Information Imbalance Gain

Usage

imbalance_gain(
  mx,
  my,
  alpha = seq(0, 1, 0.1),
  lib = NULL,
  pred = NULL,
  h = 1,
  k = 3,
  threads = 1,
  method = "euclidean"
)

Arguments

mx: Numeric matrix of hypothesised driving variable measurements.
my: Numeric matrix of hypothesised response variable measurements.
alpha: (optional) Scaling parameter weighting the putative driver measurements.
lib: (optional) Library indices.
pred: (optional) Prediction indices.
h: (optional) Prediction horizon.
k: (optional) Number of nearest neighbors when estimating ranks.
threads: (optional) Number of parallel threads.
method: (optional) Distance measure to be used: "euclidean", "manhattan", or maximum".

Value

A numeric vector.

References

Del Tatto, V., Fortunato, G., Bueti, D., Laio, A., 2024. Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks. Proceedings of the National Academy of Sciences 121.

Examples

set.seed(42)
mx = embed(rnorm(100), 3)
my = embed(rnorm(100), 3)
infoxtr::imbalance_gain(mx, my)
#>  [1] 0.4302264 0.4282425 0.4576469 0.4392249 0.4645198 0.4660077 0.4584972
#>  [8] 0.4920112 0.5017891 0.5073865 0.5269423