BayesMallows (version 0.3.0)

compute_consensus: Compute Consensus Ranking

Description

Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus vitelli2018BayesMallows. For mixture models, the consensus is given for each mixture.

Usage

compute_consensus(model_fit, type = "CP", burnin = model_fit$burnin)

Arguments

model_fit

An object 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.

References

Examples

# NOT RUN {
# 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
# We then compute the CP consensus.
compute_consensus(model_fit, type = "CP")
# And we compute the MAP consensus
compute_consensus(model_fit, type = "MAP")

# }
# NOT RUN {
  # 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(sushi_rankings, n_clusters = 5)
  # Keeping the burnin at 1000, we can compute the consensus ranking per cluster
  model_fit$burnin <- 1000
  cp_consensus_df <- compute_consensus(model_fit, type = "CP")
  # Using dplyr::select and tidyr::cumprob we can now make a table
  # which shows the ranking in each cluster:
  library(dplyr)
  library(tidyr)
  cp_consensus_df %>%
    select(-cumprob) %>%
    spread(key = cluster, value = item)
# }
# NOT RUN {
# }
# NOT RUN {
  # MAP CONSENSUS FOR PAIRWISE PREFENCE DATA
  # We use the example dataset with beach preferences.
  model_fit <- compute_mallows(preferences = beach_preferences)
  # We set burnin = 1000
  model_fit$burnin <- 1000
  # We now compute the MAP consensus
  map_consensus_df <- compute_consensus(model_fit, type = "MAP")
# }