BayesMallows: generate_constraints – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

generate_constraints

Generate Constraint Set from Pairwise Comparisons

Description

This function is relevant when compute_mallows is called repeatedly with the same data, e.g., when determining the number of clusters. It precomputes a list of constraints used internally by the MCMC algorithm, which otherwise would be recomputed each time compute_mallows is called.

Usage

generate_constraints(preferences, n_items, cl = NULL)

Arguments

`preferences`	Data frame of preferences. For the case of consistent rankings, `preferences` should be returned from `generate_transitive_closure`. For the case of inconsistent preferences, when using an error model as described in Crispino et al. (2019), a dataframe of preferences can be directly provided.
`n_items`	Integer specifying the number of items.
`cl`	Optional computing cluster used for parallelization, returned from `parallel::makeCluster`. Defaults to `NULL`.

Value

A list which is used internally by the MCMC algorithm.

References

Crispino M, Arjas E, Vitelli V, Barrett N, Frigessi A (2019). “A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds?” The Annals of Applied Statistics, 13(1), 492–519. doi: 10.1214/18-aoas1203, https://doi.org/10.1214/18-aoas1203.

Examples

# Here is an example with the beach preference data.
# First, generate the transitive closure.
beach_tc <- generate_transitive_closure(beach_preferences)

# Next, generate an initial ranking.
beach_init_rank <- generate_initial_ranking(beach_tc)

# Then generate the constrain set used intervally by compute_mallows
constr <- generate_constraints(beach_tc, n_items = 15)

# Provide all these elements to compute_mallows
model_fit <- compute_mallows(rankings = beach_init_rank,
preferences = beach_tc, constraints = constr)

## Not run: 
  # The constraints can also be generated in parallel
  library(parallel)
  cl <- makeCluster(detectCores() - 1)
  constr <- generate_constraints(beach_tc, n_items = 15, cl = cl)
  stopCluster(cl)

## End(Not run)

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

v1.0.1

GPL-3

Authors

Oystein Sorensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>), Valeria Vitelli [aut] (<https://orcid.org/0000-0002-6746-0453>), Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut]

Initial release