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

sdmTMB_cv(formula, data, spde, time = NULL, k_folds = 10, fold_ids = NULL, ...)

Arguments

formula	Model formula.
data	A data frame.
spde	Output from `make_mesh()`.
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.
...	All other arguments required to run `sdmTMB()` model with the exception of `weights`, which are used to define the folds.

Examples

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

# library(future) # for parallel processing
# plan(multisession) # for parallel processing
m_cv <- sdmTMB_cv(
  density ~ 0 + depth_scaled + depth_scaled2,
  data = pcod, spde = spde,
  family = tweedie(link = "log"), k_folds = 2
)
m_cv$fold_loglik
#> [1] -3318.609 -3436.644
m_cv$sum_loglik
#> [1] -6755.254
head(m_cv$data)
#> # A tibble: 6 x 15
#>    year     X     Y depth density present   lat   lon depth_mean depth_sd
#>   <int> <dbl> <dbl> <dbl>   <dbl>   <dbl> <dbl> <dbl>      <dbl>    <dbl>
#> 1  2003  446. 5793.   201   113.        1  52.3 -130.       5.16    0.445
#> 2  2003  446. 5800.   212    41.7       1  52.3 -130.       5.16    0.445
#> 3  2003  449. 5802.   220     0         0  52.4 -130.       5.16    0.445
#> 4  2003  437. 5802.   197    15.7       1  52.4 -130.       5.16    0.445
#> 5  2003  421. 5771.   256     0         0  52.1 -130.       5.16    0.445
#> 6  2003  418. 5772.   293     0         0  52.1 -130.       5.16    0.445
#> # … with 5 more variables: depth_scaled <dbl>, depth_scaled2 <dbl>,
#> #   cv_fold <int>, cv_predicted <dbl>, cv_loglik <dbl>
m_cv$models[[1]]
#> Spatial model fit by ML ['sdmTMB']
#> Formula: formula
#> Time column: time
#> SPDE: spde
#> Data: data
#> Family: tweedie(link = 'log')
#>               coef.est coef.se
#> depth_scaled     -1.58    0.18
#> depth_scaled2    -1.48    0.11
#> 
#> Matern range parameter: 150.77
#> Dispersion parameter: 13.63
#> Spatial SD (sigma_O): 2.48
#> Spatiotemporal SD (sigma_E): not estimated
#> ML criterion at convergence: 3291.025
#> 
#> See ?tidy.sdmTMB to extract these values as a data frame.
m_cv$max_gradients
#> [1] 0.0004420159 0.0001048449