Skip to contents

spFFBS: Spatiotemporal Bayesian Pipeline (friendly interface)

spFFBS.Rd

A user-friendly, modular wrapper for running a Bayesian spatiotemporal filtering + weighting pipeline, with optional backward sampling, forecasting, and spatial interpolation.

Usage

spFFBS(
  Y,
  G,
  P,
  D,
  grid = list(tau = NULL, phi = NULL),
  prior,
  do_BS = FALSE,
  do_forecast = FALSE,
  do_spatial = FALSE,
  L = 200,
  tnew = NULL,
  spatial = NULL,
  num_threads = 1,
  verbose = TRUE
)

Arguments

Y

Response data (3D array or cube).

G

System matrix (cube).

P

Observation matrix (cube).

D

Spatial distance matrix.

grid

List with elements:

  • tau: numeric vector

  • phi: numeric vector

prior

Prior list for forward filter (m, C, nu, Psi)

do_BS

Logical: run backward sampling? (default: FALSE)

do_forecast

Logical: run temporal forecasts? (default: FALSE)

do_spatial

Logical: run spatial interpolation? (default: FALSE)

L

Number of posterior samples (default 200)

tnew

Forecast horizon (default 5)

spatial

Optional list for spatial: list(crd = , crdtilde = , Xtilde = )

num_threads

Number of cores for parallel computing (default: 1)

verbose

Logical; print progress messages to the console? (default: TRUE)

Value

A list with the components executed according to the flags.

Examples

n <- 50
t <- 5
p <- 2
q <- 2

Y <- array(rnorm(n*q*t), dim = c(n, q, t))
P <- array(rnorm(n*(p+n)*t), dim = c(n, (p+n), t))
G <- array(rnorm((p+n)*(p+n)*t), dim = c((p+n), (p+n), t))
coords <- matrix(runif(n*2), ncol = 2)
D <- as.matrix(dist(coords))

priors <- list("m" = matrix(0, n+p, q),
               "C" = diag(p+n),
               "nu" = 3,
               "Psi" = diag(q))

hyperpar <- list(tau = 0.5, phi = 1)

res <- spFFBS(Y = Y, G = G, P = P, D = D, grid = hyperpar, prior = priors)
#> 
#> ====================================================
#>          Welcome to spFFBS Bayesian Engine
#> ====================================================
#> 
#> Building parameter grid ... OK ( 1 models )
#> 
#> Running Forward Filtering (FF)...
#> 0.004 sec elapsed
#> FF completed.
#> 
#> Computing stacking weights ...
#> 
#> ====================================================
#>       Computing Dynamic Bayesian Predictive
#>              Stacking Weights (Wi)
#> ====================================================
#> 
#> Found:
#>    + Time points:   5 
#>    + Locations:     50 
#>    + Models (J):    1 
#> 
#> Using SERIAL backend (foreach sequential)
#> 
#> Evaluating model scores and optimizing weights...
#> Weight computation: 0.028 sec elapsed
#> 
#>  Weight matrix computed successfully.
#>    Dimensions:  50 x 1 
#> 
#> ====================================================
#> 
#> Total time: 0.035 sec elapsed
#> 0.036 sec elapsed
#> Global weights computed.
#> 
#> ====================================================
#>      spFFBS pipeline completed successfully!
#> ====================================================
#>