n_loc <- 20
a <- simulatedData$a[1:n_loc]
logb <- simulatedData$logb[1:n_loc]
logs <- simulatedData$logs[1:n_loc]
y <- simulatedData$y[1:n_loc]
locs <- simulatedData$locs[1:n_loc,]
beta_a <- mean(a)
beta_b <- mean(logb)
fit <- spatialGEV_fit(
data = y,
locs = locs,
random = "ab",
init_param = list(
beta_a = beta_a,
beta_b = beta_b,
a = rep(0, n_loc),
log_b = rep(0, n_loc),
s = 0,
log_sigma_a = 0,
log_kappa_a = 0,
log_sigma_b = 0,
log_kappa_b = 0
),
reparam_s = "positive",
kernel = "spde",
silent = TRUE
)
loc_ind <- sample(n_loc, 5)
sam <- spatialGEV_sample(model=fit, n_draw=1e5,
observation=TRUE, loc_ind=loc_ind)
# sampled mean and sd
rbind(est = colMeans(sam$parameter_draws),
sd = apply(sam$parameter_draws, 2, sd))
# theoretical mean and sd
strep <- summary(fit$report, select = "all")
t(strep[rownames(strep) == "a",][fit$meshidxloc[sort(loc_ind)],])
t(strep[rownames(strep) == "log_b",][fit$meshidxloc[sort(loc_ind)],])
t(strep[!rownames(strep) %in% c("a", "log_b"),])
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