bbnam.bf(sna)
bbnam.bf()所属R语言包：sna
Estimate Bayes Factors for the bbnam
估计贝叶斯因子的bbnam
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function uses monte carlo integration to estimate the BFs, and tests the fixed probability, pooled, and pooled by actor models. (See bbnam for details.)
此功能使用蒙特卡罗积分估计的高炉，并测试固定的概率，汇集，汇集的演员模型。 （见bbnam的详细信息。）
用法----------Usage----------
bbnam.bf(dat, nprior=0.5, em.fp=0.5, ep.fp=0.5, emprior.pooled=c(1, 11),
epprior.pooled=c(1, 11), emprior.actor=c(1, 11), epprior.actor=c(1, 11),
diag=FALSE, mode="digraph", reps=1000)
参数----------Arguments----------
参数：dat
Input networks to be analyzed. This may be supplied in any reasonable form, but must be reducible to an array of dimension m x n x n, where n is |V(G)|, the first dimension indexes the observer (or information source), the second indexes the sender of the relation, and the third dimension indexes the recipient of the relation. (E.g., dat[i,j,k]==1 implies that i observed j sending the relation in question to k.) Note that only dichotomous data is supported at present, and missing values are permitted; the data collection pattern, however, is assumed to be ignorable, and hence the posterior draws are implicitly conditional on the observation pattern.
输入网络，以进行分析。这可以在任何合理的方式提供，但必须还原到一个数组的尺寸m x n x n，这里n是|V(G)|，第一个维度指标观察者（或信息源），第二索引的发送者的关系，和在第三个维度指标的关系的收件人。 （例如，dat[i,j,k]==1意味着我观察发送问题到k的关系，在Ĵ。）注意，只有二分数据的支持，目前，失踪的允许值的数据收集模式，但是，被认为是忽略不计，因此，后路绘制是隐式条件的观察模式。
参数：nprior
Network prior matrix. This must be a matrix of dimension n x n, containing the arc/edge priors for the criterion network. (E.g., nprior[i,j] gives the prior probability of i sending the relation to j in the criterion graph.) Non-matrix values will be coerced/expanded to matrix form as appropriate. If no network prior is provided, an uninformative prior on the space of networks will be assumed (i.e., Pr(i->j)=0.5). Missing values are not allowed.
之前网络矩阵。这必须是一个矩阵的维nXn，包含弧/边缘先验的标准网络。 （例如，nprior[i,j]的i发送的关系j，的标准图中给出的先验概率。），非矩阵值将被强制转换/扩展到适当的矩阵形式。如果没有网络之前，网络空间的无信息先验的假设（即，Pr(i->j)=0.5）。遗漏值是不允许的。
参数：em.fp
Probability of false negatives for the fixed probability model
假阴性的概率为固定的概率模型
参数：ep.fp
Probability of false positives for the fixed probability model
误报的概率为固定的概率模型
参数：emprior.pooled
(alpha,beta) pairs for the (beta) false negative prior under the pooled model
(alpha,beta)（测试版）假阴性之前对集中模式下的
参数：epprior.pooled
(alpha,beta) pairs for the (beta) false positive prior under the pooled model
(alpha,beta)集中模式下的双（测试版）误报前
参数：emprior.actor
Matrix of per observer (alpha,beta) pairs for the (beta) false negative prior under the per observer/actor model, or something that can be coerced to this form
每观察员(alpha,beta)（测试版）之前，在每个观察者/演员模型，假阴性的东西，可以强制转换为这种形式对矩阵
参数：epprior.actor
Matrix of per observer ((alpha,beta) pairs for the (beta) false positive prior under the per observer/actor model, or something that can be coerced to this form
矩阵的每观察员（(alpha,beta)对前（测试版）假阳性元的观察者/演员模型下，或可以强制转换为这种形式的东西，
参数：diag
Boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the criterion graph can contain loops. Diag is false by default.
布尔指示是否对角线应被视为有效的数据。设置这是真的，当且仅当的标准图形可以包含循环。默认情况下，诊断是假的。
参数：mode
String indicating the type of graph being evaluated. "digraph" indicates that edges should be interpreted as directed; "graph" indicates that edges are undirected. Mode is set to "digraph" by default.
的图表类型的字符串，表示正在评估中。 "digraph"表示的边缘应被解释为指示;"graph"表明边缘是无向的。默认情况下被设置成"digraph"模式。
参数：reps
Number of Monte Carlo draws to take
蒙地卡罗数量即将采取
Details
详细信息----------Details----------
The bbnam model (detailed in the bbnam function help) is a fairly simple model for integrating informant reports regarding social network data. bbnam.bf computes log Bayes Factors (integrated likelihood ratios) for the three error submodels of the bbnam: fixed error probabilities, pooled error probabilities, and per observer/actor error probabilities.
bbnam模型（详见bbnam功能帮助）是一个相当简单的线人报告，关于社会网络数据集成模型。 bbnam.bf计算log贝叶斯因子（综合似然比）的三个错误子模型的bbnam：固定误差概率，汇集错误概率，和每个观察员/演员错误的概率。
By default, bbnam.bf uses weakly informative Beta(1,11) priors for false positive and false negative rates, which may not be appropriate for all cases. (Likewise, the initial network prior is uniformative.) Users are advised to consider adjusting the error rate priors when using this function in a practical context; for instance, it is often reasonable to expect higher false negative rates (on average) than false positive rates, and to expect the criterion graph density to be substantially less than 0.5. See the reference below for a discussion of this issue.
默认情况下，bbnam.bf使用弱翔实的测试版（1,11）先验假阳性和假阴性率，这可能不是适合所有的情况下。 （同样，前最初的网络是uniformative）。用户应考虑调整误差率先验知识，在实际的环境中使用此功能时，例如，它往往是合理的期望较高的假阴性率（平均）比假阳性率，并期望的标准图形密度基本上小于0.5。这个问题的讨论，请参阅以下参考。
值----------Value----------
An object of class bayes.factor.
对象的类bayes.factor。
注意----------Note----------
It is important to be aware that the model parameter priors are essential components of the models to be compared; inappropriate parameter priors will result in misleading Bayes Factors.
重要的是要意识到，模型参数先验的重要组成部分的模型进行比较，不恰当的参数先验会导致误导性的贝叶斯因子。
（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. (2003). “Network Inference, Error, and Informant (In)Accuracy: A Bayesian Approach.” Social Networks, 25(2), 103-140.
参见----------See Also----------
bbnam
bbnam
实例----------Examples----------
#Create some random data from the "pooled" model[从“池”的模式，创建一些随机数据]
g<-rgraph(7)
g.p<-0.8*g+0.2*(1-g)
dat<-rgraph(7,7,tprob=g.p)
#Estimate the log Bayes Factors[估计log贝叶斯因子]
b<-bbnam.bf(dat,emprior.pooled=c(3,5),epprior.pooled=c(3,5),
emprior.actor=c(3,5),epprior.actor=c(3,5))
#Print the results[打印结果]
b
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。
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发表于 2012-9-30 10:47:54
