compute_posterior_intervals: Compute Posterior Intervals

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Compute Posterior Intervals

Description

Compute posterior intervals of parameters of interest.

Usage

compute_posterior_intervals(model_fit, ...)

Arguments

model_fit	A model object.
...	other arguments passed to methods.

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. The HPDI intervals are computed using the HDInterval package \insertCitemeredith2018BayesMallows.

References

\insertAllCited

See Also

compute_mallows

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

Examples

# The example datasets potato_visual and potato_weighing contain complete
# rankings of 20 items, by 12 assessors. We first analyse these using the Mallows
# model:
model_fit <- compute_mallows(potato_visual)

# Se the documentation to compute_mallows for how to assess the convergence of the algorithm
# Having chosen burin = 1000, we compute posterior intervals
model_fit$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")

## Not run: 
  # Posterior intervals of cluster probabilities
  # We can run a mixture of Mallows models, using the n_clusters argument
  # We use the sushi example data. See the documentation of compute_mallows for a more elaborate
  # example
  model_fit <- compute_mallows(sushi_rankings, n_clusters = 5)
  # Keeping the burnin at 1000, we can compute the posterior intervals of the cluster probabilities
  compute_posterior_intervals(model_fit, burnin = 1000, parameter = "cluster_probs")

## End(Not run)

BayesMallows documentation built on Nov. 25, 2023, 5:09 p.m.