Calculate and scale the distance matrix used in a Kriging model. Include definiteness correction. Not to be called directly.
modelKrigingDistanceCalculation(
x,
distanceFunction,
parameters = NA,
distances,
scaling,
combineDistances,
indefiniteMethod,
indefiniteType,
indefiniteRepair,
lower
)a list with elements D (distance matrix), maxD (maximal distance for scaling purpose).
list of samples in input space
a suitable distance function of type f(x1,x2), returning a scalar distance value, preferably between 0 and 1. Maximum distances larger 1 are no problem, but may yield scaling bias when different measures are compared. Should be non-negative and symmetric. It can also be a list of several distance functions. In this case, Maximum Likelihood Estimation (MLE) is used to determine the most suited distance measure. The distance function may have additional parameters.
parameters passed to the distance function as a vector.
precomputed distances, set to NA if not available.
boolean, whether to scale the distance matrix.
boolean, whether to combine the distances of different functions.
method for handling non-conditionally-definite matrices.
type of handling for non-conditionally-definite matrices.
whether to further repair other conditions (beside definiteness).
lower boundary for distance function parameters.
modelKriging