neonatal_data {makemyprior} | R Documentation |
Simulated neonatal mortality data with 323 observations.
neonatal_data
A list with the following variables:
Response
Number of trials for each cluster
Covariate indicating if cluster is urban (1) or rural (0)
Cluster effect indexes
County effect indexes for iid effect
County effect indexes for Besag effect
## Not run:
vignette("neonatal_mortality", package = "makemyprior")
## End(Not run)
if (interactive() && requireNamespace("rstan")){
graph_path <- paste0(path.package("makemyprior"), "/neonatal.graph")
formula <- y ~ urban + mc(nu) + mc(v) +
mc(u, model = "besag", graph = graph_path, scale.model = TRUE)
set.seed(1)
find_pc_prior_param(lower = 0.1, upper = 10, prob = 0.9, N = 2e5)
prior <- make_prior(
formula, neonatal_data, family = "binomial",
prior = list(tree = "s1 = (u, v); s2 = (s1, nu)",
w = list(s1 = list(prior = "pc0", param = 0.25),
s2 = list(prior = "pc1", param = 0.75)),
V = list(s2 = list(prior = "pc",
param = c(3.35, 0.05)))))
posterior <- inference_stan(prior, iter = 150, warmup = 50,
seed = 1, init = "0", chains = 1)
# Note: For reliable results, increase the number of iterations
plot(prior)
plot_tree_structure(prior)
plot_posterior_fixed(posterior)
plot_posterior_stan(posterior, param = "prior", prior = TRUE)
}
## Not run:
posterior <- inference_stan(prior, iter = 15000, warmup = 5000,
seed = 1, init = "0", chains = 1)
plot(prior)
plot_tree_structure(prior)
plot_posterior_fixed(posterior)
plot_posterior_stan(posterior, param = "prior", prior = TRUE)
## End(Not run)