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Base Class for a Collection of Models over a Range of Cluster Counts

Source: R/NormalBlockCollectionClusters.R
NormalBlockCollectionClusters.Rd

Shared scaffolding for [NormalBlockVarCollectionClusters] and [NormalBlockMeanCollectionClusters]: everything that does not depend on the model family (model lookup, model selection, the criteria plot and the split/merge `refine()` search). Concrete subclasses only build `self$models` in their `initialize()` and name themselves through `who_am_I`.

Super class

NormalBlockCollection -> NormalBlockCollectionClusters

Active bindings

q_list

number of blocks

Methods

Public methods

Inherited methods

NormalBlockCollectionClusters$get_model()

returns the unknown-clusters model corresponding to given q

Usage

NormalBlockCollectionClusters$get_model(q)

Arguments

q

number of blocks asked by user

Returns

A unknown-clusters object with given value q

NormalBlockCollectionClusters$get_best_model()

Extract best model in the collection

Usage

NormalBlockCollectionClusters$get_best_model(
  crit = c("ICL", "BIC", "EBIC", "deviance")
)

Arguments

crit

a character for the criterion used to performed the selection. Either "ICL" or "BIC". "ICL" is the default criterion

Returns

a [`unknown-clusters`] object

NormalBlockCollectionClusters$plot()

Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of network fits (a [`Networkfamily`])

Usage

NormalBlockCollectionClusters$plot(
  criteria = c("deviance", "ICL", "BIC", "EBIC")
)

Arguments

criteria

vector of characters. The criteria to plot in `c("deviance", "BIC", "ICL")`. Defaults to all of them.

Returns

a [`ggplot2::ggplot`] graph

NormalBlockCollectionClusters$optimize()

optimizes every model in the collection, then – if `control$refine` is `TRUE` (see [NB_control()], default `FALSE`) – calls [refine()] automatically.

Usage

NormalBlockCollectionClusters$optimize(
  control = list(niter = 500, threshold = 1e-04, verbose = TRUE)
)

Arguments

control

optimization parameters (niter, threshold, verbose)

NormalBlockCollectionClusters$refine()

Tries to improve every model in the collection with a short split-and-reoptimize trial seeded from its smaller-q neighbor (`"split"`), a short merge-and-reoptimize trial seeded from its larger-q neighbor (`"merge"`), or both (the default); a candidate replaces the original only if it strictly lowers the deviance, so this can only improve (or leave unchanged) each model it touches. Only contiguous q pairs (`q` and `q -/+ 1`, both present in the collection) are refined. See `inst/methods_initialization_and_refine.md` for the rationale and empirical evidence.

Usage

NormalBlockCollectionClusters$refine(
  trial_niter = 2,
  max_candidates = 30,
  directions = c("split", "merge"),
  verbose = self$control$verbose
)

Arguments

trial_niter

number of EM iterations used for the cheap trial candidates (passed to `candidates_split()`/`candidates_merge()`) before fully re-optimizing only the best one.

max_candidates

passed to `candidates_merge()` (ignored for `"split"`, which is never combinatorial in q) – see its documentation.

directions

which neighbor(s) to seed refinement candidates from: `"split"` (smaller-q neighbor), `"merge"` (larger-q neighbor), or both (the default).

verbose

whether to print, for each q attempted, whether the candidate from that neighbor improved on it. Defaults to `control$verbose` (the value set at construction, see [NB_control()]).

Returns

invisibly returns `self`; improved models replace the originals in `$models` in place.

NormalBlockCollectionClusters$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockCollectionClusters$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# An internal abstract base class, never instantiated directly -- see
# normal_block() for how collections are created and fitted.