neonatal_data: Neonatal mortality data

In makemyprior: Intuitive Construction of Joint Priors for Variance Parameters

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)

makemyprior documentation built on Sept. 11, 2024, 6:56 p.m.