Skip to contents

Unified spatial BPS workflow (multivariate path, works for q = 1)

spBPS.Rd

Orchestrates subsetting, local stacking weight estimation, global stacking combination, and optional posterior or predictive simulation using the multivariate Student-t spatial model. Works for both multivariate outcomes and the univariate case via q = 1.

Usage

spBPS(
  data,
  priors,
  coords,
  hyperpar,
  subset_size = 500L,
  K = NULL,
  cv_folds = 5L,
  rp = 1,
  combine_method = c("bps", "pseudoBMA"),
  draws = 0L,
  newdata = NULL,
  include_latent = FALSE,
  cores = NULL
)

Arguments

data

List with matrices Y (response) and X (covariates).

priors

List of priors for the multivariate model (mu_B, V_r, Psi, nu).

coords

Matrix of observation coordinates.

hyperpar

List with elements alpha and phi (vectors allowed).

subset_size

Target subset size when K is not provided. Default 500.

K

Optional number of subsets. When NULL, computed as ceiling(nrow(Y) / subset_size) and lower-bounded at 1.

cv_folds

Number of folds for local cross-validation (default 5).

rp

Fraction of rows used when recomputing global stacking weights (passed to BPS_combine). Ignored when combine_method = "pseudoBMA".

combine_method

Choose between Bayesian Predictive Stacking ("bps") or pseudo-BMA ("pseudoBMA") for combining subsets.

draws

Number of joint posterior/predictive draws to return (0 to skip). When positive, newdata must be supplied because draws are obtained via BPS_post_MvT which jointly samples posterior and predictive.

newdata

Optional list with X and coords for prediction locations; required when either draw count is positive.

include_latent

Logical; if TRUE, posterior draws include latent processes.

cores

Optional integer; when >1 a parallel backend is registered internally via doParallel::registerDoParallel(cores) for the fit and draw loops. When NULL, the existing foreach backend (if any) is used.

Value

List with components subsets, weights_global, weights_local, epd, and optional posterior and predictive draws.

Examples

# \donttest{
n <- 1000
p <- 2
q <- 1

Y <- matrix(rnorm(n*q), ncol = q)
X <- matrix(rnorm(n*p), ncol = p)
coords <- matrix(runif(n*2), ncol = 2)

data <- list(Y = Y, X = X)
priors <- list(mu_B = matrix(0, nrow = p, ncol = q),
                             V_r = diag(10, p),
                             Psi = diag(1, q),
                             nu = 3)
hyperpar <- list(alpha = 0.5, phi = 1)
subset_size <- 200

res <- spBPS(data, priors, coords, hyperpar, subset_size = subset_size)
#> 
#> ====================================================
#>          Welcome to spBPS Bayesian Engine
#> ====================================================
#> 
#> Pritioning data into K = 5 subsets ... 
#> 
#> Computing local stacking weights over J = 1 models ...
#> Local weights computed.
#> 
#> Computing global stacking weights over K = 5 partitions ...
#> Global weights computed.
#> 
#> ====================================================
#>      spBPS pipeline completed successfully!
#> ====================================================
#> 

# }