Inverse distance weighted interpolation — grid

Estimate each cell as a weighted average of the input values, weighted by one over distance to the power idp.

Usage

grid_idw(x, value = NULL, grid = NULL, idp = 2, nmax = Inf, ...)

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
idp: the inverse distance power
nmax: use at most this many of the nearest points per cell
...: passed to gstat::idw()

Value

A guerrilla_grid with values.

Details

There is no model here, only a rule, and that is the point of having it next to the others. Raising idp makes the nearest point dominate, so the surface tends to nearest neighbour (grid_voronoi()); lowering it flattens towards the overall mean. Neither end is more correct than the other, and nothing in the data tells you where to sit between them.

The consequence is that this method cannot report a standard error, because it never claimed to be estimating anything. grid_kriging() is the same idea taken seriously: weights chosen from a fitted model of how the values vary with distance, which then also says how uncertain each cell is.

Examples

xy <- cbind(runif(50), runif(50))
if (requireNamespace("gstat", quietly = TRUE)) {
  op <- par(mfrow = c(1, 2))
  plot(grid_idw(xy, xy[, 1], idp = 0.5), main = "idp = 0.5")
  plot(grid_idw(xy, xy[, 1], idp = 8), main = "idp = 8")
  par(op)
}