Update an Opal model configuration

Changes to the scientific configuration clear dependent fits, samples, and reports. Metadata and optimiser controls do not alter the scientific target. Defaults are resolved again when their upstream configuration changes.

Usage

opal_update(
  x,
  data,
  parameters,
  map,
  random,
  bounds,
  control,
  makeadfun_args,
  priors,
  metadata
)

Arguments

x

An opal_obj.

data, parameters, map, random, bounds, control, makeadfun_args

As in opal_obj().

priors

Optional replacement for data$priors.

metadata

Named metadata to merge.

Value

An updated opal_obj; the input object is not modified.

Details

Changes to data, priors, parameters, maps, random effects, bounds, or RTMB construction settings clear dependent fits, posteriors, diagnostics, and projections. Metadata is merged, and optimiser controls can change without invalidating results. A supplied map is retained unless explicitly replaced; check its dimensions when changing parameter structure.

See also

Other assessment workflow: opal_attach_fit(), opal_attach_mcmc(), opal_build(), opal_check(), opal_derived(), opal_example_inputs(), opal_fit(), opal_from_fit(), opal_io, opal_mcmc(), opal_obj(), opal_posterior(), opal_project(), opal_report()

Examples

inputs <- opaka_quickstart_inputs()
assessment <- opal_obj(inputs$data, inputs$parameters, inputs$map)
labelled <- opal_update(assessment, metadata = list(label = "Baseline"))
summary(labelled)
#> <opal_obj> configured
#> Active parameters: NA 
#> Fit check: not run  | MCMC check: not run