Collection of Mean-Block Models over Cluster Counts and Sparsity Levels

R6 class for a collection of mean-block models ([NormalBlockMeanBase]) over both a range of cluster counts (q) and a sparsity path, i.e. one [NormalBlockMeanCollectionSparsity] per q. Mirrors [NormalBlockVarCollectionClustersSparsity], minus its SBM-path shortcut for the initial clustering (ill-suited to this family, see [NormalBlockMeanCollectionClusters]).

Super classes

NormalBlockCollection -> NormalBlockCollectionClustersSparsity -> NormalBlockMeanCollectionClustersSparsity

Active bindings

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NormalBlockMeanCollectionClustersSparsity$new()

• NormalBlockMeanCollectionClustersSparsity$clone()

Inherited methods

• NormalBlockCollection$optimize()
• NormalBlockCollection$print()
• NormalBlockCollection$summary()
• NormalBlockCollectionClustersSparsity$get_best_model()
• NormalBlockCollectionClustersSparsity$get_model()
• NormalBlockCollectionClustersSparsity$plot()

---

NormalBlockMeanCollectionClustersSparsity$new()

Create a new [`NormalBlockMeanCollectionClustersSparsity`] object.

Usage

NormalBlockMeanCollectionClustersSparsity$new(
  mydata,
  q_list,
  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

control

structured list of parameters to handle sparsity control

Returns

A new [`NormalBlockMeanCollectionClustersSparsity`] object

---

NormalBlockMeanCollectionClustersSparsity$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockMeanCollectionClustersSparsity$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

ex <- generate_normal_block_mean_data(n = 60, p = 20, d = 1, q = 3)
data <- NormalBlockData$new(ex$Y, ex$X)
models <- normal_block(data, blocks = 2:4, sparsity = TRUE, model = "mean",
                       control = NB_control(n_sparsity_penalties = 4))
#> Fitting a collection of normal-block-mean models with different values of q and different penalties 
#> 	 number of blocks = 2           
	 penalty = 0.553603           
	 penalty = 0.1192702           
	 penalty = 0.02569597           
	 penalty = 0.00553603           
	 number of blocks = 3           
	 penalty = 0.553603           
	 penalty = 0.1192702           
	 penalty = 0.02569597           
	 penalty = 0.00553603           
	 number of blocks = 4           
	 penalty = 0.553603           
	 penalty = 0.1192702           
	 penalty = 0.02569597           
	 penalty = 0.00553603           

#> DONE
models$plot("BIC")

models$get_best_model("BIC")
#> A full normal-block-mean model with 3 unknown blocks .
#> ===========================================================================
#>  nb_param q n_edges sparsity    loglik deviance      BIC      ICL     EBIC
#>        90 3      65    0.119 -1186.023 2372.045 2740.536 2741.148 2883.356
#>  niter
#>      8
#> ===========================================================================
#> * 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()