simulate_sfnetwork: Simulate GMRF for stream network

In tinyVAST: Multivariate Spatio-Temporal Models using Structural Equations

Simulate GMRF for stream network

Description

Simulate values from a GMRF using a tail-up exponential model on a stream network

Usage

simulate_sfnetwork(sfnetwork_mesh, theta, n = 1, what = c("samples", "Q"))

Arguments

sfnetwork_mesh	Output from sfnetwork_mesh
theta	Decorrelation rate
n	number of simulated GMRFs
what	Whether to return the simulated GMRF or its precision matrix

Value

a matrix of simulated values for a Gaussian Markov random field arising from a stream-network spatial domain, with row for each spatial random effect and n columns, using the sparse precision matrix defined in Charsley et al. (2023)

References

Charsley, A. R., Gruss, A., Thorson, J. T., Rudd, M. B., Crow, S. K., David, B., Williams, E. K., & Hoyle, S. D. (2023). Catchment-scale stream network spatio-temporal models, applied to the freshwater stages of a diadromous fish species, longfin eel (Anguilla dieffenbachii). Fisheries Research, 259, 106583. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fishres.2022.106583")}

tinyVAST documentation built on April 4, 2025, 2:43 a.m.