corLevDiag: Correlation or Covariance Matrix for a Diagonal Structure

In kergp: Gaussian Process Laboratory

Correlation or Covariance Matrix for a Diagonal Structure

Description

Compute the correlation or covariance matrix for a diagonal structure.

Usage

corLevDiag(par, nlevels, levels, lowerSQRT = FALSE, compGrad = TRUE,
  cov = 0)

Arguments

par	A numeric vector with length npVar where npVar is the number of variance parameters, namely 0, 1 or nlevels corresponding to the values of cov: 0, 1 and 2.
nlevels	Number of levels.
levels	Character representing the levels.
lowerSQRT	Logical. When TRUE the (lower) Cholesky root \mathbf{L} of the correlation or covariance matrix \mathbf{C} is returned instead of the correlation matrix.
compGrad	Logical. Should the gradient be computed?
cov	Integer 0, 1 or 2. If cov is 0, the matrix is a correlation matrix (or its Cholesky root) i.e. an identity matrix. If cov is 1 or 2, the matrix is a covariance (or its square root) with constant variance vector for code = 1 and with arbitrary variance vector for code = 2.

Value

A correlation matrix (or its Cholesky root) with the optional gradient attribute.

Examples

set.seed(123)
checkGrad <- TRUE
nlevels <- 12
sigma2 <- rexp(n = nlevels)
T0 <- corLevDiag(nlevels = nlevels, par = sigma2, cov = 2)
L0 <- corLevDiag(nlevels = nlevels, par = sigma2, cov = 2,
                 lowerSQRT = TRUE)

kergp documentation built on May 29, 2024, 10:25 a.m.