Save log likelihoods of k-fold cross-validation for sdmTMB models

sdmTMB_cv(
  formula,
  data,
  mesh_args,
  spde,
  time = NULL,
  k_folds = 8,
  fold_ids = NULL,
  parallel = TRUE,
  use_initial_fit = FALSE,
  ...
)

Arguments

formula	Model formula.
data	A data frame.
mesh_args	Arguments for `make_mesh()`. If supplied, the mesh will be reconstruncted for each fold.
spde	Output from `make_mesh()`. If supplied, the mesh will be constant across folds.
time	The name of the time column. Leave as `NULL` if this is only spatial data.
k_folds	Number of folds.
fold_ids	Optional vector containing user fold IDs. Can also be a single string, e.g. `"fold_id"` representing the name of the variable in `data`.
parallel	If `TRUE` and a `future::plan()` is supplied, will be run in parallel.
use_initial_fit	Fit the first fold and use those parameter values as starting values for subsequent folds? Can be faster with many folds.
...	All other arguments required to run `sdmTMB()` model with the exception of `weights`, which are used to define the folds.

Value

A list:

data: Original data plus columns for fold ID, CV predicted value, and CV log likelihood.
models: A list of models; one per fold.
fold_loglik: Sum of left-out log likelihoods per fold.
fold_elpd: Expected log predictive density per fold on left-out data.
sum_loglik: Sum of fold_loglik across all left-out data.
elpd: Expected log predictive density across all left-out data.
pdHess: Logical vector: Hessian was invertible each fold?
converged: Logical: all pdHess TRUE?
max_gradients: Max gradient per fold.

Examples

if (inla_installed()) {
spde <- make_mesh(pcod, c("X", "Y"), cutoff = 25)

# Set parallel processing if desired:
# library(future)
# plan(multisession)
m_cv <- sdmTMB_cv(
  density ~ 0 + depth_scaled + depth_scaled2,
  data = pcod, spde = spde,
  family = tweedie(link = "log"), k_folds = 2
)

m_cv$fold_elpd
m_cv$elpd

m_cv$fold_loglik
m_cv$sum_loglik

head(m_cv$data)
m_cv$models[[1]]
m_cv$max_gradients

# \donttest{
# Create mesh each fold:
m_cv2 <- sdmTMB_cv(
  density ~ 0 + depth_scaled + depth_scaled2,
  data = pcod, mesh_args = list(xy_cols = c("X", "Y"), cutoff = 20),
  family = tweedie(link = "log"), k_folds = 2
)

# Use fold_ids:
m_cv3 <- sdmTMB_cv(
  density ~ 0 + depth_scaled + depth_scaled2,
  data = pcod, spde = spde,
  family = tweedie(link = "log"),
  fold_ids = rep(seq(1, 3), nrow(pcod))[seq(1, nrow(pcod))]
)
# }
}
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.