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Plot sdmTMB model comparison results

Source: R/compare-models.R
plot.sdmTMB_model_comparison.Rd

Plots the results of compare_models() as either a time series of survey indices (type = "index") or spatial maps of prediction differences (type = "spatial", rendered on a ggOceanMaps::basemap()).

Usage

# S3 method for class 'sdmTMB_model_comparison'
plot(
  x,
  type = "index",
  index_divisor = NULL,
  unit = NA,
  mohn = FALSE,
  viridis = FALSE,
  spatial_response = "mean_pr_diff",
  fill_prefix = NULL,
  transform_color_scale = TRUE,
  limit_rounding = 10,
  break_rounding = 5,
  base_size = 11,
  ...
)

Arguments

x

An object of class sdmTMB_model_comparison.

type

Character. "index" (default) or "spatial".

index_divisor

Optional numeric divisor applied to index values.

unit

Character. Unit label for axes. If NA, "Index value" is used.

mohn

Logical. Annotate the index plot with Mohn's rho (via mohns_rho())? Default FALSE. Only applies when type = "index".

viridis

Logical. Use the viridis palette for model groups? Default FALSE (a fixed colour vector).

spatial_response

Character. Column mapped in spatial plots: one of "mean", "pr_mean" or "mean_pr_diff". Default "mean_pr_diff". See Details.

fill_prefix

Character. Prefix for the spatial-plot fill legend title (type = "spatial"), e.g. "Biomass density" or "CPUE". If NULL (default), the legend title is "Percent anomaly"; otherwise paste(fill_prefix, "percent anomaly", sep = "\n"). Models don't necessarily share a response variable, so this isn't inferred automatically.

transform_color_scale

Logical. Apply a signed square-root colour scale for spatial plots? Default TRUE.

limit_rounding, break_rounding

Numeric rounding for map fill limits and breaks. Defaults 10 and 5.

base_size

Numeric. Base font size. Default 11.

...

Currently ignored.

Value

A ggplot object (type = "index") or a cowplot grid (type = "spatial").

Details

For type = "spatial", each grid cell is compared between a fitted model and the reference model for every year they share, on the response scale (i.e. predict(..., type = "response"), e.g. density rather than log density): diff = est - ref_est and pr_diff = 100 * diff / ref_est (percent). spatial_response selects how these per-year values are summarised across years into the single value mapped for each cell:

  • "mean": mean(diff) — the average absolute difference, in response units. Useful for the raw magnitude of disagreement, but not comparable across cells with very different baseline abundance.

  • "pr_mean": mean(pr_diff) — the average of the yearly percent differences. Because each year is divided by that year's own (possibly small) reference value, a handful of low-abundance years/cells can dominate this average with large percentage swings even when the absolute difference is small.

  • "mean_pr_diff" (default): 100 * mean(diff) / mean(ref_est) — the percent difference between the across-year mean predictions (a ratio of means, not a mean of ratios). This is usually the more robust percent-based summary, since a cell's overall multi-year baseline is used in the denominator instead of each individual year's value.

See also

compare_models()

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, mohn = TRUE)

plot(x, type = "spatial", fill_prefix = "Biomass density")

# }