logLikH | R Documentation |
Generic Log-likelihood function This function can be used to compute loglikelihood for homGP/hetGP models
logLikH(
X0,
Z0,
Z,
mult,
theta,
g,
Delta = NULL,
k_theta_g = NULL,
theta_g = NULL,
logN = FALSE,
beta0 = NULL,
eps = sqrt(.Machine$double.eps),
covtype = "Gaussian"
)
X0 |
unique designs |
Z0 |
averaged observations |
Z |
replicated observations (sorted with respect to X0) |
mult |
number of replicates at each Xi |
theta |
scale parameter for the mean process, either one value (isotropic) or a vector (anistropic) |
g |
nugget of the nugget process |
Delta |
vector of nuggets corresponding to each X0i or pXi, that are smoothed to give Lambda |
k_theta_g |
constant used for linking nuggets lengthscale to mean process lengthscale, i.e., theta_g[k] = k_theta_g * theta[k], alternatively theta_g can be used |
theta_g |
either one value (isotropic) or a vector (anistropic), alternative to using k_theta_g |
logN |
should exponentiated variance be used |
beta0 |
mean, if not provided, the MLE estimator is used |
eps |
minimal value of elements of Lambda |
covtype |
covariance kernel type |
For hetGP, this is not the joint log-likelihood, only the likelihood of the mean process.
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