An R6 Class object, a collection of model for population of sbm netowrks

Public fields

n: A list of size M with the number of nodes per network
A: List of incidence Matrix of size n \times n
M: Number of networks
mask: List of M masks, indicating NAs in the matrices. 1 for NA, 0 else
directed: A boolean indicating if the networks are directed or not
distribution: Emission distribution either : "poisson" or "bernoulli"
net_id: A vector containing the "ids" or names of the networks (if none given, they are set to their number in A list)
model_list: A list of size Q containing the best models
global_opts: A list of options for the model space exploration. See details for more information on which options are available.
fit_opts: A list of options specifically for fitting the models.
fit_sbm: Pre-fitted sbm objects
Z_init: A list of initializations for the Z memberships.
free_density: A boolean indicating if we consider free density or if all networks have the same density
free_mixture: A boolean signaling if there is free mixture
ICL_sbm: A list storing the ICL for each sbm fitted
ICL: A list of size Q storing the best ICL found for each Q
BICL: A list of size Q storing the best BICL found for each Q
vbound: A list of size Q storing the best vbound found for each Q
best_fit: A fitSimpleSBMPop object changing regularly to store the current best fit.
logfactA: A quantity used with the Poisson probability distribution
improved: A field use at each step to check if it has improved.

Methods

`bmpop$new()`

Create a new instance of the bisbmpop object

This class is generally called via the user function estimate_colBiSBM

Usage

bmpop$new(
  netlist = NULL,
  net_id = NULL,
  directed = NULL,
  distribution = "bernoulli",
  free_density = FALSE,
  free_mixture = FALSE,
  fit_sbm = NULL,
  Z_init = NULL,
  global_opts = list(),
  fit_opts = list()
)

Arguments

netlist: The list of M networks
net_id: A list of name for the networks, defaults to 1 to M if not provided
directed: A boolean indicating if the networks are directed or not
distribution: The emission distribution either "bernoulli" or "poisson"
free_density: If we account for different density between networks
free_mixture: A boolean indicating if there is free mixture
fit_sbm: The pre-fitted SBM
Z_init: A list providing a clustering of the nodes
global_opts: A list of global options used by the algorithm. See details of the user function for more information.
fit_opts: A list of fit options used by the algorithm. See details of the user function for more information.

Returns

A new 'sbmpop' object. Fit a list of SBM if fit_sbm == TRUE

`bmpop$optimize_sbm()`

Usage

bmpop$optimize_sbm()

Returns

nothing; but stores the values Fit the colSBM mode using sbm as initializations

`bmpop$optimize_from_sbm()`

Usage

bmpop$optimize_from_sbm(index, Q, nb_clusters)

Arguments

index: The sequence of networks number going from 1 to M
Q: The number of clusters
nb_clusters: A subindex for model list, in practice always 1

Returns

bmpop object Fit the colSBM mode using spectral decompositions as initializations

`bmpop$optimize_spectral()`

Usage

bmpop$optimize_spectral(index, Q, nb_clusters)

Arguments

index: The sequence of networks number going from 1 to M
Q: The number of clusters
nb_clusters: A subindex for model list, in practice always 1

Returns

bmpop object Fit a colSBM model using a given initialization provided with Z for a specific network

`bmpop$optimize_init()`

Usage

bmpop$optimize_init(index, Z, Q, nb_clusters, Cpi = NULL, Calpha = NULL)

Arguments

index: The sequence of networks number going from 1 to M
Z: The provided initialization
Q: The number of clusters
nb_clusters: A subindex for model list, in practice always 1
Cpi: A list of size M containing the support for the possibly absent blocks, defaults to NULL
Calpha: A list of size M containing the support for the possibly absent interaction parameter, defaults to NULL

Returns

bmpop object Fit the whole colSBM mode using a given initialization provided with a given Z_init

`bmpop$optimize_from_zinit()`

Usage

bmpop$optimize_from_zinit(index, Q, nb_clusters)

Arguments

index: The sequence of networks number going from 1 to M
Q: The number of clusters
nb_clusters: A subindex for model list, in practice always 1

Returns

bmpop object Burn-in method that performs the initialization necessary to next begin the search and model selection

`bmpop$burn_in()`

Usage

bmpop$burn_in()

Returns

nothing; but stores the values This forward pass split the clusters found previously and reallocate the nodes to the new clusters

`bmpop$forward_pass()`

Usage

bmpop$forward_pass(
  Q_min = self$global_opts$Q_min,
  Q_max = self$global_opts$Q_max,
  index = seq(self$M),
  nb_clusters = 1L
)

Arguments

Q_min: The minimal number of clusters, defaults to the global options Q_min parameter
Q_max: The max number of clusters, defaults to the global options Q_max parameter
index: The sequence of networks number going from 1 to M
nb_clusters: A subindex for model list, in practice always 1

Returns

Q - 1 This backward pass merges the clusters found previously

`bmpop$backward_pass()`

Usage

bmpop$backward_pass(
  Q_min = self$global_opts$Q_min,
  Q_max = self$global_opts$Q_max,
  index = seq(self$M),
  nb_clusters = 1L
)

Arguments

Q_min: The minimal number of clusters, defaults to the global options Q_min parameter
Q_max: The max number of clusters, defaults to the global options Q_max parameter
index: The sequence of networks number going from 1 to M
nb_clusters: A subindex for model list, in practice always 1

Returns

Q + 1 The optimization method

`bmpop$optimize()`

This method performs the burn in and the steps of moving window with cluster splitting (forward pass) and merging (backward pass) around the mode found.

Usage

bmpop$optimize()

Returns

nothing; but stores the values Performs the model selection based on the BICL criterion

`bmpop$choose_models()`

Usage

bmpop$choose_models(models, Q, index = seq(self$M), nb_clusters = 1L)

Arguments

models: The list of models in which to choose the best
Q: The value of Q for which the model selection is performed
index: The sequence of networks number going from 1 to M
nb_clusters: A subindex for model list, in practice always 1

Returns

The best models The message printed when one prints the object

`bmpop$show()`

Usage

bmpop$show(type = "Fitted Collection of Simple SBM")

Arguments

type: The title above the message. The print method

`bmpop$print()`

Usage

bmpop$print()

Returns

nothing; print to console

`bmpop$clone()`

The objects of this class are cloneable with this method.

Usage

bmpop$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.