modelKrigingDistanceCalculation: Kriging: Distance Matrix Calculation

In CEGO: Combinatorial Efficient Global Optimization

Kriging: Distance Matrix Calculation

Description

Calculate and scale the distance matrix used in a Kriging model. Include definiteness correction. Not to be called directly.

Usage

modelKrigingDistanceCalculation(
  x,
  distanceFunction,
  parameters = NA,
  distances,
  scaling,
  combineDistances,
  indefiniteMethod,
  indefiniteType,
  indefiniteRepair,
  lower
)

Arguments

x	list of samples in input space
distanceFunction	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	parameters passed to the distance function as a vector.
distances	precomputed distances, set to NA if not available.
scaling	boolean, whether to scale the distance matrix.
combineDistances	boolean, whether to combine the distances of different functions.
indefiniteMethod	method for handling non-conditionally-definite matrices.
indefiniteType	type of handling for non-conditionally-definite matrices.
indefiniteRepair	whether to further repair other conditions (beside definiteness).
lower	lower boundary for distance function parameters.

Value

a list with elements D (distance matrix), maxD (maximal distance for scaling purpose).

See Also

modelKriging

CEGO documentation built on May 29, 2024, 3:35 a.m.