Plot Posterior Distributions

Plot posterior distributions of the parameters of the Mallows Rank model.

Usage

# S3 method for class 'BayesMallows'
plot(x, parameter = "alpha", items = NULL, ...)

Arguments

x

An object of type BayesMallows, returned from compute_mallows().

parameter

Character string defining the parameter to plot. Available options are "alpha", "rho", "cluster_probs", "cluster_assignment", and "theta".

items

The items to study in the diagnostic plot for rho. Either a vector of item names, corresponding to x$data$items or a vector of indices. If NULL, five items are selected randomly. Only used when parameter = "rho".

...

Other arguments passed to plot (not used).

See also

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

Examples

model_fit <- compute_mallows(setup_rank_data(potato_visual))
burnin(model_fit) <- 1000

# By default, the scale parameter "alpha" is plotted
plot(model_fit)

# We can also plot the latent rankings "rho"
plot(model_fit, parameter = "rho")
#> Items not provided by user. Picking 5 at random.

# By default, a random subset of 5 items are plotted
# Specify which items to plot in the items argument.
plot(model_fit, parameter = "rho",
     items = c(2, 4, 6, 9, 10, 20))

# When the ranking matrix has column names, we can also
# specify these in the items argument.
# In this case, we have the following names:
colnames(potato_visual)
#>  [1] "P1"  "P2"  "P3"  "P4"  "P5"  "P6"  "P7"  "P8"  "P9"  "P10" "P11" "P12"
#> [13] "P13" "P14" "P15" "P16" "P17" "P18" "P19" "P20"
# We can therefore get the same plot with the following call:
plot(model_fit, parameter = "rho",
     items = c("P2", "P4", "P6", "P9", "P10", "P20"))


if (FALSE) { # \dontrun{
  # Plots of mixture parameters:
  model_fit <- compute_mallows(
    setup_rank_data(sushi_rankings),
    model_options = set_model_options(n_clusters = 5))
  burnin(model_fit) <- 1000
  # Posterior distributions of the cluster probabilities
  plot(model_fit, parameter = "cluster_probs")
  # Cluster assignment plot. Color shows the probability of belonging to each
  # cluster.
  plot(model_fit, parameter = "cluster_assignment")
} # }