Assign assessors to clusters by finding the cluster with highest posterior probability.
assign_cluster(
model_fit,
burnin = model_fit$burnin,
soft = TRUE,
expand = FALSE
)A dataframe. If soft = FALSE, it has one row per assessor, and columns assessor,
probability and map_cluster. If soft = TRUE, it has n_cluster
rows per assessor, and the additional column cluster.
An object of type BayesMallows, returned from
compute_mallows.
A numeric value specifying the number of iterations
to discard as burn-in. Defaults to model_fit$burnin, and must be
provided if model_fit$burnin does not exist. See assess_convergence.
A logical specifying whether to perform soft or
hard clustering. If soft=TRUE, all cluster probabilities
are returned, whereas if soft=FALSE, only the maximum a
posterior (MAP) cluster probability is returned, per assessor. In the
case of a tie between two or more cluster assignments, a random cluster
is taken as MAP estimate.
A logical specifying whether or not to expand the rowset
of each assessor to also include clusters for which the assessor has
0 a posterior assignment probability. Only used when soft = TRUE. Defaults
to FALSE.
compute_mallows for an example where this function is used.
Other posterior quantities:
compute_consensus.BayesMallows(),
compute_consensus.SMCMallows(),
compute_consensus(),
compute_posterior_intervals.BayesMallows(),
compute_posterior_intervals.SMCMallows(),
compute_posterior_intervals(),
heat_plot(),
plot.BayesMallows(),
plot.SMCMallows(),
plot_elbow(),
plot_top_k(),
predict_top_k(),
print.BayesMallowsMixtures(),
print.BayesMallows()