Evaluate covariance over upper triangle of distance matrix
Evaluates the covariance over the upper triangle of a distance matrix
rather than over the entire matrix to reduce computation time. Note
that the chol function only requires the upper triangle of
the covariance matrix to perform the Cholesky decomposition.
ExponentialUpper(distMat, range = 1, alpha = 1/range)
distMat |
The distance matrix to evaluate the covariance over. |
range |
Range parameter default is one. Note that the scale can also be specified through the "theta" scaling argument used in fields covariance functions) |
alpha |
1/range |
The covariance matrix, where only the upper triangle is calculated.
John Paige
set.seed(123) #make distance matrix using the random locations coords = matrix(runif(10), ncol=2) distMat = rdist(coords) #compute covariance matrix, but only over the upper triangle upperCov = ExponentialUpper(distMat, range=.1) print(distMat) print(upperCov)
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