mrnet function

Maximum Relevance Minimum Redundancy

A function that infers the interaction network using the MRNET algorithm.

mrnet(mi)

Arguments

• mi: matrix of the mutual information.

Returns

A square weighted adjacency matrix of the inferred network.

Details

The MRNET approach starts by selecting the variable $Xi$

having the highest mutual information with the target Y.

Then, it repeatedly enlarges the set of selected variables $S$ by taking the $Xk$ that maximizes

for all $Xi$ already in S.

The procedure stops when the score becomes negative.

By default, the function uses all the available cores. You can set the actual number of threads used to N by exporting the environment variable OMP_NUM_THREADS=N.

References

H. Peng, F.long and C.Ding. Feature selection based on mutual information: Criteria of max-dependency, max relevance and min redundancy. IEEE transaction on Pattern Analysis and Machine Intelligence, 2005.

See Also

aracne.a

aracne.m

clr

Examples

mat <- matrix(rnorm(1000), nrow=10)
mi  <- knnmi.all(mat)
grn <- mrnet(mi)