nsCrossdist3d: Calculate coordinate-specific cross-distance matrices, only...

In BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models

Description

nsCrossdist3d generates and returns new 3-dimensional arrays containing the former dist1_sq, dist2_s1, and dist12 matrices, but only as needed for the k nearest-neighbors of each location. these 3D matrices (dist1_3d, dist2_3d, and dist12_3d) are used in the new implementation of calculateAD_ns().

Usage

nsCrossdist3d(
  coords,
  predCoords,
  P_nID,
  scale_factor = NULL,
  isotropic = FALSE
)

Arguments

coords	N x d matrix; contains the x-y coordinates of stations.
predCoords	M x d matrix
P_nID	N x k matrix; contains indices of nearest neighbors.
scale_factor	Scalar; optional argument for re-scaling the distances.
isotropic	Logical; indicates whether distances should be calculated separately for each coordinate dimension (FALSE) or simultaneously for all coordinate dimensions (TRUE). isotropic = FALSE can only be used for two-dimensional coordinate systems.

Value

Arrays with nearest neighbor distances in each coordinate direction. When the spatial dimension d > 2, dist1_3d contains squared Euclidean distances, and dist2_3d and dist12_3d are empty.

Examples

# Generate some coordinates and neighbors
coords <- cbind(runif(100),runif(100))
predCoords <- cbind(runif(200),runif(200))
P_nID <- FNN::get.knnx(coords, predCoords, k = 10)$nn.index # Prediction NN
# Calculate distances
Pdist <- nsCrossdist3d(coords, predCoords, P_nID)

BayesNSGP documentation built on Jan. 9, 2022, 9:07 a.m.