Fit SDR from verified, previously estimated components — margot_lmtp_sdr_from

Use with margot_lmtp() through its lmtp_model_type argument. The caller supplies node density ratios, the realised fold map and the RNG procedure. No exposure or observation model is fitted and no additional cap is applied. The caller remains responsible for the provenance and scientific validity of the components. Input identities are checked before outcome fitting.

Usage

margot_lmtp_sdr_from_components(
  data,
  trt,
  outcome,
  shift,
  components,
  baseline,
  time_vary,
  learners_outcome,
  bounds,
  control = lmtp::lmtp_control(.trim = 1),
  progress_bar = function(...) invisible(NULL),
  learners_trt = NULL
)

Arguments

data: Analysis frame, including one continuous outcome and id.
trt: Ordered exposure column names.
outcome: Outcome column name.
shift: Registered shift function.
components: List containing frame (all non-outcome analysis columns), history, baseline, time_vary, trt, cens, density_ratios, folds, weights, seed, rng_kind, shift_source_sha256 and shifted_exposures.
baseline: Baseline covariate names, identical to components$baseline.
time_vary: List of covariates entering each node, identical to components$time_vary, including node order and within-node variable order.
learners_outcome: Outcome learner library.
bounds: Explicit outcome limits on the analysis scale.
control: An lmtp_control object with .trim = 1.
progress_bar: Optional native SDR progress callback.
learners_trt: Ignored; exposure components are already supplied.

Value

An ordinary lmtp fit with supplied folds, histories, bounds and RNG provenance attached.

Details

The current implementation supports continuous outcomes and requires a unique, nonmissing id column. It uses the internal interface of lmtp 1.5.4; other versions are rejected by the shared interface guard. At least two outer folds must be supplied, and the active future plan must provide exactly one worker per fold. This function does not change the future plan. Exposure waves may be nonconsecutive, and each node may include covariates from multiple earlier surveys; the caller declares when each covariate enters.