Cluster Diagnostics

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'
print(x, digits = 3L, ...)

# S3 method for class 'net_cluster_diagnostics'
plot(x, type = NULL, ...)

# 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. For the print(), plot() and as.data.frame() methods: an object of class net_cluster_diagnostics.
...: Unsupported. Supplying unused arguments raises an error. In as.data.frame.net_cluster_diagnostics() and print.net_cluster_diagnostics(): Unsupported. Supplying unused arguments raises an error. In plot.net_cluster_diagnostics(): Forwarded to the underlying plot method.
digits: Integer. Decimal places for floating-point statistics. Default 3L.
type: Character. Forwarded to the underlying plot method. Valid values for distance: "silhouette" (default), "mds", "heatmap", "predictors". Valid values for mmm: "posterior" (default), "covariates" / "predictors".
row.names, optional: Standard as.data.frame arguments (ignored).

Value

cluster_diagnostics() returns a net_cluster_diagnostics object: a list carrying family, k, n, sizes, the per_cluster data frame (one row per cluster), overall, ics, metadata and source, as detailed above. as.data.frame() on that object returns the per_cluster data frame itself – one row per cluster, with family-specific columns.

In print.net_cluster_diagnostics(): The input object, invisibly.

In plot.net_cluster_diagnostics(): Whatever the underlying plot method returns: a ggplot object, invisibly; or, for the covariate forest views called with combined = FALSE, a list of ggplot objects named by cluster (invisibly).

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.

Methods

plot.net_cluster_diagnostics(): Delegates to the original clustering object's plot method (plot.net_clustering for distance-based diagnostics, plot.net_mmm_clustering or plot.net_mmm for model-based). The diagnostics object itself stores no plot geometry – it just keeps a reference to the source so the existing visual layer is reused.
print.net_cluster_diagnostics(): Prints a uniform header, family-specific quality / IC line, and a per-cluster table. Layout matches print.net_clustering and print.net_mmm.

Examples