BPS_post_MvT: 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_MvT(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_r,\Psi,\nu
coords	matrix sample coordinates for X and Y
crd_u	matrix unboserved instances coordinates
hyperpar	list two elemets: first named \alpha, 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
q <- 2
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
Y <- matrix(rnorm(n*q), nrow = n, ncol = q)
crd <- matrix(runif(n*2), nrow = n, ncol = 2)
data_part <- subset_data(data = list(Y = Y, X = X, crd = crd), K = 10)

## Select competitive set of values for hyperparameters
alfa_seq <- c(0.7, 0.8, 0.9)
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$crd_list[[i]]
    bps <- spBPS::BPS_weights_MvT(data = list(Y = Yi, X = Xi),
                              priors = list(mu_B = matrix(0, nrow = p, ncol = q),
                                            V_r = diag(10, p),
                                            Psi = diag(1, q),
                                            nu = 3), coords = crd_i,
                                            hyperpar = list(alpha = alfa_seq,
                                                            phi = phi_seq),
                                            K = 5)
     w_hat <- bps$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 <- 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_MvT(data = list(Y = Ys, X = Xs), coords = crds,
                                X_u = X_new, crd_u = crd_new,
                                priors = list(mu_B = matrix(0, nrow = p, ncol = q),
                                              V_r = diag(10, p),
                                              Psi = diag(1, q),
                                              nu = 3),
                                              hyperpar = list(alpha = alfa_seq,
                                                              phi = phi_seq),
                                              W = Ws, R = 1)

  postsmp_and_pred[[r]] <- result}

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