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Multivariate Jacobian Index Estimated From a Multivariate Autocorrelation Matrix

Source: R/multiAR.R
multiAR.Rd

Estimate the dominant Jacobian eigenvalue of a multivariate time series using autocorrelated stochastic differential equations

Usage

multiAR(data, scale = TRUE, winsize = 50, p = 1, dt = 1)

Source

Williamson and Lenton (2015). Detection of bifurcations in noisy coupled systems from multiple time series. Chaos, 25, 036407

Arguments

data

Numeric matrix with time in first column and species abundance in the remainder.

scale

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

winsize

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

p

Numeric. Defines the model order. Defaults to `1`.

dt

Numeric An appropriate time step

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.

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

egarJ <- multiAR(data = pre_simTransComms[,2:7],
winsize = 25, dt = 1)