fundamentalNetworkConcepts(WGCNA)
fundamentalNetworkConcepts()所属R语言包：WGCNA
Calculation of fundamental network concepts from an adjacency matrix.
邻接矩阵计算基本的网络概念。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes fundamental network concepts (also known as network indices or statistics) based on an adjacency matrix and optionally a node significance measure. These network concepts are defined for any symmetric adjacency matrix (weighted and unweighted). The network concepts are described in Dong and Horvath (2007) and Horvath and Dong (2008). Fundamental network concepts are defined as a function of the off-diagonal elements of an adjacency matrix adj and/or a node significance measure GS.
该函数计算基本的网络概念（也被称为网络指数或统计数据）的基础上的邻接矩阵和任选的节点意义度量。这些网络的概念被定义为任何对称邻接矩阵（加权和不加权）。董和霍瓦特（2007）和霍瓦特和董（2008年）中描述的网络概念。基本的网络概念被定义作为邻接矩阵adj和/或节点的意义措施GS的非对角（off-diagonal）的元素的函数。
用法----------Usage----------
fundamentalNetworkConcepts(adj, GS = NULL)
参数----------Arguments----------
参数：adj
an adjacency matrix, that is a square, symmetric matrix with entries between 0 and 1
邻接矩阵，即0和1之间的条目是一个正方形，对称矩阵
参数：GS
a node significance measure: a vector of the same length as the number of rows (and columns) of the adjacency matrix.
一个节点的意义措施：（和列）的邻接矩阵的行数相同的长度的矢量。
值----------Value----------
A list with the following components:
以下组件列表：
参数：Connectivity
a numerical vector that reports the connectivity (also known as degree) of each node. This fundamental network concept is also known as whole network connectivity. One can also define the scaled connectivity K=Connectivity/max(Connectivity) which is used for computing the hub gene significance.
报告每个节点的连接（也称为度）的数值矢量。这个基本的网络概念也被称为整个网络的连接。人们还可以定义缩放连通K=Connectivity/max(Connectivity)，其用于计算轮毂基因意义。
参数：ScaledConnectivity
the Connectivity vector scaled by the highest connectivity in the network, i.e., Connectivity/max(Connectivity).
Connectivity最高的连接在网络上，即缩放矢量，Connectivity/max(Connectivity)。
参数：ClusterCoef
a numerical vector that reports the cluster coefficient for each node. This fundamental network concept measures the cliquishness of each node.
一个数值向量，报告的每个节点的聚类系数。这个基本的网络概念测量每个节点的cliquishness。
参数：MAR
a numerical vector that reports the maximum adjacency ratio for each node. MAR[i] equals 1 if all non-zero adjacencies between node i and the remaining network nodes equal 1. This fundamental network concept is always 1 for nodes of an unweighted network. This is a useful measure for weighted networks since it allows one to determine whether a node has high connectivity because of many weak connections (small MAR) or because of strong (but few) connections (high MAR), see Horvath and Dong 2008.
一个数值向量，报告的每个节点的最大邻接比。 MAR[i]等于1，如果所有非零的邻接节点i和剩余的网络节点等于1之间。这个基本的网络概念始终是不加权的网络节点。这是一个有用的指标，加权网络，因为它允许一个确定节点是否具有较高的连接，因为许多弱连接（小MAR），或因为强大的（但很少）连接（MAR），2008年霍瓦特和董。
参数：Density
the density of the network.
网络的密度。
参数：Centralization
the centralization of the network.
集中的网络。
参数：Heterogeneity
the heterogeneity of the network.
异质性的网络。
（作者）----------Author(s)----------
Steve Horvath
参考文献----------References----------
4(8): e1000117
参见----------See Also----------
conformityBasedNetworkConcepts for calculation of conformity based network concepts for a network adjacency matrix;
conformityBasedNetworkConcepts整合基于网络的网络邻接矩阵的概念，计算方法;
networkConcepts, for calculation of conformity based and eigennode based network concepts for a correlation network.
networkConcepts，符合基于eigennode基于网络概念的相关网络的计算。
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 2012-10-1 21:13:19
