brokerage(sna)
brokerage()所属R语言包：sna
Perform a Gould-Fernandez Brokerage Analysis
执行古尔德 - 费尔南德斯经纪分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs the brokerage analysis of Gould and Fernandez on one or more input graphs, given a class membership vector.
执行古尔德和Fernandez的经纪分析在一个或多个输入曲线图，给出一个类成员资格向量。
用法----------Usage----------
brokerage(g, cl)
参数----------Arguments----------
参数：g
one or more input graphs.
一个或多个输入图表。
参数：cl
a vector of class memberships.
一个向量类的成员资格。
Details
详细信息----------Details----------
Gould and Fernandez (following Marsden and others) describe brokerage as the role played by a social actor who mediates contact between two alters. More formally, vertex v is a broker for distinct vertices a and b iff a -> v -> b and a -!> b. Where actors belong to a priori distinct groups, group membership may be used to segment brokerage roles into particular types. Let A -> B -> C denote the two-path associated with a brokerage structure, such that some vertex from group B brokers the connection from some vertex from group A to a vertex in group C. The types of brokerage roles defined by Gould and Fernandez (and their accompanying two-path structures) are then defined in terms of group membership as follows:
古尔德和费尔南德斯（Marsden和其他）描述的社会角色所扮演的角色之间改变介导接触的经纪佣金。更正式地说，顶点v是不同的顶点a和b当且仅当a -> v -> b和a -!> b的经纪人。凡演员属于先验不同的组，组成员可使用段经纪角色到特定类型。让我们A -> B -> C表示两路径与一个经纪结构，这样，一些从某个顶点顶点组B经纪人的连接组A的一个顶点组C 。古尔德和费尔南德斯（及其随行的两路构）筑物的类型定义的经纪角色，然后定义组成员如下：
w_I: Coordinator role; the broker mediates contact between two individuals from his or her own group. Two-path structure: A -> A -> A
w_I：协调员的作用;的经纪人介导从他或她自己组的两个人之间的联系。双路径结构：A -> A -> A
w_O: Itinerant broker role; the broker mediates contact between two individuals from a single group to which he or she does not belong. Two-path structure: A -> B -> A
w_O：巡回经纪人的作用;经纪介导，他或她不属于一个组的两个个体之间的联系。双路径结构：A -> B -> A
b_{IO}: Gatekeeper role; the broker mediates an incoming contact from an out-group member to an in-group member. Two-path structure: A -> B -> B
b_{IO}：看门人的角色，经纪人介导的传入联络的出组成员的组成员。双路径结构：A -> B -> B
b_{OI}: Representative role; the broker mediates an outgoing contact from an in-group member to an out-group member. Two-path structure: A -> A -> B
b_{OI}：代表作用;经纪中介传出接触到了组成员组成员。双路径结构：A -> A -> B
b_O: Liaison role; the broker mediates contact between two individuals from different groups, neither of which is the group to which he or she belongs. Two-path structure: A -> B -> C
b_O：联络作用;经纪介导两个人从不同群体之间的接触，这都不是他或她所属的组。双路径结构：A -> B -> C
t: Total (cumulative) brokerage role occupancy. (Any of the above two-paths.) </ul>
t：总（累计）的经纪角色占用。 （在任何上述的两个路径。）</ UL>
The brokerage score for a given vertex with respect to a given role is the number of ordered pairs having the appropriate group membership(s) brokered by said vertex. brokerage computes the brokerage scores for each vertex, given an input graph and vector of class memberships. Aggregate scores are also computed at the graph level, which correspond to the total frequency of each role type within the network structure. Expectations and variances of the brokerage scores conditional on size and density are computed, along with approximate z-tests for incidence of brokerage. (Note that the accuracy of the normality assumption is not known in the general case; see Gould and Fernandez (1989) for details. Simulation-based tests may be desirable as an alternative.)
对于一个给定的顶点，对于一个给定的角色是经纪得分下令对相应的组成员（S）的斡旋下，顶点的数量。 brokerage计算每一个顶点，输入图形和矢量类会员资格的经纪分数。总成绩的曲线图的水平，也可以计算在对应于每个角色类型的网络结构内的总频次。期望和方差的大小和密度的条件经纪分数，计算随着近似z测试的发病率的经纪。 （请注意，在一般情况下，还没有已知的正态性假设的准确性;有关详细信息，请参阅Gould和费尔南德斯（1989）。作为替代，基于仿真的测试可能是可取的。）
值----------Value----------
An object of class brokerage, containing the following elements: <table summary="R valueblock"> <tr valign="top"><td>raw.nli </td> <td> The matrix of observed brokerage scores, by vertex</td></tr> <tr valign="top"><td>exp.nli </td> <td> The matrix of expected brokerage scores, by vertex</td></tr> <tr valign="top"><td>sd.nli </td> <td> The matrix of predicted brokerage score standard deviations, by vertex</td></tr> <tr valign="top"><td>z.nli </td> <td> The matrix of standardized brokerage scores, by vertex</td></tr> <tr valign="top"><td>raw.gli </td> <td> The vector of observed aggregate brokerage scores</td></tr> <tr valign="top"><td>exp.gli </td> <td> The vector of expected aggregate brokerage scores</td></tr> <tr valign="top"><td>sd.gli </td> <td> The vector of predicted aggregate brokerage score standard deviations</td></tr> <tr valign="top"><td>z.gli </td> <td> The vector of standardized aggregate brokerage scores</td></tr> <tr valign="top"><td>exp.grp </td> <td> The matrix of expected brokerage scores, by group</td></tr> <tr valign="top"><td>sd.grp </td> <td> The matrix of predicted brokerage score standard deviations, by group</td></tr> <tr valign="top"><td>cl </td> <td> The vector of class memberships</td></tr> <tr valign="top"><td>clid </td> <td> The original class names</td></tr> <tr valign="top"><td>n </td> <td> The input class sizes</td></tr> <tr valign="top"><td>N </td> <td> The order of the input network</td></tr> </table>
对象的类brokerage，包含以下元素：<table summary="R valueblock"> <tr valign="top"> <TD> raw.nli </ TD> <TD>的矩阵观察到的的经纪分数，顶点</ TD> </ TR> <tr valign="top"> <TD>exp.nli </ TD> <TD>矩阵的预期经纪分数，由顶点</ TD> </ TR> <tr valign="top"> <TD> sd.nli </ TD> <TD>预测经纪得分标准差的矩阵，由顶点</ TD> </ TR> <TR VALIGN = “顶”> <TD> z.nli </ TD> <TD>矩阵的标准化的经纪成绩，顶点</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>的矢量观测到的总经纪分数</ TD> </ TR> <tr valign="top"> <TD> raw.gli </ TD> <TD>的向量，预计总的经纪分数</ TD> </ TR> <tr valign="top"> <TD>exp.gli </ TD> <TD>的矢量预测的总经纪得分标准差</ TD> </ TR> <tr valign="top"> <TD>sd.gli </ TD> <TD>的向量标准化骨料经纪分数</ TD> </ TR> <tr valign="top"> <TD >z.gli </ TD> <TD>的预期经纪分数，矩阵，由组</ TD> </ TR> <tr valign="top"> <TD> exp.grp </ TD> <TD>的矩阵预测经纪得分的标准差，组</ TD> </ TR> <tr valign="top"> <TD>sd.grp </ TD> <TD>的矢量类会员资格</ TD> </ TR> <tr valign="top"> <TD>cl </ TD> <TD>原来的类名</ TD> </ TR> <TR VALIGN =“顶部> <TD> clid </ TD> <TD>输入类的大小</ TD> </ TR> <tr valign="top"> <TD> n </ TD> <TD输入网络的顺序</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
<h3>See Also</h3>
实例----------Examples----------
#Draw a random network with 3 groups[绘制一个随机的网络与3组]
g<-rgraph(15)
cl<-rep(1:3,5)
#Compute a brokerage object[计算一个经纪对象]
b<-brokerage(g,cl)
summary(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。
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 10:48:45
