nsgpCovMat {GPFDA} | R Documentation |
Calculate a NSGP covariance matrix given a vector of hyperparameters
Description
Calculate a NSGP covariance matrix given a vector of hyperparameters
Usage
nsgpCovMat(
hp,
input,
inputSubsetIdx = NULL,
nBasis = 5,
corrModel = corrModel,
gamma = NULL,
nu = NULL,
cyclic = NULL,
whichTau = NULL,
calcCov = T
)
Arguments
hp |
Vector of hyperparameters estimated by function nsgpr. |
input |
List of Q input variables (see Details). |
inputSubsetIdx |
A list identifying a subset of the input values to be used in the estimation (see Details). |
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). |
calcCov |
Logical. Calculate covariance matrix or not. If FALSE, time or spatially-varying parameters are still provided. |
Value
A list containing
- Cov
Covariance matrix
- vareps
Noise variance
- As_perTau
List of varying anisotropy matrix over the input space
- sig2_perTau
Vector of signal variance over the input space
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")