Fit controlled cross-model reference partitions

K-means, Ward.D2 and Gaussian mixture models use the same data object, resampling indices and leakage-safe preprocessing as a fitted SOM ensemble. The function creates reference partitions for triangulation; it does not rank algorithms or define any method as ground truth.

Usage

fit_cross_models(
  ensemble,
  methods = c("kmeans", "ward"),
  k = ensemble$spec$k,
  kmeans_seeds = 1L,
  kmeans_nstart = 50L,
  kmeans_iter_max = 100L,
  gmm_model_names = NULL,
  keep_models = FALSE,
  fail_fast = FALSE,
  gmm_seed = 1L
)

Arguments

ensemble

A fitted som_ensemble defining data, preprocessing and resampling.

methods

Any of "kmeans", "ward" and "gmm".

k

Candidate numbers of clusters.

kmeans_seeds

Random seeds used only for K-means initialization.

kmeans_nstart

Number of random starts per K-means fit.

kmeans_iter_max

Maximum number of iterations per K-means start.

gmm_model_names

Optional covariance models passed to mclust::Mclust(). BIC selection occurs within each analysis split.

keep_models

Whether to retain fitted cross-model objects.

fail_fast

Whether to stop at the first fit failure.

gmm_seed

Base random seed for reproducible GMM initialization. A deterministic fit-specific seed is derived from the split and candidate k, and is retained in the result object.

Value

A som_cross_models object with partitions, warnings and explicit failures.

Examples

data <- simulate_som_scenario("clusters", n = 45, p = 3, seed = 4)
specification <- som_spec(c(3, 2), seeds = 1:2, rlen = 10, k = 2)
ensemble <- fit_som_ensemble(data, specification, keep_models = FALSE)
fit_cross_models(ensemble, methods = c("kmeans", "ward"))
#> <som_cross_models>
#>   successful fits: 2 
#>   failed fits    : 0 
#>   warnings       : 0 
#>   methods        : kmeans, ward