multiinformation: multiinformation computation

In Gibbsdavidl/perminfotheo: Information-Theoretic Measures

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)

Gibbsdavidl/perminfotheo documentation built on May 6, 2019, 6:29 p.m.