Fit a SPoRC RTMB model

Constructs an RTMB automatic differentiation function via RTMB::MakeADFun, optimises it with stats::nlminb, and optionally refines the solution with Newton steps using the analytic Hessian. The best parameter vector (obj$env$last.par.best), optimiser output, and model report are attached to the returned object.

Usage

fit_model(
  data,
  parameters,
  mapping,
  random = NULL,
  newton_loops = 3,
  silent = FALSE,
  do_optim = TRUE,
  nlminb_control = list(iter.max = 1e+05, eval.max = 1e+05, rel.tol = 1e-15),
  ...
)

Arguments

data

Named list of model data as constructed by the Setup_Mod_* family of functions.

parameters

Named list of parameter starting values.

mapping

Named list of factor maps controlling parameter sharing and fixing.

random

Character vector of parameter names to integrate out as random effects. NULL (default) fits a fixed-effects-only model.

newton_loops

Integer. Number of Newton refinement steps applied after nlminb convergence to reduce gradient magnitudes. Each step solves \(\Delta\theta = -H^{-1} g\) and updates the objective. Default 3. Errors and warnings are caught silently via tryCatch.

silent

Logical. If TRUE, suppresses RTMB and optimiser console output. Default FALSE.

do_optim

Logical. If TRUE (default), runs nlminb and Newton refinement. If FALSE, returns the un-optimised MakeADFun object only.

nlminb_control

Named list of control parameters passed to stats::nlminb. Default list(iter.max = 1e5, eval.max = 1e5, rel.tol = 1e-15).

...

Additional arguments forwarded to RTMB::MakeADFun.

Value

The RTMB ADFun object with three additional fields: $optim (the nlminb output list), and $rep (the model report evaluated at obj$env$last.par.best).

See also

Other Utility: get_logistN_Sigma(), get_par_est_info(), post_optim_sanity_checks(), rho_trans(), set_data_indicator_unused()

Examples

if (FALSE) { # \dontrun{
obj <- fit_model(data, parameters, mapping, random = NULL, newton_loops = 3)
obj$rep$SSB
} # }