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##
## spatial econometric models and model comparison with DIC, WAIC, and model probabilities. N = 520
##
#devtools::load_all("~/dev/geostan")
library(geostan)
row = 12
col = 26
N <- row * col
sdl <- prep_sar_data2(row = row, col = col, quiet = TRUE)
w <- sdl$W
x <- sim_sar(w = w, rho = .5)
mu = (.5 * x - .25 * w %*% w)[,1]
y <- sim_sar(w = w, rho = .5, mu = mu)
df <- data.frame(y=y, x=x)
iter = 175
sdlm <- stan_sar(y ~ x,
data = df,
type = "SDLM",
sar_parts = sdl,
chains = 1,
iter = iter,
quiet = TRUE,
keep_all = TRUE,
slim = F)
sdem <- stan_sar(y ~ x,
data = df,
type = "SDEM",
sar_parts = sdl,
chains = 1,
iter = iter,
quiet = TRUE,
keep_all = TRUE,
slim = F)
print(rbind(dic(sdlm), dic(sdem)))
print(rbind(waic(sdlm), waic(sdem)))
# library(bridgesampling)
# sdlm <- bridge_sampler(sdlm$stanfit)
# sdem <- bridge_sampler(sdem$stanfit)
# cat("post_prob (SDLM, SDEM):\n", post_prob(sdlm, sdem), "\n")
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