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Convert sdmTMB_cv() objects to sf format for spatial assessment with waywiser

Source: R/cross-val.R
cv_to_waywiser.Rd

[Experimental] Converts cross-validation results to an sf::sf() object for use with spatial model assessment tools such as those in the waywiser package. This enables multi-scale spatial assessment of model predictions.

Usage

cv_to_waywiser(
  object,
  ll_names = c("longitude", "latitude"),
  ll_crs = 4326,
  utm_crs = get_crs(object$data, ll_names)
)

Arguments

object

An object of class sdmTMB_cv from sdmTMB_cv().

ll_names

Column names for longitude and latitude in the original data. Note the order: longitude first, then latitude.

ll_crs

The coordinate reference system (CRS) for the ll_names columns. Defaults to 4326 (WGS84 lon/lat).

utm_crs

The projected coordinate reference system (CRS) for the output sf object. By default (if you're feeling lucky!) automatically detected using get_crs() based on ll_names. Can be manually specified as an EPSG code (e.g., 32609) or any format accepted by sf::st_crs().

Value

An sf::sf() object with POINT geometry containing:

truth

The observed response values

estimate

The cross-validated predictions

geometry

Spatial point locations

Details

This function is particularly useful for assessing spatial models at multiple scales using the waywiser package. After converting to sf format, you can use functions like waywiser::ww_multi_scale() to evaluate how model performance changes when predictions are aggregated to different spatial scales.

For delta/hurdle models, the combined predictions are returned (e.g., the product of the encounter probability and positive catch rate).

See also

sdmTMB_cv(), get_crs(), https://pbs-assess.github.io/sdmTMB/articles/cross-validation.html

Examples

mesh <- make_mesh(pcod_2011, c("X", "Y"), cutoff = 12)

# Run cross-validation
set.seed(123)
m_cv <- sdmTMB_cv(
  density ~ depth_scaled,
  data = pcod_2011,
  mesh = mesh,
  family = tweedie(),
  k_folds = 2
)
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.

# Convert with default auto-detected CRS based on lon/lat columns:
cv_sf <- cv_to_waywiser(m_cv, ll_names = c("lon", "lat"))
#> Detected UTM zone 9N; CRS = 32609.
#> Visit https://epsg.io/32609 to verify.

# Or manually specify the desired UTM CRS:
cv_sf <- cv_to_waywiser(m_cv, ll_names = c("lon", "lat"), utm_crs = 32609)

# Use with waywiser for multi-scale assessment
waywiser::ww_multi_scale(
  cv_sf,
  truth,    # column name (unquoted)
  estimate, # column name (unquoted)
  n = list(c(5, 5), c(2, 2)) # 5x5 and 2x2 grid blocks
)
#> # A tibble: 4 × 6
#>   .metric .estimator .estimate .grid_args       .grid         .notes          
#>   <chr>   <chr>          <dbl> <list>           <list>        <list>          
#> 1 rmse    standard       12.8  <tibble [1 × 1]> <sf [25 × 5]> <tibble [0 × 2]>
#> 2 mae     standard        9.23 <tibble [1 × 1]> <sf [25 × 5]> <tibble [0 × 2]>
#> 3 rmse    standard        7.79 <tibble [1 × 1]> <sf [4 × 5]>  <tibble [0 × 2]>
#> 4 mae     standard        6.42 <tibble [1 × 1]> <sf [4 × 5]>  <tibble [0 × 2]>