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Compare sdmTMB models

Source: R/compare-models.R
compare_models.Rd

Compares a set of fitted sdmTMB models by calculating survey indices, per-grid-cell prediction differences relative to a reference model, and model sanity checks. The result can be plotted with plot.sdmTMB_model_comparison().

Usage

compare_models(
  ...,
  newdata,
  reference_model = 1,
  object_crs = NULL,
  bias_correct = FALSE,
  level = 0.95,
  area = 1,
  silent = TRUE,
  n_workers = 1,
  nsplit = 3,
  future_seed = TRUE
)

Arguments

...

A set of fitted sdmTMB::sdmTMB() models to compare, either as individual arguments or as a single (optionally named) list. List names are used as model names; otherwise default names are assigned.

newdata

A data frame for predictions, with the same predictor columns as the fitted data and a time column matching the fitted data.

reference_model

Integer. Index of the model to use as the reference for comparison. Default 1.

object_crs

Coordinate reference system for the model data. If NULL (default), extracted from the first model's mesh when available.

bias_correct

Logical. Apply bias correction via TMB::sdreport()? Default FALSE.

level

Numeric. Confidence level for index intervals. Default 0.95.

area

Grid cell area for area-weighted index calculation. Passed to sdmTMB::get_index_split().

silent

Logical. Suppress model output? Default TRUE.

n_workers

Integer. Number of parallel workers. Default 1.

nsplit

Integer. Number of splits for sdmTMB::get_index_split().

future_seed

Logical or integer. Future-compatible random seed for parallel processing. See future.apply::future_lapply().

Value

An object of class sdmTMB_model_comparison, a list with elements sanity, nll, index and grid (a list of per-cell prediction- difference data frames). Carries crs, reference_model and model_names attributes.

See also

sdmTMB::sdmTMB(), sdmTMB::get_index_split(), plot.sdmTMB_model_comparison()

Author

Mikko Vihtakari

Examples

# \donttest{
mesh <- sdmTMB::make_mesh(sdmTMB::pcod, c("X", "Y"), cutoff = 20)
m1 <- sdmTMB::sdmTMB(
  data = sdmTMB::pcod, formula = density ~ 0 + as.factor(year),
  time = "year", mesh = mesh, family = sdmTMB::tweedie(link = "log")
)
m2 <- sdmTMB::sdmTMB(
  data = sdmTMB::pcod, formula = density ~ 0 + as.factor(year) + depth_scaled,
  time = "year", mesh = mesh, family = sdmTMB::tweedie(link = "log")
)
nd <- sdmTMB::replicate_df(sdmTMB::qcs_grid, "year", unique(sdmTMB::pcod$year))
x <- compare_models(m1, m2, newdata = nd, object_crs = 32609)
#> ℹ `ln_tau_O` is an internal parameter affecting `sigma_O`
#> ℹ `sigma_O` is the spatial standard deviation
#> ℹ `ln_tau_E` is an internal parameter affecting `sigma_E`
#> ℹ `sigma_E` is the spatiotemporal standard deviation
#> ℹ `ln_kappa` is an internal parameter affecting `range`
#> ℹ `range` is the distance at which data are effectively independent
plot(x)

plot(x, type = "spatial")

# }