Collection of Mean-Block Models over a Range of Cluster Counts

R6 class for a collection of mean-block models ([NormalBlockMeanBase]) with different numbers of clusters (q). Inherits its scaffolding (`print()`/`summary()`/`plot()`/`optimize()`, the `criteria` table) from [NormalBlockCollection]. Unlike [NormalBlockVarCollectionClusters], there is no SBM-path shortcut here: the shared clustering-heuristic registry's cov()/correlation-based methods are ill-suited to the mean-block family (see [NormalBlockMeanBase]'s own default), so each q is fit independently.

Super classes

NormalBlockCollection -> NormalBlockCollectionClusters -> NormalBlockMeanCollectionClusters

Active bindings

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NormalBlockMeanCollectionClusters$new()

• NormalBlockMeanCollectionClusters$clone()

Inherited methods

• NormalBlockCollection$print()
• NormalBlockCollection$summary()
• NormalBlockCollectionClusters$get_best_model()
• NormalBlockCollectionClusters$get_model()
• NormalBlockCollectionClusters$optimize()
• NormalBlockCollectionClusters$plot()
• NormalBlockCollectionClusters$refine()

---

NormalBlockMeanCollectionClusters$new()

Create a new [`NormalBlockMeanCollectionClusters`] object.

Usage

NormalBlockMeanCollectionClusters$new(
  mydata,
  q_list,
  zero_inflation = FALSE,
  sparsity = 0,
  control = NB_control()
)

Arguments

mydata

object of NormalBlockData class, with responses and design matrix

q_list

list of q values (number of groups) in the collection

zero_inflation

whether Y carries structural zeros; every model in the collection is then zero-inflated (Sigma diagonal or spherical only, see [NormalBlockMeanBase])

sparsity

sparsity penalty on the network density

control

structured list of more specific parameters, to generate with NB_control

Returns

A new [`NormalBlockMeanCollectionClusters`] object

---

NormalBlockMeanCollectionClusters$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockMeanCollectionClusters$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

ex <- generate_normal_block_mean_data(n = 50, p = 20, d = 1, q = 3)
data <- NormalBlockData$new(ex$Y, ex$X)
models <- normal_block(data, blocks = 2:5, model = "mean")
#> Fitting a normal-block-mean model with unknown q 
#> 	 number of blocks = 2           
	 number of blocks = 3           
	 number of blocks = 4           
	 number of blocks = 5           

#> DONE
models$plot(c("BIC", "ICL"))

models$get_best_model()
#> A diagonal normal-block-mean model with 2 unknown blocks .
#> ===========================================================================
#>  nb_param q n_edges sparsity   loglik deviance      BIC      ICL     EBIC niter
#>        23 2       0        0 -963.653 1927.305 2017.282 2017.282 2017.282     3
#> ===========================================================================
#> * Useful fields
#>     $model_par, $posterior_par / $var_par, $clustering 
#>     $loglik, $BIC, $ICL, $objective, $nb_param, $criteria
#> * Useful S3 methods
#>     print(), summary(), plot(), coef(), sigma(), fitted(), predict()