mrnetb: Maximum Relevance Minimum Redundancy Backward

In minet: Mutual Information NETworks

Description

mrnetb takes the mutual information matrix as input in order to infer the network using the maximum relevance/minimum redundancy criterion combined with a backward elimination and a sequential replacement - see references. This method is a variant of mrnet.

Usage

mrnetb(mim)

Arguments

mim	A square matrix whose i,j th element is the mutual information between variables X_i and X_j - see build.mim.

Value

mrnetb 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:
plot( as( returned.matrix ,"graphNEL") )

References

Patrick E. Meyer, Daniel Marbach, Sushmita Roy and Manolis Kellis. Information-Theoretic Inference of Gene Networks Using Backward Elimination. The 2010 International Conference on Bioinformatics and Computational Biology.

Patrick E. Meyer, Kevin Kontos, Frederic Lafitte and Gianluca Bontempi. Information-theoretic inference of large transcriptional regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology, 2007.

See Also

build.mim, clr, mrnet, aracne

Examples

data(syn.data)
mim <- build.mim(syn.data, estimator="spearman")
net <- mrnetb(mim)

Example output

minet documentation built on Nov. 8, 2020, 6 p.m.