mrnet {parmigene}

Maximum Relevance Minimum Redundancy

Description

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

Usage

mrnet(mi)

Arguments

mi

matrix of the mutual information.

Details

The MRNET approach starts by selecting the variable X_i having the highest mutual information with the target Y.

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

I(X_k;Y) - mean(I(X_k;X_i))

for all X_i 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.

Value

A square weighted adjacency matrix of the inferred network.

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)