compute_consensus.BayesMallows: Compute Consensus Ranking

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Compute Consensus Ranking

Description

Compute Consensus Ranking

Usage

## S3 method for class 'BayesMallows'
compute_consensus(
  model_fit,
  type = "CP",
  burnin = model_fit$burnin,
  parameter = "rho",
  assessors = 1L,
  ...
)

Arguments

model_fit	Object of type BayesMallows returned from compute_mallows.
type	Character string specifying which consensus to compute. Either "CP" or "MAP". Defaults to "CP".
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 the parameter for which to compute the consensus. Defaults to "rho". Available options are "rho" and "Rtilde", with the latter giving consensus rankings for augmented ranks.
assessors	When parameter = "rho", this integer vector is used to define the assessors for which to compute the augmented ranking.
...	Other arguments passed on to other methods. Currently not used. Defaults to 1L, which yields augmented rankings for assessor 1.

See Also

Other posterior quantities: assign_cluster(), compute_consensus.SMCMallows(), compute_consensus(), compute_posterior_intervals.BayesMallows(), 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.