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.