R: Evaluate covariance over upper triangle of distance matrix

CovarianceUpper {fields} | R Documentation

## Evaluate covariance over upper triangle of distance matrix

### Description

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.

### Usage

    ExponentialUpper(distMat, range = 1, alpha = 1/range)

### Arguments

`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

### Value

The covariance matrix, where only the upper triangle is calculated.

### Author(s)

John Paige

### See Also

`Exponential`

### Examples

    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)

[Package fields version 9.6.1 Index]