BayesMallows (version 2.2.3)

compute_posterior_intervals: Compute Posterior Intervals

Description

Compute posterior intervals of parameters of interest.

Usage

compute_posterior_intervals(model_fit, ...)
# S3 method for BayesMallows
compute_posterior_intervals(
  model_fit,
  parameter = c("alpha", "rho", "cluster_probs"),
  level = 0.95,
  decimals = 3L,
  ...
)
# S3 method for SMCMallows
compute_posterior_intervals(
  model_fit,
  parameter = c("alpha", "rho"),
  level = 0.95,
  decimals = 3L,
  ...
)

Arguments

model_fit

A model object.

...

Other arguments. Currently not used.

parameter

Character string defining which parameter to compute posterior intervals for. One of "alpha", "rho", or "cluster_probs". Default is "alpha".

level

Decimal number in \([0,1]\) specifying the confidence level. Defaults to 0.95.

decimals

Integer specifying the number of decimals to include in posterior intervals and the mean and median. Defaults to 3.

Details

This function computes both the Highest Posterior Density Interval (HPDI), which may be discontinuous for bimodal distributions, and the central posterior interval, which is simply defined by the quantiles of the posterior distribution.

References

See Also

Other posterior quantities: assign_cluster(), compute_consensus(), get_acceptance_ratios(), heat_plot(), plot.BayesMallows(), plot.SMCMallows(), plot_elbow(), plot_top_k(), predict_top_k(), print.BayesMallows()

Examples

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")

if (FALSE) {
  # 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")
}