Influence diagnostics for sdmTMB models — influ.sdmTMB • influ2

Fixed effects use the marginal covariance from TMB. Spatial, spatiotemporal, IID, smooth, and time-varying contributions are projected from the fitted fields without retaining dense draw arrays. Field uncertainty uses sparse joint-precision simulation and is reduced directly to compact focus-level estimands. Delta fixed effects and latent fields are combined draw by draw to obtain unconditional-mean influence. Lognormal diagnostics require a log link for the arithmetic mean. Offsets have the same single-component restrictions as the GLM adapter.

Usage

# S3 method for class 'sdmTMB'
influ(
  model,
  focus,
  data = NULL,
  weights = NULL,
  reference_data = NULL,
  reference_weights = NULL,
  uncertainty = "auto",
  retain = "summary",
  probs = c(0.025, 0.975),
  ndraws = 1000L,
  seed = NULL,
  draws_path = NULL,
  keep_model = FALSE,
  ...
)

Arguments

model: A fitted `sdmTMB` object.
focus: Name of the focus variable, usually year.
data: Optional model data. The model frame is used by default.
weights: Optional numeric observation weights or the name of a weight column in `data`.
reference_data: Optional prediction grid defining the common standardisation distribution. By default the observed data are used.
reference_weights: Optional numeric weights, or a column name, for `reference_data`.
uncertainty: One of `"auto"`, `"none"`, `"analytic"`, or `"simulation"`.
retain: One of `"summary"`, `"derived_draws"`, or `"disk"`.
probs: Lower and upper interval probabilities.
ndraws: Number of joint coefficient draws for simulation uncertainty.
seed: Optional simulation seed.
draws_path: Output path used when `retain = "disk"`.
keep_model: Retain the fitted model inside the diagnostic object.
...: Reserved for future backend options.

Value

An [influ_diag] object.