
详细信息----------Details----------
The biased net model stems from early work by Rapoport, who attempted to model networks via a hypothetical “tracing” process. This process may be described loosely as follows. One begins with a small “seed” set of vertices, each member of which is assumed to nominate (generate ties to) other members of the population with some fixed probability. These members, in turn, may nominate new members of the population, as well as members who have already been reached. Such nominations may be “biased” in one fashion or another, leading to a non-uniform growth process.
拉波波特，示范网试图通过一个假设的“追踪”过程偏颇的网络模型源于早期作品。此过程可被描述如下松散。一开始的一个小的“种子”的顶点集合，每一个成员，它被假定为提名产生联系，其他成员的人口与一些固定的概率。反过来，这些成员可能会提名新成员的人口，以及已经达成的。这种提名可以“偏置”在一个时装或另一个，导致非均匀的生长过程。
While the original biased net model depends upon the tracing process, a local interpretation has been put forward by Skvoretz and colleagues in recent years. Using the standard four-parameter process, the conditional probability of an (i,j) edge given all other edges in a random graph G can be written as
在原来的偏置网络模型取决于跟踪过程中，当地的解释已被提出由Skvoretz和他的同事在最近几年。使用标准的四参数的过程中，一个(i,j)边缘的条件概率的给定的所有其它边缘的一个随机图G可以写为
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</ P>
where x=1 iff j \to i (and 0 otherwise), y is the number of vertices k!=i,j such that k->i, k->j, and z=1 iff x=1 and y>0 (and 0 otherwise). Thus, x is the number of parent bias events, y is the number of sibling bias events, and z is the number of double role bias events. d is the probability of the baseline edge event; note that an edge arises if the baseline event or any bias event occurs, and all events are assumed conditionally independent. Written in this way, it is clear that the edges of G are conditionally independent if they share no endpoint. Thus, the above model is a subfamily of the Markov graphs.
x=1当且仅当j \to i（否则为0），y是顶点的数目k!=i,j这样k->i, k->j，z=1当且仅当x=1和y>0（否则为0）。因此，x是家长偏差事件的数量，y是兄弟偏差事件的数量，和z是的双重作用偏置事件的。 d是基线边缘事件的概率;注意，产生的边缘，如果基线事件或任何偏置事件的发生，并且假定所有的事件都有条件独立。这种写法，很明显的边缘G是有条件独立的，如果他们不共享端点。因此，上述的模型是一个亚科马尔可夫图。
It should be noted that the above process is not entirely consistent with the tracing-based model, which is itself not uniformly well-specified in the literature. For this reason, the local model is referred to here as a Skvoretz-Fararo graph process. One significant advantage of this process is that it is well-defined, and easily simulated: the above equation can be used to form the basis of a Gibbs sampler, which is used by rgbn to take draws from the (local) biased net model. Burn-in and thinning are controlled by the corresponding arguments; since degeneracy is common with models of this type, it is advisable to check for adequate mixing. An alternative simulation strategy is the exact sampling procedure of Butts (2008), which employs a form of coupling from the past (CFTP). The CFTP method generates exact, independent draws (up to numerical limits), but can be slow to attain coalescence. Setting maxiter to smaller values limits the search depth employed, at the possible cost of biasing the resulting sample.
应当指出的是，上述过程是不完全一致，基于跟踪的模型，它是本身不能均匀地在文献中指定。出于这个原因，本地模型被称为此处，作为Skvoretz-Fararo的图形化过程。此过程中的一个显着优势是，它是良好定义的，和简单的模拟：上面的等式可以被用于形成一个Gibbs采样的基础上，它被用来由rgbn采取利用从（本地）偏网模型。老化和变薄控制由相应的参数;以来的简并性是常见的这种类型的模型，它是可取的检查充分混合。另一种仿真策略是准确的抽样程序巴茨（2008年），它采用耦合从过去（CFTP）的一种形式。 CFTP方法产生精确的，独立的绘制（数值限制），但可能会很慢，以达到合并。设置maxiter较小的值限制搜索深度，偏置所得样品的成本。
值----------Value----------
An adjacency array containing the simulated graphs.
邻接数组，包含模拟图。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Butts, C.T. (2009). “A Perfect Sampling Method for Exponential Random Graph Models”. Working paper, University of California, Irvine.
Rapoport, A. (1957). “A Contribution to the Theory of Random and Biased Nets.” Bulletin of Mathematical Biophysics, 15, 523-533.
Skvoretz, J.; Fararo, T.J.; and Agneessens, F. (2004). “Advances in Biased Net Theory: Definitions, Derivations, and Estimations.” Social Networks, 26, 113-139.
参见----------See Also----------
bn
bn
实例----------Examples----------
#Generate draws with low density and no biases[生成利用具有低的密度和无偏见]
g1<-rgbn(50,10,param=list(pi=0, sigma=0, rho=0, d=0.17))
apply(dyad.census(g1),2,mean) #Examine the dyad census[检查对子人口普查]
#Add a reciprocity bias[添加互惠偏差]
g2<-rgbn(50,10,param=list(pi=0.5, sigma=0, rho=0, d=0.17))
apply(dyad.census(g2),2,mean) #Compare with g1[与G1比较]
#Alternately, add a sibling bias[另外，兄弟姐妹偏见]
g3<-rgbn(50,10,param=list(pi=0.0, sigma=0.3, rho=0, d=0.17))
mean(gtrans(g3)) #Compare transitivity scores[比较及物成绩]
mean(gtrans(g1))
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 11:01:01
