Run the full taupatch pipeline — run

Loads a config and runs every stage in order: read zooplankton stations, fetch and attach environmental covariates, label high-abundance patches against the species threshold, fit the model, and project monthly habitat suitability maps.

Usage

run_taupatch(config_path, project = TRUE, keep_covariates = 50000)

Arguments

config_path: path to a config YAML file, or an already-loaded config list
project: whether to produce monthly projections after fitting
keep_covariates: the most covariate grid cells to return for mapping; 0 returns none. See thin_covariates() for what is kept and why.

Value

a list with config (as the run actually used it, so a jackknife that dropped a covariate shows in covariates.exclude), data (the labeled modeling data), model (a fit_patch_model() result, or a fit_patch_ensemble() one), projections (or NULL if skipped), jackknife (or NULL if not run), covariate_means, and covariates (a thinned grid, for mapping)

Details

Two optional stages sit between labelling and fitting, both off by default and both turned on from the config:

covariates.jackknife tests each covariate by leaving it out — see jackknife_settings(). It runs before the fit so its answer can change which covariates the model gets, and it only removes any if jackknife.drop says so.
model.ensemble fits several algorithms instead of one and combines them — see ensemble_settings(). Everything after the fit works the same either way, so a config that turns this on gets ensemble projections without changing anything else.

Examples

if (FALSE) { # \dontrun{
result <- run_taupatch(system.file("configs/mock_test.yaml", package = "taupatch"))
result$model$metrics
result$projections
} # }