cluster_diagnostics: Cluster Diagnostics

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Cluster Diagnostics

Description

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 cluster_mmm()), or net_mmm_clustering.
...	Unsupported. Supplying unused arguments raises an error.
row.names, optional	Standard as.data.frame arguments (ignored).

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.

Value

A net_cluster_diagnostics object.

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)

grp <- cluster_mmm(seqs, k = 2, n_starts = 1, max_iter = 20, seed = 1)
cluster_diagnostics(grp)
as.data.frame(cluster_diagnostics(grp))

Nestimate documentation built on July 11, 2026, 1:09 a.m.