Initialize parameter tuning for the Kriging model, setting the initial guess as well as bound constraints.
modelKrigingInit(
startTheta = NULL,
lowerTheta = NULL,
upperTheta = NULL,
useLambda,
lambdaLower,
lambdaUpper,
combineDistances,
nd,
distanceParameters = F,
distanceParametersLower = NA,
distanceParametersUpper = NA
)a list with elements x0 (start guess), lower (lower bound), upper (upper bound).
user provided start guess (optional).
lower boundary for theta values (log scale), the kernel parameters.
upper boundary for theta values (log scale), the kernel parameters.
boolean, whether nugget effect (lambda) is used.
lower boundary for lambda (log scale).
upper boundary for lambda (log scale).
boolean, whether multiple distances are combined.
number of distance function.
whether the distance function parameters should be optimized
lower boundary for parameters of the distance function, default is NA which means there are no distance function parameters. If several distance functions are supplied, this should be a list of lower boundary vectors for each function.
upper boundary for parameters of the distance function, default is NA which means there are no distance function parameters. If several distance functions are supplied, this should be a list of upper boundary vectors for each function.
modelKriging