Base Class for a Collection over Cluster Counts and Sparsity Levels — NormalBlockCollectionClustersSparsity • normalblockr

Shared scaffolding for [NormalBlockVarCollectionClustersSparsity] and [NormalBlockMeanCollectionClustersSparsity]: a collection of sparsity sub-collections, one per q. Everything family-agnostic (two-key model lookup, model selection over both axes, the criteria heatmap) lives here; subclasses only build `self$models` and name themselves.

Super class

NormalBlockCollection -> NormalBlockCollectionClustersSparsity

Active bindings

q_list: number of blocks
sparsity: list of penalties used for each q

Methods

Public methods

NormalBlockCollectionClustersSparsity$get_model()
NormalBlockCollectionClustersSparsity$get_best_model()
NormalBlockCollectionClustersSparsity$plot()
NormalBlockCollectionClustersSparsity$clone()

Inherited methods

`NormalBlockCollectionClustersSparsity$get_model()`

returns a collection of models corresponding to given q or one single model if penalty is also given

Usage

NormalBlockCollectionClustersSparsity$get_model(q, sparsity = NA)

Arguments

q: number of blocks asked by user.
sparsity: sparsity penalty asked by user

Returns

either a sparsity sub-collection or a single model object

`NormalBlockCollectionClustersSparsity$get_best_model()`

Extract best model in the collection

Usage

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

Arguments

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

Returns

a single fitted model object

`NormalBlockCollectionClustersSparsity$plot()`

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

Usage

NormalBlockCollectionClustersSparsity$plot(
  criterion = c("deviance", "ICL", "BIC", "EBIC"),
  n_intervals = NULL
)

Arguments

criterion: The criteria to plot in `c("deviance", BIC", "EBIC", "ICL")`. Defaults deviance.
n_intervals: number of intervals into which the penalties range should be split

Returns

a [`ggplot2::ggplot`] heatmap

`NormalBlockCollectionClustersSparsity$clone()`

The objects of this class are cloneable with this method.

Usage

NormalBlockCollectionClustersSparsity$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.