build.mim {minet} | R Documentation

## Build Mutual Information Matrix

### Description

`build.mim` takes the dataset as input and computes the mutual information beetween all pair of variables according to the mutual inforamtion estimator `estimator`. The results are saved in the mutual information matrix (MIM), a square matrix whose (i,j) element is the mutual information between variables Xi and Xj.

### Usage

    build.mim(data, estimator="mi.empirical")

### Arguments

`data` | data.frame containing gene expression data or any dataset where columns contain variables/features and rows contain outcomes/samples.
`estimator` | The name of the mutual information estimator. The package implements four estimators : "mi.empirical", "mi.mm", "mi.shrink", "mi.sg" (default:"mi.empirical") - see details. These estimators require discrete data values - see `discretize`.

### Details

- "mi.empirical" : This estimator computes the entropy of the empirical probability distribution.
- "mi.mm" : This is the Miller-Madow asymptotic bias corrected empirical estimator.
- "mi.shrink" : This is a shrinkage estimate of the entropy of a Dirichlet probability distribution.
- "mi.sg" : This is the Schurmann-Grassberger estimate of the entropy of a Dirichlet probability distribution.

### Value

`build.mim` returns the mutual information matrix.

### 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.

J. Beirlant, E. J. Dudewica, L. Gyofi, and E. van der Meulen. Nonparametric entropy estimation : An overview. Journal of Statistics, 1997.

Jean Hausser. Improving entropy estimation and the inference of genetic regulatory networks. Master thesis of the National Institute of Applied Sciences of Lyon, 2006.

### See Also

`clr`, `aracne`, `mrnet`

### Examples

      data(syn.data)
      #mutual information estimator
      estimator="mi.empirical"
      #number of bins used to discretize
      nb.bins = sqrt(nrow(syn.data))
      mim <- build.mim(discretize(syn.data,nbins=nb.bins),estimator)

[Package minet version 1.4.0 Index]