tinyVASTcontrol: Control parameters for tinyVAST

In tinyVAST: Multivariate Spatio-Temporal Models using Structural Equations

Control parameters for tinyVAST

Description

Control parameters for tinyVAST

Usage

tinyVASTcontrol(
  opt_loops = 1,
  newton_loops = 0,
  eval.max = 10000,
  iter.max = 10000,
  getsd = TRUE,
  silent = getOption("tinyVAST.silent", TRUE),
  trace = getOption("tinyVAST.trace", 0),
  verbose = getOption("tinyVAST.verbose", FALSE),
  profile = c(),
  tmb_par = NULL,
  tmb_map = NULL,
  tmb_random = NULL,
  gmrf_parameterization = c("separable", "projection"),
  reml = FALSE,
  getJointPrecision = FALSE,
  calculate_deviance_explained = TRUE,
  run_model = TRUE,
  suppress_user_warnings = FALSE,
  get_rsr = FALSE,
  extra_reporting = FALSE,
  use_anisotropy = FALSE,
  sar_adjacency = "queen",
  barrier_stiffness = 0.01
)

Arguments

opt_loops	Integer number of times to call nonlinear optimizer.
newton_loops	Integer number of Newton steps to do after running stats::nlminb().
eval.max	Maximum number of evaluations of the objective function allowed. Passed to control in stats::nlminb().
iter.max	Maximum number of iterations allowed. Passed to control in stats::nlminb().
getsd	Boolean indicating whether to call TMB::sdreport()
silent	Disable terminal output for inner optimizer?
trace	Parameter values are printed every trace iteration for the outer optimizer. Passed to control in stats::nlminb().
verbose	Output additional messages about model steps during fitting?
profile	Character-vector passed to TMB::MakeADFun and see description there. Fixed effects that are highly correlated with random effects can often be estimated faster (i.e., with fewer iterations) by adding them to profile. The most common use-case is profile = c("alpha_j","alpha2_j"). However, doing so will have a small impact on model estimates and predictions.
tmb_par	named list of parameters used as starting values. Elements that have names that match those constructed internally then replace the internally constructed starting values. Those that match must have identical shape to tinyVAST(...)$internal$parlist
tmb_map	input passed to TMB::MakeADFun as argument map, over-writing the version tinyVAST(...)$tmb_inputs$tmb_map and allowing detailed control over estimated parameters (advanced feature)
tmb_random	input passed to TMB::MakeADFun as argument random, over-writing the version tinyVAST(...)$tmb_inputs$tmb_random and allowing detailed control over parameters treated as random effects. tmb_random = NULL uses the default (internal) construction, and use tmb_random = c("empty") (or some other name that doesn't match actual parameters) to use penalized likelihood (presumably while also modifying tmb_map and tmb_par)
gmrf_parameterization	Parameterization to use for the Gaussian Markov random field, where the default separable constructs a full-rank and separable precision matrix, and the alternative projection constructs a full-rank and IID precision for variables over time, and then projects this using the inverse-cholesky of the precision, where this projection allows for rank-deficient covariance.
reml	Logical: use REML (restricted maximum likelihood) estimation rather than maximum likelihood? Internally, this adds the fixed effects to the list of random effects to integrate over.
getJointPrecision	whether to get the joint precision matrix. Passed to sdreport.
calculate_deviance_explained	whether to calculate proportion of deviance explained. See deviance_explained()
run_model	whether to run the model of export TMB objects prior to compilation (useful for debugging)
suppress_user_warnings	whether to suppress warnings from package author regarding dangerous or non-standard options
get_rsr	Experimental option, whether to report restricted spatial regression (RSR) adjusted estimator for covariate responses
extra_reporting	Whether to report a much larger set of quantities via obj$env$report()
use_anisotropy	Whether to estimate two parameters representing geometric anisotropy
sar_adjacency	Whether to use queen or rook adjacency when defining a Simultaneous Autoregressive spatial precision from a sfc_GEOMETRY (default is queen)
barrier_stiffness	The ratio of local stiffness (the scale of diffusion rate and resulting decorrelation distance) for barriers relative to normal areas in the SPDE method when using add_mesh_covariates. The default barrier_stiffness = 0.01 is the value from Bakka et al. 2019.

Value

An object (list) of class tinyVASTcontrol, containing either default or updated values supplied by the user for model settings

References

Bakka, H., Vanhatalo, J., Illian, J., Simpson, D., Rue, H. (2019). Non-stationary Gaussian models with physical barriers. Spatial Statistics, 29, 268-288. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.spasta.2019.01.002")}

tinyVAST documentation built on May 19, 2026, 9:07 a.m.