heat_plot: Heat plot of posterior probabilities

In ocbe-uio/BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Heat plot of posterior probabilities

Description

Generates a heat plot with items in their consensus ordering along the horizontal axis and ranking along the vertical axis. The color denotes posterior probability.

Usage

heat_plot(model_fit, ...)

Arguments

model_fit	An object of type BayesMallows, returned from compute_mallows().
...	Additional arguments passed on to other methods. In particular, type = "CP" or type = "MAP" can be passed on to compute_consensus() to determine the order of items along the horizontal axis.

Details

In models with a single cluster, the items are sorted along the x-axis according to the consensus ranking. In models with more than one cluster, the items are sorted alphabetically.

Value

A ggplot object.

See Also

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

Examples

set.seed(1)
model_fit <- compute_mallows(
  setup_rank_data(potato_visual),
  compute_options = set_compute_options(nmc = 2000, burnin = 500))

heat_plot(model_fit)
heat_plot(model_fit, type = "MAP")

## Model with three clusters
mod <- compute_mallows(
  data = setup_rank_data(rankings = cluster_data),
  model_options = set_model_options(n_clusters = 3),
  compute_options = set_compute_options(nmc = 10000, burnin = 1000)
)

heat_plot(mod)
heat_plot(mod, type = "MAP")

ocbe-uio/BayesMallows documentation built on July 4, 2025, 3:05 a.m.