Base Class for a Collection of Models over a Sparsity Path

Shared scaffolding for [NormalBlockVarCollectionSparsity] and [NormalBlockMeanCollectionSparsity]: the warm-started path traversal, the penalty lookup and the criteria plot. Concrete subclasses derive the penalty grid in their `initialize()` and provide their own `get_best_model()` (only the variance-block family offers StARS).

Super class

NormalBlockCollection -> NormalBlockCollectionSparsity

Public fields

data

object of NormalBlockData class, with responses and design matrix

Active bindings

q

number of blocks

blocks

group matrix or number of blocks

sparsity

list of sparsity penalties

Methods

Public methods

• NormalBlockCollectionSparsity$optimize()

• NormalBlockCollectionSparsity$get_model()

• NormalBlockCollectionSparsity$plot()

• NormalBlockCollectionSparsity$clone()

Inherited methods

• NormalBlockCollection$print()
• NormalBlockCollection$summary()

---

NormalBlockCollectionSparsity$optimize()

optimizes every model in the sparsity path, warm-starting each one (after the first) from the previous, adjacent penalty's converged parameters (see the family's base class's `warm_start_from()`) instead of re-deriving everything from the heuristic clustering, the way the generic [NormalBlockCollection] `optimize()` would. `blocks` (hence q) is fixed across the whole path, only the sparsity penalty changes, so the warm start is always between models of matching shape.

Usage

NormalBlockCollectionSparsity$optimize(
  control = list(niter = 500, threshold = 1e-04, verbose = TRUE)
)

Arguments

control

optimization parameters (niter and threshold)

---

NormalBlockCollectionSparsity$get_model()

returns the NormalBlockVarKnownClusters model corresponding to given penalty

Usage

NormalBlockCollectionSparsity$get_model(sparsity)

Arguments

sparsity

sparsity penalty asked by user

Returns

A NormalBlockVarKnownClusters (sparse) object with given value penalty

---

NormalBlockCollectionSparsity$plot()

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

Usage

NormalBlockCollectionSparsity$plot(
  criteria = c("deviance", "BIC", "EBIC", "ICL"),
  log.x = TRUE
)

Arguments

criteria

vector of characters. The criteria to plot in `c("deviance", BIC", "EBIC", "ICL")`. Defaults to all of them.

log.x

logical: should the x-axis be represented in log-scale? Default is `TRUE`.

Returns

a [`ggplot2::ggplot`] graph

---

NormalBlockCollectionSparsity$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockCollectionSparsity$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# An internal abstract base class, never instantiated directly -- see
# normal_block() for how collections are created and fitted.