PID.plugin: A plug-in calculator for evaluating partial information...

In Informeasure: R implementation of Information measures

Description

PID.plugin decomposes two source information acting on the common target into four parts: joint information (synergy), unique information from source x, unique information from source y and shared information (redundancy). The input of PMI.plug is the joint probability distribution table.

Usage

PID.plugin(probs, unit = c("log", "log2", "log10"))

Arguments

probs	the joint probability distribution of three random variables.
unit	the base of the logarithm. The default is natural logarithm, which is "log". For evaluating entropy in bits, it is suggested to set the unit to "log2".

Value

PID.plugin returns a list that includes synergistic information, unique information from source x, unique information from source y, redundant information and the sum of the four parts of information.

References

Williams, P. L., & Beer, R. D. (2010). Nonnegative Decomposition of Multivariate Information. arXiv: Information Theory.

Chan, T. E., Stumpf, M. P., & Babtie, A. C. (2017). Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures. Cell systems, 5(3).

Examples

# three numeric vectors corresponding to three continuous random variables
x <- c(0.0, 0.2, 0.2, 0.7, 0.9, 0.9, 0.9, 0.9, 1.0)
y <- c(1.0, 2.0,  12, 8.0, 1.0, 9.0, 0.0, 3.0, 9.0)
z <- c(3.0, 7.0, 2.0,  11,  10,  10,  14, 2.0,  11)

# corresponding joint count table estimated by "uniform width" algorithm
count_xyz <- discretize3D(x, y, z, "uniform_width")

# the joint probability distribution table of the count data
library("entropy")
probs_xyz <- freqs.empirical(count_xyz)

# corresponding partial information decomposition
PID.plugin(probs_xyz)

Example output

    Synergy  Unique_X  Unique_Y Redundancy       PID
1 0.1716555 0.1063459 0.0405159  0.1058255 0.4243428

Informeasure documentation built on Nov. 8, 2020, 7:20 p.m.