Summarise a fitted FINN model — summary.finn

Prints, per process and per species, the environmental variable importance and the average conditional effects. Both are derived from the model's conditional effects, which are computed once and cached — so if ALE() was already run (or a previous summary()), the default path needs no further simulation.

Usage

# S3 method for class 'finn_class'
summary(
  object,
  env = NULL,
  init_cohort = NULL,
  importance = c("ale", "permutation"),
  env_autoscale = TRUE,
  sim_seed = 42L,
  nperm = 20L,
  scale = TRUE,
  ...
)

Arguments

object: (finn_class)
fitted model.
env, init_cohort: (NULL or data)
interpretation data; NULL uses the cached training data (see ALE()).
importance: (character(1))
"ale" (default) = analytical ALE-variance importance (cheap, from the cache); "permutation" = re-simulating permutation importance (see feature_importance()), cached on the model under the same input key.
env_autoscale: (logical(1))
see ALE().
sim_seed: (integer(1))
seed for the simulation (see ALE()).
nperm: (integer(1))
replicates for importance = "permutation".
scale: (logical(1))
for importance = "ale", divide Var(ALE) by the process x species rate variance so the importances are dimensionless and comparable across processes and species (Sobol-style). Default TRUE.
...: passed through (e.g. to predict() for the permutation option).

Value

invisibly, a list with importance, average_conditional_effects, and method.