mrnet(minet)
mrnet()所属R语言包：minet
Maximum Relevance Minimum Redundancy
最大的关联性最小冗余
译者：生物统计家园网 机器人LoveR
描述----------Description----------
mrnet takes the mutual information matrix as input in order to infer the network using the maximum relevance/minimum redundancy feature selection method - see details.
mrnet互信息作为输入矩阵，以推断网络使用的最大相关性/最小冗余特征选择方法 - 查看详细信息。
用法----------Usage----------
mrnet(mim)
参数----------Arguments----------
参数：mim
A square matrix whose i,j th element is the mutual information between variables X_i and X_j - see build.mim.
一个方阵的我，第j个元素是变量之间的相互信息X_i和X_j - 看到build.mim。
Details
详情----------Details----------
The MRNET approach consists in repeating a MRMR feature selection procedure for each variable of the dataset. The MRMR method starts by selecting the variable Xi having the highest mutual information with the target Y. In the following steps, given a set S of selected variables, the criterion updates S by choosing the variable Xk that maximizes I(Xk;Y) - mean(I(Xk;Xi)), Xi in S.<br> The weight of each pair Xi,Xj will be the maximum score between the one computed when Xi is the target and the one computed when Xj is the target.
MRNET方法包含重复1 MRMR特征选择过程，为每个数据集的变量。 MRMR方法开始选择变量Xi最高目标Y互信息。在下面的步骤，给出了一套S选定的变量，标准更新S选择变量Xk， I(Xk;Y) - mean(I(Xk;Xi)), Xi in S.参考每对重量最大化 Xi,Xj将一个计算机之间的最高得分，当Xi是目标和一个计算机时Xj目标。
值----------Value----------
mrnet returns a matrix which is the weighted adjacency matrix of the network. In order to display the network, load the package Rgraphviz and use the following command: <br> plot( as( returned.matrix ,"graphNEL") )
mrnet返回一个矩阵，这是网络的加权邻接矩阵。为了显示网络，加载包Rgraphviz和使用下面的命令：参考图（（returned.matrix，“graphNEL”））
作者（S）----------Author(s)----------
Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi
参考文献----------References----------
Information-theoretic inference of large transcriptional regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology, 2007.
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information. BMC Bioinformatics, Vol 9, 2008.
Feature selection based on mutual information: Criteria of max-dependency, max relevance and min redundancy. IEEE transaction on Pattern Analysis and Machine Intelligence, 2005.
参见----------See Also----------
build.mim, clr, aracne, mrnetb
build.mim，clr，aracne，mrnetb
举例----------Examples----------
data(syn.data)
mim <- build.mim(syn.data, estimator="spearman")
net <- mrnet(mim)
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 2012-2-26 00:57:35
