Description Usage Arguments Details Value Author(s)
Check that the new point is not too close to already known observations to avoid numerical issues. Closeness can be estimated with several distances.
1 | checkPredict(x, model, threshold = 1e-04, distance = "covdist", type = "UK")
|
x |
a vector representing the input to check, |
model |
list of objects of class |
threshold |
optional value for the minimal distance to an existing observation, default to |
distance |
selection of the distance between new observations, between " |
type |
" |
If the distance between 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) = √(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.
TRUE
if the point should not be tested.
Mickael Binois
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