inst/examples/compute_consensus_example.R

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

# 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(setup_rank_data(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
burnin(model_fit) <- 1000
# We then compute the CP consensus.
compute_consensus(model_fit, type = "CP")
# And we compute the MAP consensus
compute_consensus(model_fit, type = "MAP")

\dontrun{
  # CLUSTERWISE CONSENSUS
  # 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(
    setup_rank_data(sushi_rankings),
    model_options = set_model_options(n_clusters = 5))
  # Keeping the burnin at 1000, we can compute the consensus ranking per cluster
  burnin(model_fit) <- 1000
  cp_consensus_df <- compute_consensus(model_fit, type = "CP")
  # We can now make a table which shows the ranking in each cluster:
  cp_consensus_df$cumprob <- NULL
  stats::reshape(cp_consensus_df, direction = "wide", idvar = "ranking",
                 timevar = "cluster",
                 varying = list(sort(unique(cp_consensus_df$cluster))))
}

\dontrun{
  # MAP CONSENSUS FOR PAIRWISE PREFENCE DATA
  # We use the example dataset with beach preferences.
  model_fit <- compute_mallows(setup_rank_data(preferences = beach_preferences))
  # We set burnin = 1000
  burnin(model_fit) <- 1000
  # We now compute the MAP consensus
  map_consensus_df <- compute_consensus(model_fit, type = "MAP")

  # CP CONSENSUS FOR AUGMENTED RANKINGS
  # We use the example dataset with beach preferences.
  model_fit <- compute_mallows(
    setup_rank_data(preferences = beach_preferences),
    compute_options = set_compute_options(save_aug = TRUE, aug_thinning = 2))
  # We set burnin = 1000
  burnin(model_fit) <- 1000
  # We now compute the CP consensus of augmented ranks for assessors 1 and 3
  cp_consensus_df <- compute_consensus(
    model_fit, type = "CP", parameter = "Rtilde", assessors = c(1L, 3L))
  # We can also compute the MAP consensus for assessor 2
  map_consensus_df <- compute_consensus(
    model_fit, type = "MAP", parameter = "Rtilde", assessors = 2L)

  # Caution!
  # With very sparse data or with too few iterations, there may be ties in the
  # MAP consensus. This is illustrated below for the case of only 5 post-burnin
  # iterations. Two MAP rankings are equally likely in this case (and for this
  # seed).
  model_fit <- compute_mallows(
    setup_rank_data(preferences = beach_preferences),
    compute_options = set_compute_options(
      nmc = 1005, save_aug = TRUE, aug_thinning = 1))
  burnin(model_fit) <- 1000
  compute_consensus(model_fit, type = "MAP", parameter = "Rtilde",
                    assessors = 2L)
}