Prediction of NSGPR model
nsgprPredict(
hp,
response,
input,
inputNew,
noiseFreePred = F,
nBasis = nBasis,
corrModel = corrModel,
gamma = gamma,
nu = nu,
cyclic = cyclic,
whichTau = whichTau
)A list containing
Mean of predictions for the test set.
Standard deviation of predictions for the test set.
Logical. If TRUE, predictions are noise-free.
Vector of hyperparameters estimated by function nsgpr.
Response variable. This should be a (n x nSamples) matrix where each column is a realisation
List of Q input variables (see Details).
List of Q test set input variables.
Logical. If TRUE, predictions will be noise-free.
Number of B-spline basis functions in each coordinate direction along which parameters change.
Correlation function specification used for g(.). It can be either "pow.ex" or "matern".
Power parameter used in powered exponential kernel function. It must be 0<gamma<=2.
Smoothness parameter of the Matern class. It must be a positive value.
Logical vector of dimension Q which defines which covariates are cyclic (periodic). For example, if basis functions should be cyclic only in the first coordinate direction, then cyclic=c(T,F). cyclic must have the same dimension of whichTau. If cyclic is TRUE for some coordinate direction, then cyclic B-spline functions will be used and the varying parameters (and their first two derivatives) will match at the boundaries of that coordinate direction.
Logical vector of dimension Q identifying which input coordinates the parameters are function of. For example, if Q=2 and parameters change only with respect to the first coordinate, then we set whichTau=c(T,F).
Konzen, E., Shi, J. Q. and Wang, Z. (2020) "Modeling Function-Valued Processes with Nonseparable and/or Nonstationary Covariance Structure" <arXiv:1903.09981>.
## See examples in vignette:
# vignette("nsgpr", package = "GPFDA")
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