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,
  gmrf_parameterization = c("separable", "projection"),
  reml = FALSE,
  getJointPrecision = FALSE,
  calculate_deviance_explained = 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
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()