consensus(sna)
consensus()所属R语言包：sna
Estimate a Consensus Structure from Multiple Observations
估计一个共识结构从多个观测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
consensus estimates a central or consensus structure given multiple observations, using one of several algorithms.
consensus估计中央或共识结构多观察，使用的几种算法。
用法----------Usage----------
consensus(dat, mode="digraph", diag=FALSE, method="central.graph",
tol=1e-06, maxiter=1e3, verbose=TRUE, no.bias=FALSE)
参数----------Arguments----------
参数：dat
a set of input graphs (must have same order).
输入图的一组（必须有相同的顺序）。
参数：mode
"digraph" for directed data, else "graph".
"digraph"定向数据，否则"graph"。
参数：diag
logical; should diagonals (loops) be treated as data?
逻辑，对角线（循环）被视为数据？
参数：method
one of "central.graph", "single.reweight", "iterative.reweight", "romney.batchelder", "PCA.reweight", "LAS.intersection", "LAS.union", "OR.row", or "OR.col".
"central.graph"，"single.reweight"，"iterative.reweight"，"romney.batchelder"，"PCA.reweight"，"LAS.intersection"，"LAS.union"，"OR.row"，或"OR.col"。
参数：tol
convergence tolerance for the iterative reweighting and B-R algorithms.
迭代权重调整和BR算法的收敛公差。
参数：maxiter
maximum number of iterations to take (regardless of convergence) for the iterative reweighting and B-R algorithms.
最大迭代次数（权重调整和BR算法的迭代收敛）。
参数：verbose
logical; should bias and competency parameters be reported (where computed)?
逻辑，报告偏见和能力参数（如果计算）？
参数：no.bias
logical; should responses be assumed to be unbiased?
逻辑;反应应被假定为是公正的吗？
Details
详细信息----------Details----------
The term “consensus structure” is used by a number of authors to reflect a notion of shared or common perceptions of social structure among a set of observers. As there are many interpretations of what is meant by “consensus” (and as to how best to estimate it), several algorithms are employed here:
“共识结构”所使用的一些作者，以反映概念的共享或公共的社会结构的认识在一组的观察员。由于有很多的解释是什么意思“共识”（以及如何以最佳估计），采用几种算法在这里：
<ol> central.graph: Estimate the consensus structure using the central graph. This corresponds to a “median response” notion of consensus.
<OL>central.graph：估计的共识结构，使用中央图。这相当于一个“平均响应”概念的共识。
single.reweight: Estimate the consensus structure using subject responses, reweighted by mean graph correlation. This corresponds to an “expertise-weighted vote” notion of consensus.
single.reweight：估计结构，使用对象的反应，再加权平均图形相关的共识。这相当于一个“专业知识加权票”的概念达成共识。
iterative.reweight: Similar to single.reweight, but the consensus structure and accuracy parameters are estimated via an iterated proportional fitting scheme. The implementation employed here uses both bias and competency parameters.
iterative.reweight：类似single.reweight，但达成共识的结构和精度参数估计通过一个迭代的比例装修方案。这里采用的实施，使用参数偏差和竞争力。
romney.batchelder: Fits a Romney-Batchelder informant accuracy model using IPF. This is very similar to iterative.reweight, but can be interpreted as the result of a process in which each informant report is correct with a probability equal to the informant's competency score, and otherwise equal to a Bernoulli trial with parameter equal to the informant's bias score.
romney.batchelder：适合使用IPF一个罗姆尼的巴彻尔德的线人准确度模型。这是非常类似于iterative.reweight，但可以解释为一个过程，其中每个线人报告是正确的概率等于线人的能力得分，否则等于参数等于一个伯努利试验的结果报案人的偏见得分。
PCA.reweight: Estimate the consensus using the (scores on the) first component of a network PCA. This corresponds to a “shared theme” or “common element” notion of consensus.
PCA.reweight：估算的共识使用的（分数）网络PCA的第一个组成部分。这相当于一个“共同主题”或“共同元素”概念的共识。
LAS.intersection: Estimate the consensus structure using the locally aggregated structure (intersection rule). In this model, an i->j edge exists iff i and j agree that it exists.
LAS.intersection：估计的共识结构采用局部聚集结构（交叉口规则）。在这个模型中，存在的i-> j的边，当且仅当i和j同意它的存在。
LAS.union: Estimate the consensus structure using the locally aggregated structure (union rule). In this model, an i->j edge exists iff i or j agree that it exists.
LAS.union：估计的共识结构，使用局部聚集结构（工会规则）。在这个模型中，存在的i-> j的边，当且仅当i和j同意它的存在。
OR.row: Estimate the consensus structure using own report. Here, we take each informant's outgoing tie reports to be correct.
OR.row：估计的共识结构，使用自己的报告。在这里，我们需要，每个线人即将离任的领带报告是正确的。
OR.col: Estimate the consensus structure using own report. Here, we take each informant's incoming tie reports to be correct. </ol>
OR.col：估计的共识结构，使用自己的报告。在这里，我们采取每一个线人的传入领带报告是正确的。 </ OL>
Note that the results returned by the single weighting algorithms are not dichotomized by default; since some algorithms thus return valued graphs, dichotomization may be desirable prior to use.
请注意，返回的结果由单一的加权算法二默认情况下，返回价值，因为有些算法图形，的二分法可能会是可取的前使用。
It should be noted that a model for estimating an underlying criterion structure from multiple informant reports is provided in bbnam; if your goal is to reconstruct an “objective” network from informant reports, this (or the R-B model) may prove more useful than the ad-hoc solutions.
应该指出，估计的基本标准结构的模型从多个线人报告中提供bbnam如果你的目标是重建一个“客观”的网络从线人报告，今（或RB模型）证明有用特设的解决方案。
值----------Value----------
An adjacency matrix representing the consensus structure
邻接矩阵表示的共识结构
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Banks, D.L., and Carley, K.M. (1994). “Metric Inference for Social Networks.” Journal of Classification, 11(1), 121-49.
Butts, C.T., and Carley, K.M. (2001). “Multivariate Methods for Inter-Structural Analysis.” CASOS Working Paper, Carnegie Mellon University.
Krackhardt, D. (1987). “Cognitive Social Structures.” Social Networks, 9, 109-134.
Romney, A.K.; Weller, S.C.; and Batchelder, W.H. (1986). “Culture as Consensus: A Theory of Culture and Informant Accuracy.” American Anthropologist, 88(2), 313-38.
参见----------See Also----------
bbnam, centralgraph
bbnam，centralgraph
实例----------Examples----------
#Generate some test data[生成一些测试数据。]
g<-rgraph(5)
g.pobs<-g*0.9+(1-g)*0.5
g.obs<-rgraph(5,5,tprob=g.pobs)
#Find some consensus structures[查找了一些共识结构]
consensus(g.obs) #Central graph[中央图]
consensus(g.obs,method="single.reweight") #Single reweighting[单权重调整]
consensus(g.obs,method="PCA.reweight") #1st component in network PCA[在网络PCA的第一组件]
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 10:49:41
