tinyVASTcontrol function

Control parameters for tinyVAST

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

Returns

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