Group mixed external features using Gower distances and PAM

Partition an entity-level feature table using Gower dissimilarities and partitioning around medoids (PAM) from the optional cluster package. Use mfrm_cluster_pam() for this method. mfrm_cluster() is retained as an identical compatibility alias; it does not select an algorithm for you.

Usage

mfrm_cluster_pam(x, k, weights = NULL, missing = c("error", "omit"))

mfrm_cluster(x, k, weights = NULL, missing = c("error", "omit"))

# S3 method for class 'mfrm_clusters'
print(x, ...)

# S3 method for class 'mfrm_clusters'
summary(object, ...)

Arguments

x: An object returned by mfrm_features().
k: Number of groups, an integer from 2 to one less than the number of included entities, and no greater than their number of distinct profiles. The number is chosen by the user, not optimized automatically.
weights: Optional named, strictly positive finite numeric vector with one weight per selected feature. Defaults to equal weights. Names, not vector order, identify features. Remove a feature to exclude it.
missing: Either "error" (default) or "omit". The latter excludes incomplete entities explicitly, retaining their IDs, reasons, and missing group membership in the result. No values are imputed.
...: Reserved for method compatibility.
object: An object returned by mfrm_cluster_pam().

Value

An mfrm_clusters object containing membership (ID, Cluster, Medoid, Silhouette), cluster_summary, numeric and categorical profiles, medoids, the reviewed feature_data, and settings. Silhouette values describe within-sample separation, not membership probabilities or resampling stability. summary() returns the cluster-size/silhouette table.

Details

Numeric differences are divided by the feature range among included entities; nominal features use match/mismatch, ordered factors use their declared order, and logical features use symmetric binary differences. Numeric 0/1 features are treated as numeric, not asymmetric presence/absence. Every selected feature must vary among included entities. Gower scaling, feature types, weights, omission policy, and numeric ranges are retained. Numeric ranges and relative weights must be representable without overflow or underflow; rescale features or revise extreme weight ratios if refused.

PAM uses deterministic BUILD/SWAP initialization; tied distances may admit alternative partitions. Group numbers are arbitrary labels. These are exploratory groups, not latent classes, ability estimates, or assessments of rater quality. No inference, uncertainty propagation, new-entity classification, or resampling stability is provided. Omission may change both the sample and numeric ranges and does not correct missing-data bias.

Pairwise distances require quadratic memory. This interface is limited to 5,000 included entities. It does not silently sample larger inputs. This input limit does not guarantee low memory use or acceptable run time.

For feature selection, missingness review, and multiple-imputation examples, see vignette("mfrmr-external-features", package = "mfrmr").

Examples

if (requireNamespace("cluster", quietly = TRUE)) {
  # Fictional raters; R2 has unrecorded experience, not zero years.
  raters <- data.frame(Rater = paste0("R", 1:6),
    ExperienceYears = c(1, NA, 3, 12, 13, 14),
    Specialty = rep(c("Language", "Science"), each = 3))
  features <- mfrm_features(raters, "Rater", c("ExperienceYears", "Specialty"))
  # The default stops on missing features. Here omission is explicit.
  groups <- mfrm_cluster_pam(features, k = 2,
    weights = c(ExperienceYears = 2, Specialty = 1), missing = "omit")
  groups$membership  # R2 remains present with unavailable membership.
  summary(groups)
  groups$profiles
  groups$medoids
}
#>   Rater ExperienceYears Specialty
#> 3    R3               3  Language
#> 5    R5              13   Science