rNetwork(simone)
rNetwork()所属R语言包：simone
Simulation of (clustered) Gaussian networks
（聚类）高斯网络的模拟
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Simulates a network with various structures.
模拟各种结构的网络。
用法----------Usage----------
rNetwork(p,
pi,
alpha = c(1),
directed = FALSE,
name = "a network",
signed = TRUE)
参数----------Arguments----------
参数：p
the number of nodes of the simulated network.
模拟网络中的节点的数目。
参数：pi
a matrix of cluster connectivity (see details).
聚类连接矩阵（见详情）。
参数：alpha
a vector of cluster proportions.
一个向量的聚类的比例。
参数：directed
a logical indicating the directedness of the network.
一个逻辑指示directedness的网络。
参数：name
a character string indicating the name of the network.
指示网络的名称的字符串。
参数：signed
a logical indicating whether partial correlations should be signed or all kept positive.
逻辑部分的相关性是否应签署或保持积极。
Details
详细信息----------Details----------
Matrix pi should be a square matrix of the same size as vector alpha. When the network is not directed, pi should be symmetric. When the graph is directed, entry π_ql corresponds to edges heading from class q to class l.
矩阵pi应该是向量alpha的大小相同的正方形矩阵。当网络不定向，pi应该是对称的。当图形的指示，进入π_ql对应到从类q边缘标题类l。
Entries of pi can be either integers or real numbers. If they are integers, they are considered as the exact number of edges required from one class to another. Otherwise, they are considered as connectivity probabilities between classes. They should therefore sum up to at most 1. If they do not sum up to one excatly, the remaining value is considered as the probability for a node to belong to the dust class (connected to no other node).
pi的条目可以是整数或实数。如果他们是整数，它们被视为需要从一个类到另一个边缘的确切数目。否则，他们被认为是类之间的连接概率。因此，它们应总结至多为1。如果他们不总结的一个住处，其余的值被认为是作为一个节点属于的粉尘类（没有其他节点连接到）的概率。
值----------Value----------
Returns an object of class simone.network, that is, a list comprising <table summary="R valueblock"> <tr valign="top"><td>A</td> <td> the p x p adjacency matrix of the network, filled with 0 and 1's, which is symmetric if directed is FALSE.</td></tr> <tr valign="top"><td>Theta</td> <td> a p x p matrix of parameters of the associated Gaussian model, which depends on the directedness of the network: if directed, Theta contains the parameters of a VAR(1) model; if undirected, Theta is the concentration matrix (inverse of the covariance matrix) of a Gaussian vector</td></tr> <tr valign="top"><td>directed</td> <td> a logicial indicating the directedness of the network.</td></tr> <tr valign="top"><td>clusters</td> <td> a size-p factor indicating the node class. The number of levels is determined by the number of columns of the matrix of connectivity pi: the levels are labeled 1,...,Q where Q is the number of clusters. </td></tr> <tr valign="top"><td>name</td> <td> a character string containing the name of the network.</td></tr> </table>
返回类simone.network，就是一个列表，包括<table summary="R valueblock"> <tr valign="top"> <TD>A </ TD> <TD>的对象p x p邻接矩阵的网络，充满了0和1的，这是对称的，如果directed是FALSE。</ TD> </ TR> <tr valign="top"> < TD>Theta</ TD> <td>一个p x p directedness的网络，这取决于相关的高斯模型的参数矩阵：如果，Theta包含的参数一个VAR（1）模型;如果无向，Theta的浓度的协方差矩阵的逆矩阵的高斯矢量</ TD> </ TR> <tr valign="top"> <TD> directed </ TD> <td>一个logicial表示directedness的网络。</ TD> </ TR> <tr valign="top"> <TD>clusters </ TD> <TD >大小p表示节点类的因素。的连接矩阵的列的数量来确定的级别数pi：被标记的水平1,...,Q其中Q是聚类数。 </ TD> </ TR> <tr valign="top"> <TD> name</ TD> <td>一个字符串，其中包含的网络名称。</ TD> </ TR> < / TABLE>
（作者）----------Author(s)----------
J. Chiquet, C. Charbonnier
参见----------See Also----------
coNetwork, plot.simone.network.
coNetwork，plot.simone.network。
实例----------Examples----------
## generate an Erdos-Renyi network with 50 nodes and Pr of edges = 0.1[＃产生的鄂尔多斯仁义网络与50个节点和Pr边缘= 0.1]
plot(rNetwork(p = 50, pi = 0.1, name = "an Erdos-Renyi network"))
## generate an network with 15 nodes and 25 randomly selected edges[＃生成一个网络与15个节点和25个随机选择的边]
plot(rNetwork(p = 15, pi = 25, name = "a 25 edges network"))
## generate an undirected network with an affiliation structure[＃生成一个无向网络的联系结构]
PI <- matrix(c(15,2,2,50),2,2)
alpha <- c(1/3,2/3)
plot(rNetwork(p = 20, pi = PI, alpha = alpha,
name = "Affiliation, fixed num of edges"))
## generate a directed network with hubs[＃生成一个向网络中心]
PI <- t(matrix(c(0.2,0.1,0.4,0,0.05,0.15,0,0.4,rep(0,8)),4,4))
alpha <- c(1/20,1/20,9/20,9/20)
plot(rNetwork(p = 55, pi = PI, alpha = alpha, directed = TRUE,
name = "Hubs structured network"))
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 02:51:49
