uniJI {EWSmethods}

Univariate S-map Jacobian index function

Description

Calculate a stability metric from the s-map estimated Jacobian of a univariate time series

Usage

uniJI(data, winsize = 50, theta_seq = NULL, E = 1, tau = NULL, scale = TRUE)

Arguments

data

Numeric matrix with time in first column and species abundance in the second

winsize

Numeric. Defines the window size of the rolling window as a percentage of the time series length.

theta_seq

Numeric vector of thetas (nonlinear tuning parameters) to estimate the Jacobian over. If 'NULL', a default sequence is provided.

E

Numeric. The embedding dimension. Is suggested to be positive.

tau

Numeric. The time-delay offset to use for time delay embedding. Suggested to be positive here, but if not provided, is set to 10% the length of the time series.

scale

Boolean. Should data be scaled prior to estimating the Jacobian.

Value

A dataframe where the first column is last time index of the window and the second column is the estimated index value. A value <1.0 indicates stability, a value >1.0 indicates instability.

Source

Grziwotz, F., Chang, C.-W., Dakos, V., van Nes, E.H., Schwarzländer, M., Kamps, O., et al. (2023). Anticipating the occurrence and type of critical transitions. Science Advances, 9.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Subset the second community prior to the transition

pre_simTransComms <- subset(simTransComms$community2,time < inflection_pt)

#Estimate the univariate stability index for the first species in
#the second community

egJI <- uniJI(data = pre_simTransComms[1:25,2:3],
winsize = 75, E = 3)