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

R6 class for a collection of normal-block models with different number of clusters (q) and different sparsity levels.

Super class

NormalBlockVarCollection -> NormalBlockVarCollectionClustersSparsity

Active bindings

q_list

number of blocks

sparsity

list of penalties used for each q

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NormalBlockVarCollectionClustersSparsity$new()

• NormalBlockVarCollectionClustersSparsity$get_model()

• NormalBlockVarCollectionClustersSparsity$get_best_model()

• NormalBlockVarCollectionClustersSparsity$plot()

• NormalBlockVarCollectionClustersSparsity$clone()

Inherited methods

• NormalBlockVarCollection$optimize()
• NormalBlockVarCollection$print()
• NormalBlockVarCollection$summary()

---

NormalBlockVarCollectionClustersSparsity$new()

Create a new [`NormalBlockVarCollectionClustersSparsity`] object.

Usage

NormalBlockVarCollectionClustersSparsity$new(
  mydata,
  q_list,
  zero_inflation = FALSE,
  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

boolean to specify whether data is zero-inflated

control

structured list of parameters to handle sparsity control

Returns

A new [`NormalBlockVarCollectionClustersSparsity`] object

---

NormalBlockVarCollectionClustersSparsity$get_model()

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

Usage

NormalBlockVarCollectionClustersSparsity$get_model(q, sparsity = NA)

Arguments

q

number of blocks asked by user.

sparsity

sparsity penalty asked by user

Returns

either a NormalBlockVarCollectionSparsity or a NormalBlockVarUnknownClusters object

---

NormalBlockVarCollectionClustersSparsity$get_best_model()

Extract best model in the collection

Usage

NormalBlockVarCollectionClustersSparsity$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 [`NormalBlockVarUnknownClusters`] object

---

NormalBlockVarCollectionClustersSparsity$plot()

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

Usage

NormalBlockVarCollectionClustersSparsity$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 [`ggplot`] heatmap

---

NormalBlockVarCollectionClustersSparsity$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockVarCollectionClustersSparsity$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.