mi_cont: Continuous Mutual Information

In NlinTS: Models for Non Linear Causality Detection in Time Series

Continuous Mutual Information

Description

Continuous Mutual Information

Usage

mi_cont(X, Y, k = 3, algo = "ksg1", normalize = FALSE)

Arguments

X	Integer vector, first time series.
Y	Integer vector, the second time series.
k	Integer argument, the number of neighbors.
algo	A string specifying the Kraskov estimation variant: "ksg1" (default) or "ksg2". These represent the two estimators proposed by Kraskov et al. for mutual information estimation.
normalize	Logical argument (FALSE by default) for the option of normalizing the mutual information by dividing it by the joint entropy.

Details

Computes the Mutual Information between two vectors using the Kraskov estimator.

References

\insertRef

kraskov2004estimatingNlinTS

Examples

library (timeSeries)
library (NlinTS)
#load data
data = LPP2005REC
print (mi_cont (data[,1], data[,2], 3, 'ksg1'))
print (mi_cont (data[,1], data[,2], 3, 'ksg2'))

NlinTS documentation built on March 23, 2026, 5:07 p.m.