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Information Gain

Source: R/imbalance_gain.R
imbalance_gain.Rd

Estimates the information imbalance of two hypothesised linked system measurements using distance ranks.

Usage

imbalance_gain(info_imbalance)

Source

Del Tatto, V., Bueti, D. & Laio, A. (2024) Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks. PNAS 121 (19) e2317256121.

Arguments

info_imbalance

Dataframe outputted by `tuneII()`.

Value

A dataframe of the optimal alpha and the estimated information gain.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Embed the spp_4 and spp_3 of the third community

embedX <- embed_ts(X = simTransComms$community3[,c("time","spp_4")],
E = 5, tau = 1)

embedY <- embed_ts(X = simTransComms$community3[,c("time","spp_3")],
E = 5, tau = 1)

alphas <- seq(from = 0, to = 1, by = 0.1)

#Estimate the forward information imbalance
#between spp_4 and spp_3

egII_for <- tuneII(columns = embedX[,-1], target = embedY[,-1],
tau = 1, alphas = alphas, k = 5)
#> Warning: executing %dopar% sequentially: no parallel backend registered

#Estimate the reverse information imbalance
#between spp_4 and spp_3

egII_rev <- tuneII(columns = embedY[,-1], target = embedX[,-1],
tau = 1, alphas = alphas, k = 5)

#Calculate the information gain
igain_for <- imbalance_gain(egII_for)
igain_rev <- imbalance_gain(egII_rev)