sdmTMB_stacking function

Perform stacking with log scores on sdmTMB_cv() output

This approach is described in Yao et al. (2018) tools:::Rd_expr_doi("10.1214/17-BA1091") . The general method minimizes (or maximizes) some quantity across models. For simple models with normal error, this may be the root mean squared error (RMSE), but other approaches include the log score. We adopt the latter here, where log scores are used to generate the stacking of predictive distributions

sdmTMB_stacking(model_list, include_folds = NULL)

Arguments

• model_list: A list of models fit with sdmTMB_cv() to generate estimates of predictive densities. You will want to set the seed to the same value before fitting each model or manually construct the fold IDs so that they are the same across models.
• include_folds: An optional numeric vector specifying which folds to include in the calculations. For example, if 5 folds are used for k-fold cross validation, and the first 4 are needed to generate these weights, include_folds = 1:4.

Returns

A vector of model weights.

Examples

# Set parallel processing if desired. See 'Details' in ?sdmTMB_cv

# Depth as quadratic:
set.seed(1)
m_cv_1 <- sdmTMB_cv(
  density ~ 0 + depth_scaled + depth_scaled2,
  data = pcod_2011, mesh = pcod_mesh_2011,
  family = tweedie(link = "log"), k_folds = 2
)
# Depth as linear:
set.seed(1)
m_cv_2 <- sdmTMB_cv(
  density ~ 0 + depth_scaled,
  data = pcod_2011, mesh = pcod_mesh_2011,
  family = tweedie(link = "log"), k_folds = 2
)

# Only an intercept:
set.seed(1)
m_cv_3 <- sdmTMB_cv(
  density ~ 1,
  data = pcod_2011, mesh = pcod_mesh_2011,
  family = tweedie(link = "log"), k_folds = 2
)

models <- list(m_cv_1, m_cv_2, m_cv_3)
weights <- sdmTMB_stacking(models)
weights

References

Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. 2018. Using Stacking to Average Bayesian Predictive Distributions (with Discussion). Bayesian Analysis 13(3): 917–1007. International Society for Bayesian Analysis. tools:::Rd_expr_doi("10.1214/17-BA1091")