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Cluster Diagnostics

Source: R/cluster_diagnostics.R
cluster_diagnostics.Rd

Unified entry point for clustering quality information. Returns a net_cluster_diagnostics object that normalises the diagnostic surface across distance-based and model-based clusterings – you no longer have to know which fields live on net_clustering vs. net_mmm vs. the slim net_mmm_clustering attribute of a netobject_group.

Usage

cluster_diagnostics(x, ...)

# S3 method for class 'net_cluster_diagnostics'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x

A net_clustering, net_mmm, netobject_group (with attr(, "clustering") attached by cluster_network() or build_network(net_mmm)), or net_mmm_clustering.

...

Unsupported. Supplying unused arguments raises an error.

row.names, optional

Standard as.data.frame arguments (ignored).

Value

A net_cluster_diagnostics object.

Details

The returned object carries:

family

Either "distance" or "mmm".

k, n, sizes

Number of clusters, number of sequences, sizes vector.

per_cluster

A data.frame – one row per cluster, columns differ by family. Distance: cluster, size, pct, mean_within_dist, sil_mean. MMM: cluster, size, pct, mix_pct, avepp, class_err_pct.

overall

A named list of family-specific summary metrics (silhouette for distance; avepp_overall, entropy, classification_error for MMM).

ics

For MMM: a list with BIC, AIC, ICL. NULL for distance.

metadata

Method / dissimilarity / weighted / lambda etc.

source

The original clustering object, kept by reference so plot() can delegate without recomputing anything.

See also

print.net_cluster_diagnostics, plot.net_cluster_diagnostics, compare_mmm for k-sweep model selection (MMM only).

Examples

seqs <- data.frame(V1 = sample(c("A","B","C"), 30, TRUE),
                   V2 = sample(c("A","B","C"), 30, TRUE))
cl <- build_clusters(seqs, k = 2, method = "ward.D2")
cluster_diagnostics(cl)
#> Cluster Diagnostics (distance) [ward.D2 / hamming]
#>   Sequences: 30  |  Clusters: 2
#>   Quality: silhouette = 0.403
#> 
#>   Cluster  N           Mean within-dist  Silhouette
#>   1        20 (66.7%)  1.137             0.326
#>   2        10 (33.3%)  0.733             0.558
# \donttest{
fit <- cluster_mmm(seqs, k = 2, n_starts = 1, max_iter = 20, seed = 1)
cluster_diagnostics(fit)
#> Cluster Diagnostics (mmm) [k = 2]
#>   Sequences: 30  |  Clusters: 2  |  States: 3
#>   Quality: AvePP = 0.989  |  Entropy = 0.090  |  Class.Err = 0.0%
#>   ICs: LL = -61.147  |  BIC = 180.114  |  AIC = 156.293  |  ICL = 180.806
#> 
#>   Cluster  N           Mix%   AvePP  Class.Err%
#>   1        24 (80.0%)  79.4%  0.989   0.0%
#>   2        6 (20.0%)   20.6%  0.987   0.0%
as.data.frame(cluster_diagnostics(fit))
#>   cluster size pct  mix_pct     avepp class_err_pct
#> 1       1   24  80 79.44647 0.9889875             0
#> 2       2    6  20 20.55353 0.9867095             0
# }