library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
library(gmo)
data <- list(J = 8,
K = 2,
y = c(28, 8, -3, 7, -1, 1, 18, 12),
sigma = c(15, 10, 16, 11, 9, 11, 10, 18))
local_file <- "models/8schools_local.stan"
M <- 2
m <- 1
draws <- 10
phi <- c(2,5)
seed <- 42
alpha <- "random"
# Check conditional approximation.
g_alpha <- optimizing(stan_model(local_file),
data=c(data, list(phi=phi)),
seed=seed, init=alpha,
as_vector=FALSE,
draws=M*draws, constrained=FALSE)
alpha <- g_alpha$par
# Check conditional approximation with warm start.
g_alpha <- optimizing(stan_model(local_file),
data=c(data, list(phi=phi)),
seed=seed, init=alpha,
as_vector=FALSE,
draws=M*draws, constrained=FALSE)
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