neonatal_data {makemyprior} | R Documentation |
Neonatal mortality data
Description
Simulated neonatal mortality data with 323 observations.
Usage
neonatal_data
Format
A list with the following variables:
- y
Response
- Ntrials
Number of trials for each cluster
- urban
Covariate indicating if cluster is urban (1) or rural (0)
- nu
Cluster effect indexes
- v
County effect indexes for iid effect
- u
County effect indexes for Besag effect
Examples
## 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)
[Package makemyprior version 1.2.1 Index]