determineNeighbors

<code>determineNeighbors</code> returns an N x k matrix of the nearest neighbors
for spatial locations coords, with the ith row giving indices of the k nearest
neighbors to the ith location, which are selected from among the 1,...(i-1)
other spatial locations. The first row is -1's, since the first location has
no neighbors. The i=2 through i=(k+1) rows each necessarily contain 1:i.

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

Daniel Turek

BayesNSGP

Bayesian Analysis of Non-Stationary Gaussian Process Models

Mark Risser

determineNeighbors function

<dl><dt>coords</dt>
<dd>N x 2 array of N 2-dimensional (x,y) spatial coordinates.</dd>
<dt>k</dt>
<dd>Scalar; number of neighbors</dd></dl>

Arguments

Determine the k-nearest neighbors for each spatial coordinate. — determineNeighbors

<dl>

<dt>coords</dt>
<dd>N x 2 array of N 2-dimensional (x,y) spatial coordinates.</dd>


<dt>k</dt>
<dd>Scalar; number of neighbors</dd>

</dl>

determineNeighbors: Determine the k-nearest neighbors for each spatial coordinate.

Description

Usage

Value

Arguments

Examples