Kriging

Fit a variogram to the data, then use it to weight the input values for every cell.

Usage

grid_kriging(
  x,
  value = NULL,
  grid = NULL,
  model = NULL,
  statistic = c("prediction", "se"),
  ...
)

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

model

a variogramModel from gstat::vgm() to fit, or NULL for a spherical starting guess

statistic

"prediction" for the kriged surface, "se" for the square root of the kriging variance

...

passed to gstat::krige()

Value

A guerrilla_grid with values.

Details

The variogram is a picture of how much two values differ, as a function of how far apart they are. Kriging fits a curve to that picture and uses it to choose the weights, so unlike grid_idw() the weighting is estimated from the data rather than chosen.

That also makes this the one method here that can refuse. If the values have no spatial structure at these distances there is no curve to fit, the fit comes back with a negative range, and this stops rather than kriging from nonsense. Every other method in the package will happily interpolate pure noise and hand you a picture of it.

Because there is a fitted model, there is also a variance: statistic = "se" returns the square root of the kriging variance, which is small near the data and rises to the sill away from it. That surface is usually more informative than the prediction, and it is the reason to reach for this method at all.

model is passed to gstat::fit.variogram() as the starting point. The default is a spherical model with the sill at the overall variance and the range at a third of the distances the variogram covers. That is a starting guess, not an analysis: a real one plots gstat::variogram() first and chooses. If the fit does not converge, that is what it is telling you.

Examples

xy <- cbind(runif(80), runif(80))
v <- xy[, 1] * 3 + rnorm(80, sd = 0.1)
if (requireNamespace("gstat", quietly = TRUE)) {
  plot(grid_kriging(xy, v))
  plot(grid_kriging(xy, v, statistic = "se"))
}
#> Warning: No convergence after 200 iterations: try different initial values?

#> Warning: No convergence after 200 iterations: try different initial values?