mrnet {minet} | R Documentation

## Maximum Relevance Minimum Redundancy

### 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.

### Usage

    mrnet( 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`.

### Details

Consider a supervised learning task, where the output is denoted by Y and V is the set of input variables. The method ranks the set V of inputs according to a score that is the difference between the mutual information with the output variable Y (maximum relevance) and the average mutual information with the previously ranked variables (minimum redundancy). The greedy search starts by selecting the variable Xi having the highest mutual information with the target Y. The second selected variable Xj will be the one that maximizes I(Xj;Y)-I(Xj;Xi). 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.
The MRNET approach consists in repeating this selection procedure for each target variable by putting Y=Xi and V = X\{Xi}, i=1,...,n where X is the set of outcomes of all variables. The weight of each pair Xi,Xj will be the maximum score between the one computed when Xi is the output and the one computed when Xj is the output.

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

### Author(s)

Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi

### References

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`, `aracne`

### Examples

    data(syn.data)
    mim <- build.mim(discretize(syn.data))
    net <- mrnet(mim)

[Package minet version 1.4.0 Index]