redist(sna)
redist()所属R语言包：sna
Find a Matrix of Distances Between Positions Based on Regular Equivalence
找到一个矩阵的基础上定期对等的位置之间的距离
译者：生物统计家园网 机器人LoveR
描述----------Description----------
redist uses the graphs indicated by g in dat to assess the extent to which each vertex is regularly equivalent; method determines the measure of approximate equivalence which is used (currently, only CATREGE).
redist使用的图形表示：“g在dat，以评估在何种程度上的每个顶点是定期同等学历; method决定的措施近似等价的（目前，只有CATREGE）。
用法----------Usage----------
redist(dat, g = NULL, method = c("catrege"), mode = "digraph",
diag = FALSE, seed.partition = NULL, code.diss = TRUE, ...)
参数----------Arguments----------
参数：dat
a graph or set thereof.
其图形或一组。
参数：g
a vector indicating which elements of dat should be examined (by default, all are used).
一个向量的元素dat应检查（默认情况下，所有被使用）。
参数：method
method to use when assessing regular equivalence (currently, only "catrege").
使用方法进行评估时经常等价（目前，只有"catrege"）。
参数：mode
"digraph" for directed data, otherwise "graph".
"digraph"定向数据，否则"graph"。
参数：diag
logical; should diagonal entries (loops) should be treated as meaningful data?
逻辑;对角线项（循环）应被视为有意义的数据吗？
参数：seed.partition
optionally, an initial equivalence partition to “seed” the CATREGE algorithm.
可选的初始等价类划分为“种子”的CATREGE的算法。
参数：code.diss
logical; return as dissimilarities (rather than similarities)?
逻辑;返回相异（而不是相似性）？
参数：...
additional parameters (currently ignored).
额外的参数（目前忽略不计）。
Details
详细信息----------Details----------
redist provides a basic tool for assessing the (approximate) regular equivalence of actors. Two vertices i and j are said to be regularly equivalent with respect to role assignment r if {r(u): u in N^+(i)} = {r(u): u in N^+(j)} and {r(u): u in N^-(i)} = {r(u): u in N^-(j)}, where N^+ and N^- denote out- and in-neighborhoods (respectively). RE similarity/difference scores are computed by method, currently Borgatti and Everett's CATREGE algorithm (which is based on the multiplex maximal regular equivalence on G and its transpose). The “distance” between positions in this case is the inverse of the number of iterative refinements of the initial equivalence (i.e., role) structure required to allocate the positions to regularly equivalent roles (with 0 indicating positions which ultimately belong in the same role). By default, the initial equivalence structure is one in which all vertices are treated as occupying the same role; the seed.partition option can be used to impose alternative constraints. From this initial structure, vertices within the same role having non-identical mixes of neighbor types are re-allocated to different roles (where “neighbor type” is initially due to the pattern of (possibly valued) in- and out-ties, cross-classified by current alter type). This procedure is then iterated until no further division of roles is necessary to satisfy the regularity condition.
redist提供了一个基本的工具，评估的（近似）定期等价的演员。两个顶点i和j说是定期等效角色分配r如果{r(u): u in N^+(i)} = {r(u): u in N^+(j)}和{r(u): u in N^-(i)} = {r(u): u in N^-(j)}，其中N^+和 N^-表示，在居民区（分别）。重相似性/差值计算的method，目前博尔加蒂的Everett的CATREGE算法（它是基于复用最大的正则等价于G和它的转置）。在这种情况下的位置之间的“距离”是需要定期等效的角色（与0指示位置最终属于相同的角色分配的位置的数量的初始等价的迭代优化结构（即，角色）的逆）。默认情况下，初始等价的结构中，所有顶点都被视为占用相同的作用; seed.partition选项可以用来施加替代的约束。从这个初始结构，相同的作用，具有不相同的混合类型的邻居内的顶点被重新分配给不同的角色（其中，“邻居”的图案（可能值）和关系最初是由于，交叉分类的当前圣坛类型）。然后，此过程被迭代，直到没有进一步的角色分工是必要的，以满足正则条件。
Once the similarities/differences are calculated, the results can be used with a clustering routine (such as equiv.clust) or an MDS (such as cmdscale) to identify the underlying role structure.
的相似/差异计算后，结果可以用一个聚类程序（如equiv.clust）或MDS（如cmdscale），以确定潜在的角色结构。
值----------Value----------
A matrix of similarity/difference scores.
矩阵的相似性/差值。
注意----------Note----------
The maximal regular equivalence is often very uninteresting (i.e., degenerate) for unvalued, undirected graphs. An exogenous constraint (e.g., via the seed.partition) may be required to uncover a more useful refinement of the unconstrained maximal equivalence.
最大定期等价往往是非常无趣的（即退化）为不定值保险单的，无向图。可能需要外源性的约束（例如，通过seed.partition），无约束的最大等价发现了更多有用的细化。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Borgatti, S.P. and Everett, M.G. (1993). “Two Algorithms for Computing Regular Equivalence.” Social Networks, 15, 361-376.
参见----------See Also----------
sedist, equiv.clust
sedist，equiv.clust
实例----------Examples----------
#Create a random graph with _some_ edge structure[创建一个随机的图形_some_边缘结构]
g.p<-sapply(runif(20,0,1),rep,20) #Create a matrix of edge [创建一个矩阵的边缘]
#probabilities[概率]
g<-rgraph(20,tprob=g.p) #Draw from a Bernoulli graph [从伯努利图绘制]
#distribution[分配]
#Get RE distances[RE距离]
g.re<-redist(g)
#Plot a metric MDS of vertex positions in two dimensions[绘制的顶点位置在两个方面度量MDS]
plot(cmdscale(as.dist(g.re)))
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 11:00:55
