inst/examples/compute_posterior_intervals_example.R

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

set.seed(1)
model_fit <- compute_mallows(
  setup_rank_data(potato_visual),
  compute_options = set_compute_options(nmc = 3000, burnin = 1000))

# First we compute the interval for alpha
compute_posterior_intervals(model_fit, parameter = "alpha")
# We can reduce the number decimals
compute_posterior_intervals(model_fit, parameter = "alpha", decimals = 2)
# By default, we get a 95 % interval. We can change that to 99 %.
compute_posterior_intervals(model_fit, parameter = "alpha", level = 0.99)
# We can also compute the posterior interval for the latent ranks rho
compute_posterior_intervals(model_fit, parameter = "rho")

\dontrun{
  # Posterior intervals of cluster probabilities
  model_fit <- compute_mallows(
    setup_rank_data(sushi_rankings),
    model_options = set_model_options(n_clusters = 5))
  burnin(model_fit) <- 1000

  compute_posterior_intervals(model_fit, parameter = "alpha")

  compute_posterior_intervals(model_fit, parameter = "cluster_probs")
}