Run an SDM using no cross validation and previously established tune arguments

Usage

run_full_sdm(
  prep,
  tune,
  out_dir,
  return_val = "path",
  use_metric = "combo",
  force_new = FALSE,
  do_gc = FALSE,
  ...
)

Arguments

prep: Character or named list. If character, the path to an existing prep.rds. Otherwise, the result of a call to prep_sdm with return_val = "object"
tune: Character or named list. If character, the path to an existing tune.rds. Otherwise, the result of a call to tune_sdm with return_val = "object"
out_dir: FALSE or character. If FALSE the result of run_full_sdm() will be saved to a temporary folder. If character, a file 'tune.rds' will be created at the path defined by out_dir.
return_val: Character: "object" or "path". Both return a named list. In the case of "path" the named list is simply list(full_run = out_dir). Will be set to "object" if out_dir is FALSE.
use_metric: Character. Which metric to use to find the 'best' tune arguments from previous tuning results? Default is combo, the product of auc_po, CBI_rescale and IMAE. use_metric must be combo or have been used in the use_metrics argument to tune_sdm().
force_new: Logical. If outputs already exist, should they be remade?
do_gc: Logical. Run base::rm(list = ls) and base::gc() at end of function? Useful when running SDMs for many, many taxa, especially if done in parallel.
...: Passed to tune_sdm()

Value

If return_val is "object" a named list. If return_val is "path" a path to the saved file. If out_dir is a valid path, the 'full result' (irrespective of return_val) is also saved to fs::path(out_dir, "prep.rds"). The 'full result' is a named list with elements:

Examples

inst/examples/predict_sdm_ex.R
#> Error: object 'inst' not found