Points of the map to explain: a regular grid of the model's raster cells.

The cells of the rasters the model was fitted on, every every rows and columns, inside extent, where the first channel has a value. With every = 1 every cell – the map at full resolution, for a tile; with 40 at 250 m, one point every 10 km, for a region. The points carry their grid, so importance_map() can lay the values back on it.

Usage

importance_points(final, data, extent = NULL, every = 1L, rasters = NULL)

Arguments

final

A dsm_final, or the directory of a final run.

data

The dsm_data it was fitted on.

extent

c(xmin, xmax, ymin, ymax) in the rasters' coordinates; NULL for the whole raster.

every

Take every every-th cell, in rows and in columns.

rasters

Where the rasters are, as in dsm_predict(); NULL for the store's own raster table.

Value

A tibble of x, y (cell centres), with the grid as an attribute.

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)   # every 4th cell
nrow(pts)
# }
}