BPS_post: Perform the BPS sampling from posterior and posterior...

In spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning

Perform the BPS sampling from posterior and posterior predictive given a set of stacking weights

Description

Perform the BPS sampling from posterior and posterior predictive given a set of stacking weights

Usage

BPS_post(data, X_u, priors, coords, crd_u, hyperpar, W, R)

Arguments

data	list two elements: first named Y, second named X
X_u	matrix unobserved instances covariate matrix
priors	list priors: named \mu_b,V_b,a,b
coords	matrix sample coordinates for X and Y
crd_u	matrix unboserved instances coordinates
hyperpar	list two elemets: first named \delta, second named \phi
W	matrix set of stacking weights
R	integer number of desired samples

Value

list BPS posterior predictive samples

Examples

## Generate subsets of data
n <- 100
p <- 3
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
Y <- matrix(rnorm(n), nrow = n, ncol = 1)
crd <- matrix(runif(n*2), nrow = n, ncol = 2)
data_part <- subset_data(data = list(Y = Y, X = X, crd = crd), K = 10)

## Select competetive set of values for hyperparameters
delta_seq <- c(0.1, 0.2, 0.3)
phi_seq <- c(3, 4, 5)
## Fit local models
fit_list <- vector(length = 10, mode = "list")
for (i in 1:10) {
  Yi <- data_part$Y_list[[i]]
  Xi <- data_part$X_list[[i]]
  crd_i <- data_part$crd_list[[i]]
  p <- ncol(Xi)
  bps <- spBPS::BPS_weights(data = list(Y = Yi, X = Xi),
                           priors = list(mu_b = matrix(rep(0, p)),
                                         V_b = diag(10, p),
                                         a = 2,
                                         b = 2), coords = crd_i,
                                         hyperpar = list(delta = delta_seq,
                                                         phi = phi_seq),
                                                         K = 5)
  w_hat <- bps$W
  epd <- bps$epd
  fit_list[[i]] <- list(epd, w_hat) }

## Model combination weights between partitions using Bayesian Predictive Stacking
comb_bps <- BPS_combine(fit_list = fit_list, K = 10, rp = 1)
Wbps <- comb_bps$W
W_list <- comb_bps$W_list

## Generate prediction points
m <- 100
X_new <- matrix(rnorm(m*p), nrow = m, ncol = p)
crd_new <- matrix(runif(m*2), nrow = m, ncol = 2)

## Perform posterior and posterior predictive sampling
R <- 250
subset_ind <- sample(1:10, R, TRUE, Wbps)
postsmp_and_pred <- vector(length = R, mode = "list")
for (r in 1:R) {
 ind_s <- subset_ind[r]
 Ys <- matrix(data_part$Y_list[[ind_s]])
 Xs <- data_part$X_list[[ind_s]]
 crds <- data_part$crd_list[[ind_s]]
 Ws <- W_list[[ind_s]]
 result <- spBPS::BPS_post(data = list(Y = Ys, X = Xs), coords = crds,
                           X_u = X_new, crd_u = crd_new,
                           priors = list(mu_b = matrix(rep(0, p)),
                                         V_b = diag(10, p),
                                         a = 2,
                                         b = 2),
                                         hyperpar = list(delta = delta_seq,
                                                         phi = phi_seq),
                                                         W = Ws, R = 1)
 postsmp_and_pred[[r]] <- result}

spBPS documentation built on Oct. 25, 2024, 5:07 p.m.