ecostate_control: Detailed control for ecostate structure
In ecostate: State-Space Mass-Balance Model for Marine Ecosystems

ecostate_control

R Documentation

Detailed control for ecostate structure

Description

Define a list of control parameters.

Usage

ecostate_control(
  nlminb_loops = 1,
  newton_loops = 0,
  eval.max = 1000,
  iter.max = 1000,
  getsd = TRUE,
  silent = getOption("ecostate.silent", TRUE),
  trace = getOption("ecostate.trace", 0),
  verbose = getOption("ecostate.verbose", FALSE),
  profile = c("logF_ti", "log_winf_z", "s50_z", "srate_z"),
  random = c("epsilon_ti", "alpha_ti", "nu_ti", "phi_tg2"),
  tmb_par = NULL,
  map = NULL,
  getJointPrecision = FALSE,
  integration_method = c("ABM", "RK4", "ode23", "rk4", "lsoda"),
  process_error = c("epsilon", "alpha"),
  n_steps = 10,
  F_type = c("integrated", "averaged"),
  derived_quantities = c("h_g2", "B_ti", "B0_i"),
  scale_solver = c("joint", "simple"),
  inverse_method = c("Standard", "Penrose_moore"),
  tmbad.sparse_hessian_compress = 1,
  start_tau = 0.001
)

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 that are profiled across, passed to `MakeADFun`
`random`	parameters that are treated as random effects, passed to `MakeADFun`
`tmb_par`	list of parameters for starting values, with shape identical to `tinyVAST(...)$internal$parlist`
`map`	list of mapping values, passed to RTMB::MakeADFun
`getJointPrecision`	whether to get the joint precision matrix. Passed to `sdreport`.
`integration_method`	What numerical integration method to use. `"ABM"` uses a native-R versions of Adam-Bashford, `"RK4"` uses a native-R version of Runge-Kutta-4, and `"ode23"` uses a native-R version of adaptive Runge-Kutta-23, where all are adapted from `pracma` functions. `"rk4"` and `lsoda` use those methods from `deSolve::ode` as implemented by `RTMBode::ode`
`process_error`	Whether to include process error as a continuous rate (i.e., an "innovation" parameterization, `process_error="epsilon"`) or as a discrete difference between expected and predicted biomass (i.e., a "state-space" parameterization), `process_error="alpha"`The former is more interpretable, whereas the latter is much more computationally efficient.
`n_steps`	number of steps used in the ODE solver for biomass dynamics
`F_type`	whether to integrate catches along with biomass (`"integrated"`) or calculate catches from the Baranov catch equation applied to average biomass (`"averaged"`)
`derived_quantities`	character-vector listing objects to ADREPORT
`scale_solver`	Whether to solve for ecotrophic efficiency EE given biomass B (`scale_solver="simple"`) or solve for a combination of EE and B values
`inverse_method`	whether to use pseudoinverse or standard inverse
`tmbad.sparse_hessian_compress`	passed to `TMB::config()`, and enabling an experimental feature to save memory when first computing the inner Hessian matrix. Using `tmbad.sparse_hessian_compress=1` seems to have no effect on the MLE (although users should probably confirm this), and hugely reduces memory use in both small and large models. Using `tmbad.sparse_hessian_compress=1` seems to hugely speed up the model-fitting with a large model but results in a small decrease in speed for model-fitting with a small model.
`start_tau`	Starting value for the standard deviation of process errors