tuneII {EWSmethods}

Information Imbalance Across Alphas

Description

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

Usage

tuneII(columns, target, tau, alphas, k = 1, method = "euclidean")

Arguments

columns

Numeric matrix of hypothesised driving variable measurements. If univariate, call 'embedTS(X)' prior to calling 'II()'.

target

Numeric matrix of hypothesised response variable measurements. If univariate, call 'embedTS(Y)' prior to calling 'II()'.

tau

Numeric. Time lag of information transfer between X and Y.

alphas

Numeric vector. Range of X scaling parameters bewtween '0' & '1' inclusive. If information imbalance is minimised at an 'alpha' > 0, this may be indicative of Granger causality.

k

Numeric. Number of nearest neighbours when estimating ranks.

method

String. Distance measure to be used - defaults to 'euclidean' but see '?dist' for options.

Value

A dataframe of alphas and the estimate information 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.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Embed the spp_2 and spp_5 of the third community

embedX <- embed_ts(X = simTransComms$community3[,c("time","spp_2")],
E = 5, tau = 1)

embedY <- embed_ts(X = simTransComms$community3[,c("time","spp_5")],
E = 5, tau = 1)

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


#if parallelisation desired,
#this can be achieved using the
#below code
cl <- parallel::makeCluster(2)



doParallel::registerDoParallel(cl)


#Estimate the forward information imbalance
#between spp_2 and spp_5

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

#Estimate the reverse information imbalance
#between spp_2 and spp_5

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


parallel::stopCluster(cl)