Multivariate S-map Inferred Jacobian — multi_smap

Performs the S-map on a multivariate time series to infer the Jacobian matrix at different points in time across thetas.

Usage

multi_smap_jacobian(data, theta_seq = NULL, scale = TRUE)

Source

Medeiros, L.P., Allesina, S., Dakos, V., Sugihara, G. & Saavedra, S. (2022) Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics. Ecology Letters, 00, 1– 14.

Arguments

data: Numeric matrix with time in first column and species abundances in other columns
theta_seq: Numeric vector of thetas (nonlinear tuning parameters) to estimate the Jacobian over. If `NULL`, a default sequence is provided.
scale: Boolean. Should data be scaled prior to estimating the Jacobian.

Value

A list containing three objects:

smap_J: Jacobian matrices for each point in time. It is recommended to just use the last estimate.
rho: Pearson correlation between observed and predicted for each species.
smap_intercept.r: Intercepts of the regression fit.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data("simTransComms")

#Subset the third community prior to the transition

pre_simTransComms <- subset(simTransComms$community3,time < inflection_pt)


#Estimate the Jacobian using s-map (typically only
#the final estimate is informative)
est_jac <- multi_smap_jacobian(pre_simTransComms[1:10,2:7])