compute_posterior_intervals.BayesMallows: Compute posterior intervals

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Compute posterior intervals

Description

Compute posterior intervals

Usage

## S3 method for class 'BayesMallows'
compute_posterior_intervals(
  model_fit,
  burnin = model_fit$burnin,
  parameter = "alpha",
  level = 0.95,
  decimals = 3L,
  ...
)

Arguments

model_fit	An object of class BayesMallows returned from compute_mallows.
burnin	A numeric value specifying the number of iterations to discard as burn-in. Defaults to model_fit$burnin, and must be provided if model_fit$burnin does not exist. See assess_convergence.
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.
...	Other arguments. Currently not used.

See Also

assess_convergence

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

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