Predict from a FINN model

Usage

# S3 method for class 'finn_class'
predict(
  object,
  env,
  disturbance = NULL,
  patches = 100L,
  patch_size = 0.1,
  init_cohort = NULL,
  device = c("cpu", "gpu"),
  return_cohorts = FALSE,
  debug = FALSE,
  ...
)

Arguments

object: (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 predictions. $long$site (and $wide$site) give the site-level results (columns siteID, year, species, variable, value). When return_cohorts is set, $long$cohort / $wide$cohort add the per-cohort state (dbh, trees, species, growth g, mortality m, ...) for the requested timesteps.

Details

Simulate from a (fitted) FINN model. This is an S3 method for the stats::predict generic, so it is dispatched as predict(model, ...).