Precompute aggregated region-level correlation matrices

Builds the \(N \times N \times\) length(phi) array of region-level correlations used by the SDA-LGCP model, where $$R(\phi)_{ij} = \sum_{k,l} w_{ik} w_{jl}\, C(\lVert x_{ik}-x_{jl}\rVert; \phi, \kappa)$$ (population-weighted) or the unweighted mean over candidate-point pairs. The heavy reduction runs in C++ (OpenMP-parallel over region pairs).

Usage

precompute_corr(points, phi, kappa = 0.5, weighted = NULL, nthreads = 0L)

Arguments

points

a list of length \(N\); each element holds $xy (an \(n_i \times 2\) matrix of candidate-point coordinates) and, when weighted, $weight (a length-\(n_i\) vector summing to 1). The "weighted" and "my_shp" attributes produced by the point-generation step are honoured and carried through.

phi

numeric vector of spatial scale parameters.

kappa

Matern smoothness; 0.5 (exponential, default), 1.5 or 2.5 use closed forms in C++.

weighted

logical; if NULL (default) it is taken from attr(points, "weighted").

nthreads

number of OpenMP threads; 0 (default) uses the OpenMP runtime default.

Value

a list with R (the correlation array) and phi, carrying weighted, my_shp and S_coord attributes on R.

See also

sda_points, mcml_fit

Examples

# \donttest{
data(sdalgcp_data)
pts <- sda_points(sdalgcp_data, delta = 1.2, method = 3)
cc  <- precompute_corr(pts, phi = c(2, 4, 6))
dim(cc$R)            # N x N x length(phi)
#> [1] 64 64  3
# }