bn(sna)
bn()所属R语言包：sna
Fit a Biased Net Model
适合有偏见的网模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a biased net model to an input graph, using moment-based or maximum pseudolikelihood techniques.
适用于有偏见的网络模型的输入图形，时刻为基础的或最大pseudolikelihood的技术。
用法----------Usage----------
bn(dat, method = c("mple.triad", "mple.dyad", "mple.edge",
"mtle"), param.seed = NULL, param.fixed = NULL,
optim.method = "BFGS", optim.control = list(),
epsilon = 1e-05)
参数----------Arguments----------
参数：dat
a single input graph.
一个单一的输入图。
参数：method
the fit method to use (see below).
的合适的方法来使用（见下文）。
参数：param.seed
seed values for the parameter estimates.
种子的参数估计值。
参数：param.fixed
parameter values to fix, if any.
参数值来解决，如果有的话。
参数：optim.method
method to be used by optim.
要使用的方法optim。
参数：optim.control
control parameter for optim.
控制参数optim。
参数：epsilon
tolerance for convergence to extreme parameter values (i.e., 0 or 1).
误差收敛到极端的参数值（即0或1）。
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. Specifically, let e_ij be the random event that vertex i nominates vertex j when reached. Then the conditional probability of e_ij is given by
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。具体来说，我们e_ij是随机事件，顶点i提名顶点j当达到。然后e_ij的条件概率由下式给出
</i>
</ P>
where T is the current state of the trace, B_e is the a Bernoulli event corresponding to the baseline probability of e_ij, and the B_k are "bias events." Bias events are taken to be independent Bernoulli trials, given T, such that e_ij is observed with certainty if any bias event occurs. The specification of a biased net model, then, involves defining the various bias events (which, in turn, influence the structure of the network).
T是跟踪的当前状态，B_e是一个伯努利事件对应的基线概率e_ij，和B_k是“偏置事件的。”偏置事件采取的是独立的贝努利试验，T，这样e_ij观察与把握，如果出现任何偏差事件。有偏见的网络模型，然后，该规范定义的各种偏见的事件（这反过来影响了网络的结构）。
Although other events have been proposed, the primary bias events employed in current biased net models are the "parent bias" (a tendency to return nominations); the "sibling bias" (a tendency to nominate alters who were nominated by the same third party); and the "double role bias" (a tendency to nominate alters who are both siblings and parents). These bias events, together with the baseline edge events, are used to form the standard biased net model. It is standard to assume homogeneity within bias class, leading to the four parameters pi (probability of a parent bias event), sigma (probability of a sibling bias event), rho (probability of a double role bias event), and d (probability of a baseline event).
虽然已经提出的其他事件，初级偏置电流偏置网模型中采用的事件是“父偏见”（返回提名的倾向）“兄弟偏见”（提名被提名同一第三方的改变趋势），以及“双重角色偏见”（趋势提名谁都是兄弟姐妹和父母的改变）。这些偏置连同基线边沿事件，事件，被用来形成的标准偏置网模型。这是标准，假设偏置类内的均匀性，从而导致的四个参数pi（父偏压事件的概率），sigma（概率的兄弟姐妹偏压事件），rho（概率双重作用偏置事件），和d（概率的基准事件）。
Unfortunately, there is no simple expression for the likelihood of a graph given these parameters (and hence, no basis for likelihood based inference). However, Skvoretz et al. have derived a class of maximum pseudo-likelihood estimators for the the biased net model, based on local approximations to the likelihood at the edge, dyad, or triad level. These estimators may be employed within bn by selecting the appropriate MPLE for the method argument. Alternately, it is also possible to derive expected triad census rates for the biased net model, allowing an estimator which maximizes the likelihood of the observed triad census (essentially, a method of moments procedure). This last may be selected via the argument mode="mtle". In addition to estimating model parameters, bn generates predicted edge, dyad, and triad census statistics, as well as structure statistics (using the Fararo-Sunshine recurrence). These can be used to evaluate goodness-of-fit.
不幸的是，有没有简单的表达对这些参数（因此，没有可能性的推理的基础）的图形的可能性。然而，Skvoretz等。派生类的最大伪似然估计的偏网模型，根据当地的边缘近似的可能性，二分体，或黑社会。这些估计可以采用内bn方法的参数选择合适的MPLE。另外，它也可以得到预期的黑社会普查率的偏见的净模型，使所观察到的黑社会人口普查（本质上是一个方法的瞬间过程）的估计，其中最大的可能性。这最后可以选择通过参数mode="mtle"。除了估计模型参数，bn生成预测边缘，对子，和黑社会人口普查统计，以及结构统计（使用的Fararo阳光复发）。这些可用于评估善良的配合。
print, summary, and plot methods are available for bn objects. See rgbn for simulation from biased net models.
print，summary，plot方法是可用的为bn对象。见rgbn偏颇网模型进行模拟。
值----------Value----------
An object of class bn.
对象的类bn。
注意----------Note----------
Asymptotic properties of the MPLE are not known for this model. Caution is strongly advised.
此模型的渐近性质不知道的MPLE的。请注意，强烈建议。
（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. and Sunshine, M.H. (1964). “A study of a biased friendship net.” Syracuse, NY: Youth Development Center.
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----------
rgbn, structure.statistics
rgbn，structure.statistics
实例----------Examples----------
#Generate a random graph[生成一个随机图]
g<-rgraph(25)
#Fit a biased net model, using the triadic MPLE[适合有偏见的网络模型，使用三元MPLE]
gbn<-bn(g)
#Examine the results[检查结果]
summary(gbn)
plot(gbn)
#Now, fit a model containing only a density parameter[现在，拟合模型只包含密度参数]
gbn<-bn(g,param.fixed=list(pi=0,sigma=0,rho=0))
summary(gbn)
plot(gbn)
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。
|
发表于 2012-9-30 10:48:33
