Collection of Normal-Block Models over a Sparsity Path

R6 class for a collection of normal-block models with a fixed clustering (blocks) and different sparsity levels.

Super classes

NormalBlockCollection -> NormalBlockCollectionSparsity -> NormalBlockVarCollectionSparsity

Active bindings

sparsity_details

list of information about model's penalties

criteria

a data frame with the values of some criteria ((approximated) log-likelihood, BIC) for the collection of models

stability_path

measure of edges stability based on StARS method

stability

mean edge stability along the sparsity penalties path

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NormalBlockVarCollectionSparsity$new()

• NormalBlockVarCollectionSparsity$get_best_model()

• NormalBlockVarCollectionSparsity$stability_selection()

• NormalBlockVarCollectionSparsity$clone()

Inherited methods

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

---

NormalBlockVarCollectionSparsity$new()

Create a new [`NormalBlockVarCollectionSparsity`] object.

Usage

NormalBlockVarCollectionSparsity$new(
  mydata,
  blocks,
  zero_inflation = FALSE,
  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)

zero_inflation

boolean to specify whether data is zero-inflated

control

structured list of parameters to handle sparsity control

Returns

A new [`NormalBlockVarCollectionSparsity`] object

---

NormalBlockVarCollectionSparsity$get_best_model()

Extract best model in the collection

Usage

NormalBlockVarCollectionSparsity$get_best_model(
  crit = c("BIC", "EBIC", "ICL", "StARS"),
  stability = 0.9
)

Arguments

crit

a character for the criterion used to performed the selection.

stability

if criterion = "StARS" gives level of stability required. Either "BIC", "EBIC", "ICL" or "StARS". Default is BIC

Returns

a [`NormalBlockVarUnknownClusters`] object

---

NormalBlockVarCollectionSparsity$stability_selection()

Compute the stability path by stability selection

Usage

NormalBlockVarCollectionSparsity$stability_selection(
  subsamples = NULL,
  n_subsamples = 10
)

Arguments

subsamples

a list of vectors describing the subsamples. The number of vectors (or list length) determines the number of subsamples used in the stability selection. Automatically set to 20 subsamples with size `10*sqrt(n)` if `n >= 144` and `0.8*n` otherwise following Liu et al. (2010) recommendations.

n_subsamples

number of subsamples to create if the subsamples are not given

---

NormalBlockVarCollectionSparsity$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockVarCollectionSparsity$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

ex <- generate_normal_block_var_data(n = 50, p = 20, d = 1, q = 3)
data <- NormalBlockData$new(ex$Y, ex$X)
models <- normal_block(data, blocks = ex$parameters$C, sparsity = TRUE,
                       control = NB_control(verbose = FALSE, n_sparsity_penalties = 5))
models$get_best_model("BIC")$sparsity
#> [1] 0.001058849