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Predict at new locations using a fitted spBPS model

predict.spBPS.Rd

Generates posterior predictive samples at new spatial locations using a fitted spBPS object. Memory usage is controlled via pred_batch_size

Usage

# S3 method for class 'spBPS'
predict(
  object,
  newdata,
  draws = 200L,
  pred_batch_size = 200L,
  n_cores = 1L,
  return_summary = FALSE,
  probs = c(0.025, 0.975),
  ...
)

Arguments

object

Object of class "spBPS" returned by spBPS().

newdata

List with elements X (u x p covariate matrix) and coords (u x d coordinate matrix).

draws

Integer. Number of posterior predictive draws. Default 200.

pred_batch_size

Integer. Number of prediction sites processed per batch. Default 200. Smaller values use less RAM; larger values may be faster.

n_cores

Integer. Number of OMP threads. Default 1.

return_summary

Logical. If FALSE (default) return full draw arrays. If TRUE return summary statistics.

probs

Numeric vector of length 2. Quantile probabilities for credible intervals when return_summary = TRUE. Default c(0.025, 0.975).

...

Currently unused.

Value

When return_summary = FALSE, a list with arrays Wu, Yu, MY of dimension u x q x R. When return_summary = TRUE, a list with u x q matrices mu_Yu, sd_Yu, q_lo, q_hi, mu_Wu.

See also

spBPS

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)

res <- spBPS(data, priors, coords, hyperpar, subset_size = 200)
#> 
#> ====================================================
#>          Welcome to spBPS Bayesian Engine
#> ====================================================
#> 
#> Partitioning data into K = 5 subsets ...
#> 
#> Computing local stacking weights over J = 1 models ...
#> Local weights computed in 0.1 s.
#> 
#> Computing global stacking weights over K = 5 partitions ...
#> Global weights computed in 0.0 s.
#> 
#> ====================================================
#>      spBPS pipeline completed successfully!
#> ====================================================
#>   Fitting:     0.1 s
#>   Combination: 0.0 s
#>   TOTAL:       0.1 s
#> 

u <- 500
p <- 2
q <- 1

X_u <- matrix(rnorm(u * p), ncol = p)
crd_u <- matrix(runif(u * 2), ncol = 2)

# Predictive sampling
pred <- predict(res, newdata=list(X=X_u, coords=crd_u),
                draws=200L, pred_batch_size=500L)
#> [predict.spBPS]  u = 500  draws = 200  cores = 1
#>   Completed in 0.3 s.

# Summary statistics only (memory-efficient for large u)
pred_sum <- predict(res, newdata=list(X=X_u, coords=crd_u),
                    draws=500L, return_summary=TRUE,
                    probs=c(0.025, 0.975))
#> [predict.spBPS]  u = 500  draws = 500  cores = 1
#>   Completed in 0.5 s.
# }