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Data assimilation filter

Source: R/aci-assimilation.R
aci_filter.Rd

Generic reconstructing the hidden state from the observed record up to each time. The closed-form method is used for a cgns_model; a general stochastic_model is out of scope in this release.

Usage

aci_filter(model, obs, ...)

# S3 method for class 'cgns_model'
aci_filter(
  model,
  obs,
  init = NULL,
  conditional = NULL,
  stepper = c("explicit", "implicit"),
  nsub = 1L,
  regularize = NULL,
  loglik = TRUE,
  ...
)

# S3 method for class 'stochastic_model'
aci_filter(model, obs, ...)

Arguments

model

A cgns_model or stochastic_model object.

obs

An observed trajectory, or anything as_obs() accepts.

...

Arguments passed to methods.

init

Optional list with the initial hidden mean and cov; NULL uses a diffuse prior and warns.

conditional

Optional aci_conditional_spec selecting a conditional ACI reduction; see aci_conditional().

stepper

Either "explicit" or "implicit"; the implicit Riccati step preserves positivity.

nsub

Positive whole number of sub-steps taken per observation.

regularize

Covariance policy for this call. "none" (the default, and the value of getOption("aci.regularize") when it is unset) stops with a classed aci_error_covariance_not_spd naming the site, grid index and time as soon as a covariance leaves the positive-definite cone. "floor" is the previous behaviour: the covariance is projected back by spd_floor() and every such event is recorded in the result's meta$regularization.

loglik

TRUE (the default) accumulates the predictive log-likelihood into meta$loglik. FALSE skips that work; the filter moments are unchanged and meta$loglik is NULL. The likelihood is not used by ACI, so FALSE is the cheaper choice when only the state estimate is wanted.

Value

An assimilation path: da_path_gaussian for the closed-form engine. Its meta$loglik holds the predictive log-likelihood of the observed record, or NULL when the method was called with loglik = FALSE.

Methods (by class)

  • aci_filter(cgns_model): Closed-form filter for a conditional-Gaussian model.

  • aci_filter(stochastic_model): Classed not-implemented condition for a general (non-CGNS) stochastic model.

See also

aci_smoother(), aci(), lag_table()

Examples

m <- aci_dyad_model()
sim <- simulate(m, seed = 1, t_end = 2, dt = 0.01)
ob <- as_obs(sim)
f <- aci_filter(m, ob)
#> Warning: No init$cov supplied; using a diffuse prior. Discard an initial burn-in window when interpreting results.
f
#> <da_path_gaussian> kind = filter, l = 1, N+1 = 201