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Assimilative causal inference

Source: R/aci-core.R
aci.Rd

Runs the filter and smoother for a model and observed record and scores each time by the relative entropy of the smoother from the filter. A normal aci() call uses the supplied-code backward-ODE headline smoother, including its correlated-noise correction, independently of keep. lag_table() and aci(table = ...) instead use the complete online Theorem 3 smoother; their finite-grid diagonal can therefore differ from headline ACI.

Usage

aci(
  model,
  obs,
  engine = c("auto", "cgns"),
  conditional = NULL,
  table = NULL,
  keep = c("paths", "table", "none"),
  decompose = TRUE,
  init = NULL,
  stepper = c("explicit", "implicit"),
  nsub = 1L,
  regularize = NULL,
  loglik = TRUE,
  ...
)

Arguments

model

A cgns_model object.

obs

An observed trajectory, or anything as_obs() accepts.

engine

One of "auto" or "cgns"; "auto" selects the closed-form engine for a conditional-Gaussian model.

conditional

Optional aci_conditional_spec; see aci_conditional().

table

Optional precomputed lag_table, whose online diagonal is used in place of the headline smoother.

keep

One of "paths", "table" or "none", selecting which objects are retained on the result. It does not select a smoother.

decompose

TRUE to retain the signal and dispersion parts.

init

Optional list with the initial hidden mean and cov.

stepper

Either "explicit" or "implicit".

nsub

Positive whole number of sub-steps taken per observation.

regularize

Covariance policy for this call; see aci_filter(). One record covers the filter, the smoother and any table this call builds, and is returned in meta$regularization.

loglik

TRUE (the default) accumulates the predictive log-likelihood on the internal filter, where keep = "paths" exposes it as paths$filter$meta$loglik. ACI itself never uses it, so FALSE skips that work and leaves paths$filter$meta$loglik NULL; every ACI quantity is unchanged.

...

Must be empty; unused arguments are an error.

Value

An object of class aci_result.

See also

aci_range(), lag_table(), aci_metric()

Examples

m <- aci_dyad_model()
sim <- simulate(m, seed = 1, t_end = 2, dt = 0.01)
ob <- as_obs(sim)
a <- aci(m, ob)
#> Warning: No init$cov supplied; using a diffuse prior. Discard an initial burn-in window when interpreting results.
a
#> <aci_result> engine = cgns | peak ACI = 2.523 at t = 0.03