Kernel smoothing on a grid — grid

Bin the points to a grid, then smooth that grid with a Gaussian kernel, using fields::image.smooth().

Usage

grid_smooth(x, value = NULL, grid = NULL, theta = NULL, ...)

Arguments

x: coordinates, or coordinates carrying their value as z; see xyz_input()
value: one value per coordinate, or NULL to use the z of x
grid: a grid_spec() to interpolate onto, or NULL for a default one
theta: kernel bandwidth, in the units of the coordinates
...: passed to fields::image.smooth()

Value

A guerrilla_grid with values.

Details

This is the two step method: rasterize first (grid_bin()), estimate second. Everything else here works from the points directly, so this one is worth having as the contrast – the binning throws away where inside its cell each point was, and no amount of smoothing afterwards gets that back.

theta is the kernel bandwidth in coordinate units, and it does all the work. There is no standard error, because there is no model.

fields::image.smooth() wants a square grid, so this uses one of max(dimension) cells a side and returns it; the result covers the same extent but need not have the dimension you asked for.

Examples

xy <- cbind(runif(200), runif(200))
if (requireNamespace("fields", quietly = TRUE)) {
  plot(grid_smooth(xy, xy[, 1] + xy[, 2], theta = 0.1))
}