Simulate

Simulate

Usage

simulateForest(
  model,
  env,
  disturbance = NULL,
  patches = 100L,
  patch_size = 0.1,
  init_cohort = NULL,
  device = c("cpu", "gpu"),
  return_cohorts = FALSE,
  debug = FALSE,
  ...
)

Arguments

model

(finn_class)
Object of class finn_class created by finn.

env

(data.table|data.frame)
Data with environmental covariates must be passed as data.table or data.frame.

disturbance

(data.table|data.frame)
Data with disturbance rates must be passed as data.table or data.frame.

patches

(integer(1))
Number of patches.

patch_size

(numeric(1))
Patch size.

init_cohort

(CohortMat)
Initial cohort matrix of class CohortMat, created by CohortMat.

device

(character(1))
Should the simulation run on the CPU or the GPU (Graphics card). Support is only available for NVIDIA GPUs.

return_cohorts

Controls whether the raw per-cohort state is returned in addition to the aggregated site output. Storing cohorts every timestep is expensive, so the default is FALSE (none). Use TRUE (or "all") for every timestep, "last" for the final timestep only, or an integer vector of timesteps to store just those. Recorded cohorts appear as $long$cohort / $wide$cohort.

debug

(logical(1))
Debug modus or not. If TRUE, individual tree states are stored.

...

Advanced options forwarded to the internal simulator, chiefly batchsize (split the sites into batches of this many to cap memory for very large runs). The default processes all sites in one batch and is what almost all users want.

Value

A named list of simulation results. The patch-averaged, stand-level state variables and demographic rates are in $long$site / $wide$site (long format: siteID, year, species, variable, value). When return_cohorts is set, $long$cohort / $wide$cohort hold the raw per-cohort state for the requested timesteps.

Details

Simulate from a fitted FINN model. This is a thin, backwards-compatible alias for predict.finn_class; new code should call predict(model, ...) directly.