plot.BayesMallows: Plot Posterior Distributions

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Plot Posterior Distributions

Description

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

Usage

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

Arguments

x	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.
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$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.BayesMallows(), compute_consensus.SMCMallows(), compute_consensus(), compute_posterior_intervals.BayesMallows(), compute_posterior_intervals.SMCMallows(), compute_posterior_intervals(), heat_plot(), plot.SMCMallows(), plot_elbow(), plot_top_k(), predict_top_k(), print.BayesMallowsMixtures(), print.BayesMallows()

Examples

# 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
# We set the burnin = 1000
model_fit$burnin <- 1000
# By default, the scale parameter "alpha" is plotted
plot(model_fit)
## Not run: 
  # We can also plot the latent rankings "rho"
  plot(model_fit, parameter = "rho")
  # 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)
  # We can therefore get the same plot with the following call:
  plot(model_fit, parameter = "rho",
       items = c("P2", "P4", "P6", "P9", "P10", "P20"))
  
## End(Not run)

## Not run: 
  # Plots of mixture parameters:
  # 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)
  model_fit$burnin <- 1000
  # We can then plot the posterior distributions of the cluster probabilities
  plot(model_fit, parameter = "cluster_probs")
  # We can also get a cluster assignment plot, showing the assessors along the horizontal
  # axis and the clusters along the vertical axis. The color show the probability
  # of belonging to each clusters. The assessors are sorted along the horizontal
  # axis according to their maximum a posterior cluster assignment. This plot
  # illustrates the posterior uncertainty in cluster assignments.
  plot(model_fit, parameter = "cluster_assignment")
  # See also ?assign_cluster for a function which returns the cluster assignment
  # back in a dataframe.

## End(Not run)

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