plot.BayesMallows | R Documentation |
Plot posterior distributions of the parameters of the Mallows Rank model.
## S3 method for class 'BayesMallows'
plot(x, parameter = "alpha", items = NULL, ...)
x |
An object of type |
parameter |
Character string defining the parameter to plot. Available
options are |
items |
The items to study in the diagnostic plot for |
... |
Other arguments passed to |
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()
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")
# 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"))
## Not run:
# 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")
## End(Not run)
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