nsgprPredict: Prediction of NSGPR model

In GPFDA: Gaussian Process for Functional Data Analysis

Prediction of NSGPR model

Description

Prediction of NSGPR model

Usage

nsgprPredict(
  hp,
  response,
  input,
  inputNew,
  noiseFreePred = F,
  nBasis = nBasis,
  corrModel = corrModel,
  gamma = gamma,
  nu = nu,
  cyclic = cyclic,
  whichTau = whichTau
)

Arguments

hp	Vector of hyperparameters estimated by function nsgpr.
response	Response variable. This should be a (n x nSamples) matrix where each column is a realisation
input	List of Q input variables (see Details).
inputNew	List of Q test set input variables.
noiseFreePred	Logical. If TRUE, predictions will be noise-free.
nBasis	Number of B-spline basis functions in each coordinate direction along which parameters change.
corrModel	Correlation function specification used for g(.). It can be either "pow.ex" or "matern".
gamma	Power parameter used in powered exponential kernel function. It must be 0<gamma<=2.
nu	Smoothness parameter of the Matern class. It must be a positive value.
cyclic	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.
whichTau	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).

Value

A list containing

pred.mean

Mean of predictions for the test set.

pred.sd

Standard deviation of predictions for the test set.

noiseFreePred

Logical. If TRUE, predictions are noise-free.

References

Konzen, E., Shi, J. Q. and Wang, Z. (2020) "Modeling Function-Valued Processes with Nonseparable and/or Nonstationary Covariance Structure" <arXiv:1903.09981>.

Examples

## See examples in vignette:
# vignette("nsgpr", package = "GPFDA")

GPFDA documentation built on Sept. 11, 2023, 1:08 a.m.