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Assign Assessors to Clusters

Source: R/assign_cluster.R
assign_cluster.Rd

Assign assessors to clusters by finding the cluster with highest posterior probability.

Usage

assign_cluster(model_fit, soft = TRUE, expand = FALSE)

Arguments

model_fit

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

soft

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.

expand

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.

Value

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.

See also

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

Examples

# Fit a model with three clusters to the simulated example data
set.seed(1)
mixture_model <- compute_mallows(
  data = setup_rank_data(cluster_data),
  model_options = set_model_options(n_clusters = 3),
  compute_options = set_compute_options(nmc = 5000, burnin = 1000)
)

head(assign_cluster(mixture_model))
#>   assessor   cluster probability map_cluster
#> 1        1 Cluster 1     0.20525   Cluster 2
#> 2        1 Cluster 2     0.77775   Cluster 2
#> 3        1 Cluster 3     0.01700   Cluster 2
#> 4        2 Cluster 1     0.10625   Cluster 2
#> 5        2 Cluster 2     0.87125   Cluster 2
#> 6        2 Cluster 3     0.02250   Cluster 2
head(assign_cluster(mixture_model, soft = FALSE))
#>    assessor probability map_cluster
#> 2         1     0.77775   Cluster 2
#> 5         2     0.87125   Cluster 2
#> 8         3     0.91450   Cluster 2
#> 11        4     0.92000   Cluster 2
#> 14        5     0.77675   Cluster 2
#> 17        6     0.91525   Cluster 2