mi_cont: Continuous Mutual Information

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

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	String argument specifies the algorithm use ("ksg1", "ksg2"), as tow propositions of Kraskov estimation are provided. The first one ("ksg1") is used by default.
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'))

Example output

Loading required package: Rcpp
Loading required package: timeDate
[1] 0.2535239
[1] 4.476037

NlinTS documentation built on Feb. 2, 2021, 9:07 a.m.