checkPredict(x, model, threshold = 1e-04, distance = "covdist", type = "UK")km, one for each objective functions,1e-4,euclidean",
"covdist" (default) and "covratio", see details,SK" or "UK" (default), depending whether uncertainty related to trend estimation has to be taken into account.TRUE if the point should not be tested.
x and the closest observations in model is below
threshold, x should not be evaluated to avoid numerical instabilities.
The distance can simply be the Euclidean distance or the canonical distance associated with the kriging covariance k:
$$d(x,y) = \sqrt{k(x,x) - 2k(x,y) + k(y,y)}.$$
The last solution is the ratio between the prediction variance at x and the variance of the process.