Collection of Mean-Block Models over a Sparsity Path

R6 class for a collection of mean-block models ([NormalBlockMeanBase]) with a fixed clustering (or a fixed number of blocks) and different sparsity levels applied to the p x p precision matrix of the variables. Mirrors [NormalBlockVarCollectionSparsity], minus the StARS/stability selection path, which relies on `fixed_tau`, not supported by the mean-block VEM.

Super classes

NormalBlockCollection -> NormalBlockCollectionSparsity -> NormalBlockMeanCollectionSparsity

Active bindings

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NormalBlockMeanCollectionSparsity$new()

• NormalBlockMeanCollectionSparsity$get_best_model()

• NormalBlockMeanCollectionSparsity$clone()

Inherited methods

• NormalBlockCollection$print()
• NormalBlockCollection$summary()
• NormalBlockCollectionSparsity$get_model()
• NormalBlockCollectionSparsity$optimize()
• NormalBlockCollectionSparsity$plot()

---

NormalBlockMeanCollectionSparsity$new()

Create a new [`NormalBlockMeanCollectionSparsity`] object.

Usage

NormalBlockMeanCollectionSparsity$new(mydata, blocks, control = NB_control())

Arguments

mydata

object of NormalBlockData class, with responses and design matrix

blocks

either a clustering matrix (known, fixed clustering) or a single integer (number of blocks to infer)

control

structured list of parameters to handle sparsity control

Returns

A new [`NormalBlockMeanCollectionSparsity`] object

---

NormalBlockMeanCollectionSparsity$get_best_model()

Extract best model in the collection

Usage

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

Arguments

crit

a character for the criterion used to perform the selection, either "BIC", "EBIC" or "ICL". Default is BIC

Returns

a [`NormalBlockMeanBase`] object

---

NormalBlockMeanCollectionSparsity$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockMeanCollectionSparsity$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 = 3, sparsity = TRUE, model = "mean",
                       control = NB_control(n_sparsity_penalties = 5))
#> Fitting a normal-block-mean model with sparsity path 
#> 	 penalty = 0.3489394           
	 penalty = 0.1103443           
	 penalty = 0.03489394           
	 penalty = 0.01103443           
	 penalty = 0.003489394           

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