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R语言 tnet包 Cross.Parker.Manufacturing.net.info()函数中文帮助文档(中英文对照)

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发表于 2012-10-1 10:58:36 | 显示全部楼层 |阅读模式
Cross.Parker.Manufacturing.net.info(tnet)
Cross.Parker.Manufacturing.net.info()所属R语言包:tnet

                                         Intra-organisational networks
                                         组织内部网络

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

This dataset contains two intra-organizational networks from a research team in a manufacturing  company (77 employees). These networks was used by Cross and Parker (2004). <br><br> In the first network, the ties among the researchers are differentiated in terms of advice ("Please indicate the extent to which the people listed below provide you with information you use to accomplish your work"). The weights are based on the following scale: 0: I Do Not Know This Person/I Have Never Met this Person; 1: Very Infrequently; 2: Infrequently; 3: Somewhat Infrequently; 4: Somewhat Frequently; 5: Frequently; and 6: Very Frequently.<br><br> The second network is based on the employees' awareness of each others' knowledge and skills ("I understand this person's knowledge and skills. This does not necessarily mean that I have these skills or am knowledgeable in these domains but that I understand what skills this person has and domains they are knowledgeable in"). The weight scale in this network is: 0: I Do Not Know This Person/I Have Never Met this Person; 1: Strongly Disagree; 2: Disagree; 3: Somewhat Disagree; 4: Somewhat Agree; 5: Agree; and 6: Strongly Agree.<br><br> In addition to the relational data, the dataset also contains information about the people (nodal attributes). The following attributes are known: location (1: Paris; 2: Frankfurt; 3: Warsaw; 4: Geneva), tenure (1: 1-12 months; 2: 13-36 months; 3: 37-60 months; 4: 61+ months) and the organisational level (1: Global Dept Manager; 2: Local Dept Manager; 3: Project Leader; 4: Researcher). <br><br>
此数据集包含两个组织内部网络的一个研究小组在一家制造公司(77名)。这些网络使用的红十字会和帕克(2004)。在第一个网络<BR> <BR>,研究人员之间的关系是有区别的意见(“请注明下面列出的人在何种程度上提供你的信息,你用它来完成你的工作”)。的权数是根据以下规模:0:我不知道这个人/我从来没有见过这个人; 1:很少; 2:低,3:有些不常4:有点频繁;:常见; 6非常频繁。参考<BR>第二个网络是基于员工的意识,每个人的知识和技能(“我了解这个人的知识和技能。这并不意味着我有这些技能,或者是在这些领域的知识,但我明白这个人有什么样的技能和域他们熟悉“)。在这个网络中的体重秤是:0:我不知道这个人/我从来没有见过这个人; 1:非常不同意; 2:不同意; 3:有点不同意,4:有些同意5:同意; 6:强烈同意。<br> <br>在除关系数据,该数据集包含的信息的人(节点属性)。被称为以下属性:位置(1:巴黎2:法兰克福3:华沙4:日内瓦),年期(1-12个月,2:13-36个月,3:37-60个月; 4: 61 +个月),组织级别(1:全球部经理; 2:本地部经理; 3:项目负责人;研究员)。参考参考


用法----------Usage----------


Cross.Parker.Manufacturing.net.info
Cross.Parker.Manufacturing.net.aware
Cross.Parker.Manufacturing.node.location
Cross.Parker.Manufacturing.node.orglevel
Cross.Parker.Manufacturing.node.tenure



格式----------Format----------

The networks are data frames with three columns. The first column is the id of the sender; the second column is the id of the receiver; and the third column is the weight of the tie. The nodal attributes are vectors.
这些网络的数据框,三列。第一列是发送方的id,第二列是接收机的id,第三列是重量的领带。节点属性的向量。


参考文献----------References----------


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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