Maps of SHAP values, from an importance computed at points of the map.

Lays the values of dsm_importance()(at = ) back on a grid: each cell the mean of the points in it. With the points' own grid (from importance_points()) every point is its own cell; with a coarser resolution, each cell averages the points it holds.

Usage

importance_map(x, resolution = NULL, output_dir = NULL)

Arguments

x

A dsm_importance from shap_importance() at map points.

resolution

Cell size of the maps, in the rasters' units; NULL for the points' own grid.

output_dir

Where to write the GeoTIFFs (shap.tif, one layer per variable; shap_dominant.tif and its legend, shap_dominant.csv; prediction.tif); NULL to only return them.

Value

An importance_map, a list: shap (one layer per variable: the mean SHAP value of the points in each cell, in the network's units), dominant (in each cell, the variable with the largest mean |SHAP|, as the number in legend), legend, prediction (the mean prediction, native units) and files. The three rasters are packed with terra::wrap(), so the map survives base::saveRDS(); terra::unwrap() gives each as a SpatRaster. plot() draws it.

Examples

if (FALSE) { # torch::torch_is_installed()
# \donttest{
run <- example_run()    # a small fitted run, made once a session
pts <- importance_points(run$final, run$data, every = 4)
imp <- dsm_importance(run$final, run$data, shap_importance(samples = 10, background = 20),
                      at = pts, seeds = 42, verbose = FALSE)
m <- importance_map(imp)
plot(m)
# }
}