tinyVASTcontrol: Control parameters for tinyVAST
In tinyVAST: Multivariate Spatio-Temporal Models using Structural Equations

tinyVASTcontrol

R Documentation

Control parameters for tinyVAST

Description

Control parameters for tinyVAST

Usage

tinyVASTcontrol(
  nlminb_loops = 1,
  newton_loops = 0,
  eval.max = 1000,
  iter.max = 1000,
  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,
  gmrf_parameterization = c("separable", "projection"),
  reml = FALSE,
  getJointPrecision = FALSE,
  calculate_deviance_explained = TRUE,
  run_model = TRUE,
  suppress_nlminb_warnings = TRUE
)

Arguments

`nlminb_loops`	Integer number of times to call `stats::nlminb()`.
`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`	Parameters to profile out of the likelihood (this subset will be appended to `random` with Laplace approximation disabled).
`tmb_par`	list of parameters for starting values, with shape identical 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)
`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_nlminb_warnings`	whether to suppress uniformative warnings from `nlminb` arising when a function evaluation is NA, which are then replaced with Inf and avoided during estimation