R: multiinformation computation

multiinformation {infotheo} | R Documentation

## multiinformation computation

### Description

`multiinformation` takes a dataset as input and computes the multiinformation (also called total correlation) among the random variables in the dataset. The value is returned in nats using the entropy estimator `estimator`.

### Usage

    multiinformation(X, method ="emp")

### Arguments

`X` | data.frame containing a set of random variables where columns contain variables/features and rows contain outcomes/samples.
`method` | The name of the entropy estimator. The package implements four estimators : "emp", "mm", "shrink", "sg" (default:"emp") - see details. These estimators require discrete data values - see `discretize`.

### Details

- "emp" : This estimator computes the entropy of the empirical probability distribution.

- "mm" : This is the Miller-Madow asymptotic bias corrected empirical estimator.

- "shrink" : This is a shrinkage estimate of the entropy of a Dirichlet probability distribution.

- "sg" : This is the Schurmann-Grassberger estimate of the entropy of a Dirichlet probability distribution.

### Value

`multiinformation` returns the multiinformation (also called total correlation) among the variables in the dataset (in nats).

### Author(s)

Patrick E. Meyer

### References

Meyer, P. E. (2008). Information-Theoretic Variable Selection and Network Inference from Microarray Data. PhD thesis of the Universite Libre de Bruxelles.

Studeny, M. and Vejnarova, J. (1998). The multiinformation function as a tool for measuring stochastic dependence. In Proceedings of the NATO Advanced Study Institute on Learning in graphical models,

### See Also

`condinformation`, `mutinformation`, `interinformation`, `natstobits`

### Examples

      data(USArrests)
      dat<-discretize(USArrests)
      M <- multiinformation(dat)

[Package infotheo version 1.2.0 Index]