modelKrigingParameterizedLikelihood: Calculate negative log-likelihood

In CEGO: Combinatorial Efficient Global Optimization

Description

This is a wrapper for the Kriging likelihood function modelKrigingLikelihood. It is intended for the case where parameters of the distance function are also optimized during maximum likelihood estimation. Thus, the wrapper receives the data, computes the parameterized distance matrix and passes it to the standard likelihood function.

Usage

modelKrigingParameterizedLikelihood(
  xt,
  xs,
  ys,
  useLambda = FALSE,
  corr = fcorrGauss,
  indefiniteMethod = "none",
  indefiniteType = "PSD",
  indefiniteRepair = FALSE,
  returnLikelihoodOnly = TRUE,
  inverter = "chol",
  distanceFunction,
  combineDistances,
  distanceParametersLower,
  ntheta,
  scaling
)

Arguments

xt	vector, containing parameters like theta, p and lambda.
xs	training samples, which are the input for the distance function. Should be in list format.
ys	vector of observations at training sample locations.
useLambda	whether to use nugget effect, i.e., lambda (FALSE at default).
corr	whether to use nugget effect, i.e., lambda (fcorrGauss at default).
indefiniteMethod	The specific method used for correction: spectrum "clip", spectrum "flip", spectrum "square", spectrum "diffusion", feature embedding "feature", nearest definite matrix "near". Default is no correction: "none". See Zaefferer and Bartz-Beielstein (2016).
indefiniteType	The general type of correction for indefiniteness: "NSD","CNSD" or the default "PSD". See Zaefferer and Bartz-Beielstein (2016).
indefiniteRepair	boolean, whether conditions of the distance matrix (in case of "NSD","CNSD" correction type) or correlation matrix (in case of "PSD" correction type) are repaired.
returnLikelihoodOnly	boolean, whether the function should return only the likelihood, or a else a list (see return information below).
inverter	string specifying method for inversion of correlation matrix ("chol", cholesky decomposition at default, any other string leads to using the solve function).
distanceFunction	the distance function.
combineDistances	boolean, whether to combine several distances provided as a list of distance functions.
distanceParametersLower	lower boundary for the distance function(s) parameters. A vector in case of one distance, a list of vectors in case of several functions. The parameters are passed as a vector to each respective distance function.
ntheta	number of kernel parameters.
scaling	boolean, whether to scale the distance matrix.

Value

the numeric Likelihood value (if returnLikelihoodOnly is TRUE) or a list with elements:

NegLnLike

concentrated log-likelihood *-1 for minimising

Psi

correlation matrix

Psinv

inverse of correlation matrix (to save computation time in forrRegPredictor)

mu

MLE of model parameter mu

yMu

vector of observations y minus mu

SSQ

MLE of model parameter sigma^2

a

transformation vector for eigenspectrum transformation, see Zaefferer and Bartz-Beielstein (2016)

U

Matrix of eigenvectors for eigenspectrum transformation, see Zaefferer and Bartz-Beielstein (2016)

isIndefinite

whether the uncorrected correlation (kernel) matrix is indefinite

See Also

modelKrigingLikelihood

CEGO documentation built on May 14, 2021, 1:08 a.m.