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Control settings for sdalgcp

Source: R/sdalgcp.R
sdalgcp_control.Rd

Bundles the technical knobs so that a default fit needs none of them.

Usage

sdalgcp_control(
  delta = NULL,
  points_per_region = 16,
  point_method = c("regular", "uniform", "ssi"),
  scale = c("continuous", "grid"),
  phi = NULL,
  kappa = 0.5,
  kappa_t = 0.5,
  nugget = FALSE,
  confounding = c("none", "restricted"),
  reanchor = 2L,
  n_sim = 10000L,
  burnin = 2000L,
  thin = 8L,
  tilt_spatial = FALSE,
  nthreads = 0L
)

Arguments

delta

candidate-point spacing. If NULL (default) it is chosen automatically to place roughly points_per_region points in a typical region.

points_per_region

target number of candidate points per region used to pick delta automatically.

point_method

how candidate points are laid out: "regular" (deterministic grid, default), "uniform" or "ssi".

scale

how the spatial scale \(\phi\) is estimated: "continuous" (optimised directly, no grid – the default) or "grid" (profiled over phi). Spatio-temporal fits always profile \(\phi\) on a grid.

phi

optional \(\phi\) grid (only used when scale = "grid" or for spatio-temporal fits); chosen from the geometry if NULL.

kappa

spatial Matern smoothness (0.5, 1.5 or 2.5).

kappa_t

temporal Matern smoothness (spatio-temporal fits).

nugget

logical; add an unstructured region-level term (overdispersion). Requires scale = "continuous".

confounding

"none" (default) or "restricted". With "restricted", restricted spatial regression is used: the spatial random effect is constrained to the orthogonal complement of the fixed-effect design so it cannot absorb a spatially structured covariate (avoids spatial confounding / attenuation of beta). Spatial models only.

reanchor

number of re-anchoring passes (re-simulate the latent field at the optimum and refit) for reliable variance estimates. Default 2.

n_sim, burnin, thin

MCMC length controls for the latent-field sampler.

tilt_spatial

logical; for raster covariates, use the fully covariate-tilted correlation (see SDALGCP2_raster).

nthreads

OpenMP threads for the correlation assembly (0 = default).

Value

a list of control settings.

See also

sdalgcp

Examples

## defaults, then a faster grid-based fit with a nugget term
str(sdalgcp_control())
#> List of 15
#>  $ delta            : NULL
#>  $ points_per_region: num 16
#>  $ point_method     : chr "regular"
#>  $ scale            : chr "continuous"
#>  $ phi              : NULL
#>  $ kappa            : num 0.5
#>  $ kappa_t          : num 0.5
#>  $ nugget           : logi FALSE
#>  $ confounding      : chr "none"
#>  $ reanchor         : int 2
#>  $ n_sim            : int 10000
#>  $ burnin           : int 2000
#>  $ thin             : int 8
#>  $ tilt_spatial     : logi FALSE
#>  $ nthreads         : int 0
ctrl <- sdalgcp_control(scale = "grid", nugget = FALSE, n_sim = 4000,
                        burnin = 1000, thin = 6)