PMI.measure: A comprehensive function for evaluating part mutual...

In chupan1218/Informeasure: R implementation of informtion measures

A comprehensive function for evaluating part mutual information

Description

The PMI.measure function is used to calculate the non-linearly direct dependencies between two variables conditioned on the third one form the joint count table.

Usage

PMI.measure(
  XYZ,
  method = c("ML", "Jeffreys", "Laplace", "SG", "minimax", "shrink"),
  lambda.probs,
  unit = c("log", "log2", "log10"),
  verbose = TRUE
)

Arguments

XYZ	a joint count distribution table of three random variables.
method	six probability estimation algorithms are available, "ML" is the default.
lambda.probs	the shrinkage intensity, only called when the probability estimator is "shrink".
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".
verbose	a logic variable. if verbose is true, report the shrinkage intensity.

Details

Six probability estimation methods are available to evaluate the underlying bin probability from observed counts:
method = "ML": maximum likelihood estimator, also referred to empirical probability,
method = "Jeffreys": Dirichlet distribution estimator with prior a = 0.5,
method = "Laplace": Dirichlet distribution estimator with prior a = 1,
method = "SG": Dirichlet distribution estimator with prior a = 1/length(XY),
method = "minimax": Dirichlet distribution estimator with prior a = sqrt(sum(XY))/length(XY),
method = "shrink": shrinkage estimator.

Value

PMI.measure returns the part mutual information.

References

Hausser, J., & Strimmer, K. (2009). Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks. Journal of Machine Learning Research, 1469-1484.

Zhao, J., Zhou, Y., Zhang, X., & Chen, L. (2016). Part mutual information for quantifying direct associations in networks. Proceedings of the National Academy of Sciences of the United States of America, 113(18), 5130-5135.

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
XYZ <- discretize3D(x, y, z, "uniform_width")

# corresponding part mutual information
PMI.measure(XYZ)

chupan1218/Informeasure documentation built on Jan. 19, 2024, 5:30 p.m.