Select the Number of Clusters by Split/Merge Search — SelectionNClusters • normalblockr

R6 class for selecting the number of clusters (q) by forward/backward split-and-merge exploration, comparing models with the ICL.

Kept internal/unexported for reference: empirically (see [NormalBlockVarCollectionClusters]'s `refine()` documentation) chaining every q from a single starting point this way is no longer recommended over [NormalBlockVarCollectionClusters]'s independent per-q cold starts followed by `refine()` – the latter matches or beats this class's quality at a fraction of the cost on every real dataset tested, and has no equivalent to this class's failure mode (an early bad split/merge propagating to every larger q it explores from there).

Public fields

best_models: list of models explored so far indexed by the cluster sizes
ICL_explored: list of ICL reached so far, indexed by the cluster sizes
n_clusters_range: the range of the cluster sizes to browse
control: store the list of user-defined model settings and optimization parameters

Active bindings

best_model: best model explored so far in term of ICL

Methods

Public methods

SelectionNClusters$new()
SelectionNClusters$fit()
SelectionNClusters$train_best_candidates()
SelectionNClusters$explore_forward()
SelectionNClusters$explore_backward()
SelectionNClusters$plot()
SelectionNClusters$clone()

`SelectionNClusters$new()`

Explores and selects the optimal number of classes

Usage

SelectionNClusters$new(
  mydata,
  n_clusters_range,
  zero_inflation = FALSE,
  sparsity = 0,
  control = NB_control()
)

Arguments

mydata: object of NormalBlockData class, with responses and design matrix
n_clusters_range: a size-2 vector with the range of cluster size to browse
zero_inflation: whether the models in the collection should be zero-inflated or not
sparsity: sparsity penalty on the network density
control: structured list of more specific parameters, to generate with NB_control

Returns

The best model in the ICL sense

`SelectionNClusters$fit()`

perform model selection with forward/backward exploration with split and merge strategy

Usage

SelectionNClusters$fit()

`SelectionNClusters$train_best_candidates()`

perform model selection with forward/backward exploration with split and merge strategy

Usage

SelectionNClusters$train_best_candidates(model, strategy, max_training = 3)

Arguments

model: a normal-block model
strategy: a character, either "split" or "merge"
max_training: maximal of model fully trained at each step of the exploration

`SelectionNClusters$explore_forward()`

perform forward exploration with a split strategy

Usage

SelectionNClusters$explore_forward()

`SelectionNClusters$explore_backward()`

perform backward exploration with a merge strategy

Usage

SelectionNClusters$explore_backward()

`SelectionNClusters$plot()`

Display the ICL for all the best models explored per number of cluster, and the winner

Usage

SelectionNClusters$plot()

Returns

a [`ggplot2::ggplot`] graph

`SelectionNClusters$clone()`

The objects of this class are cloneable with this method.

Usage

SelectionNClusters$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.