Apply uncertainty quantification to an existing fit

Usage

wsmed_infer(
  object,
  method = c("mc", "bootstrap"),
  draws = NULL,
  seed = NULL,
  level = 0.95,
  interval = "perc",
  decomposition = "eigen",
  pd = TRUE,
  tol = 1e-06
)

Arguments

object: A wsmed_fit, or a new wsMed one-call result.
method: mc or bootstrap. MI currently supports mc only.
draws: Number of draws; defaults to 20000 for mc and 2000 for bootstrap.
seed: Random seed, independent of the imputation seed.
level: Confidence level.
interval: Bootstrap interval type: perc, bc, or bca.simple. This controls the legacy parameter table stored in the inference backend. Effect extraction and confint methods always use percentile intervals. bca.simple uses zero acceleration, rather than a full BCa calculation.
decomposition: MI covariance factorization: eigen, chol, or svd.
pd, tol: MI covariance checks; passed to the existing sampler.

Value

A wsmed_inference storing draws and the original fit.

Details

Inference never reimputes or changes the supplied fit. Bootstrap necessarily fits resampled datasets. No new statistical engine is introduced. Seeded calls restore the caller's random state; seed = NULL consumes the current random stream and advances it normally. Fits with endogenous products must record compatible model and MI pooling algorithm identifiers. Older unversioned moderated fits require refitting from the original data; package version alone cannot identify their algorithms.