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Collection of Normal-Block Models over a Sparsity Path

Source: R/NormalBlockVarCollectionSparsity.R
NormalBlockVarCollectionSparsity.Rd

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

Super class

NormalBlockVarCollection -> NormalBlockVarCollectionSparsity

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

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

Inherited methods

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$optimize()

optimizes every model in the sparsity path, warm-starting each one (after the first) from the previous, adjacent penalty's converged parameters (see [NormalBlockVarBase]'s `warm_start_from()`) instead of re-deriving everything from the heuristic clustering, the way the generic [NormalBlockVarCollection] `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

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

Arguments

control

optimization parameters (niter and threshold)

NormalBlockVarCollectionSparsity$get_model()

returns the NormalBlockVarKnownClusters model corresponding to given penalty

Usage

NormalBlockVarCollectionSparsity$get_model(sparsity)

Arguments

sparsity

sparsity penalty asked by user

Returns

A NormalBlockVarKnownClusters (sparse) object with given value penalty

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$plot()

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

Usage

NormalBlockVarCollectionSparsity$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 [`ggplot`] graph

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.