mi: kNN Mutual Information Estimators

In fastmit: Fast Mutual Information Based Independence Test

Description

Estimate mutual information based on the distribution of nearest neighborhood distances. The kNN method is described by Kraskov, et. al (2004).

Usage

mi(x, y, k = 5, distance = FALSE)

Arguments

x	A numeric vector, matrix, data.frame or dist object.
y	A numeric vector, matrix, data.frame or dist object.
k	Order of neighborhood to be used in the kNN method.
distance	Bool flag for considering x and y as distance matrices or not. If distance = TRUE, x and y would be considered as distance matrices, otherwise, these arguments are treated as data and Euclidean distance would be implemented for the samples in x and y. Default: distance = FALSE.

Details

If two samples are passed to arguments x and y, the sample sizes (i.e. number of rows of the matrix or length of the vector) must agree. Moreover, data being passed to x and y must not contain missing or infinite values.

Value

mi	The estimated mutual information.

References

Kraskov, A., Stögbauer, H., & Grassberger, P. (2004). Estimating mutual information. Physical review E 69(6): 066138.

Examples

library(fastmit)
set.seed(1)
x <- rnorm(100)
y <- x + rnorm(100)
mi(x, y, k = 5, distance = FALSE)

set.seed(1)
x <- rnorm(100)
y <- 100 * x + rnorm(100)
distx <- dist(x)
disty <- dist(y)
mi(distx, disty, k = 5, distance = TRUE)

fastmit documentation built on March 26, 2020, 8:50 p.m.