Plot a GWR model selection

gwr_model_selection() ranks every model it evaluated on AICc. This draws each model's criterion against its number of predictors, the winner in red, so the gap between the best model and the runners-up (which the ranked table shows only as numbers) is read as a shape: a winner well below the rest was chosen by the data, a winner a fraction of an AICc unit ahead of three others was chosen by the tie-break. The criterion is in-sample and the caption carries the label gwr_model_selection() attached to it, including any note saying it was read positionally.

Usage

# S3 method for class 'gwr_model_selection'
plot(x, ...)

Arguments

x

A gwr_model_selection object.

...

Ignored.

Value

A ggplot object.

See also

Other plotting: plot.aoa(), plot.block_size_sweep(), plot.feature_selection(), plot.resolution_profile(), plot.sac_range(), plot.spatial_fit(), plot_calibration(), plot_cv_metrics(), plot_folds()

Examples

if (requireNamespace("GWmodel", quietly = TRUE) &&
    requireNamespace("sp", quietly = TRUE) &&
    requireNamespace("ggplot2", quietly = TRUE)) {
  library(sf)
  set.seed(1)
  n <- 80
  dat <- st_as_sf(
    data.frame(x = 5e5 + runif(n, 0, 1000), y = 5e6 + runif(n, 0, 1000),
               a = rnorm(n), b = rnorm(n), noise = rnorm(n)),
    coords = c("x", "y"), crs = 32632)
  dat$z <- 2 * dat$a - dat$b + rnorm(n, 0, 0.5)
  sel <- gwr_model_selection(dat, "z", c("a", "b", "noise"), bandwidth = 30)
  # Every model tried, AICc against its size; the winner (a and b) marked.
  plot(sel)
}