heat_plot: Heat plot of posterior probabilities

In 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, burnin = model_fit$burnin, ...)

Arguments

model_fit	An object of type BayesMallows, returned from compute_mallows.
burnin	A numeric value specifying the number of iterations to discard as burn-in. Defaults to x$burnin, and must be provided if x$burnin does not exist. See assess_convergence.
...	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.

Value

A ggplot object.

See Also

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

Examples

# Setting the number of Monte Carlo samples very low for the example to run fast.
# A real application should run much longer, and have a large burnin.
model_fit <- compute_mallows(potato_visual, nmc = 500, seed = 1)
model_fit$burnin <- 100

heat_plot(model_fit)

# Items are ordered along the horizontal axis according to the ordering
# returned by compute_consensus, whose default argument is type="CP".

heat_plot(model_fit, type = "MAP")

BayesMallows documentation built on Nov. 25, 2023, 5:09 p.m.