R6Class for multilevel object

Store all simulation parameters and list of fittedmodels. Methods for global inference and model selection are included.

Active bindings

nb_nodes

List of the umber of nodes for each levels

simulation_parameters

List of parameters of the MLVSBM

affiliation_matrix

Access the affiliation matrix

adjacency_matrix

Access the list of adjacency_matrix

memberships

Access the list of the clusterings

fittedmodels

Get the list of selected fitted FitMLVSBM objects

ICL

A summary table of selected fitted models and ICL model selection criterion

ICL_sbm

Summary table of ICL by levels

tmp_fittedmodels

A list of all fitted FitMLVSBM objects

fittedmodels_sbm

A list of selected fitted FitSBM objects of each levels

max_clusters

Access the list of maximum model size

min_clusters

Access the list of minimum model size

directed

Access the list of boolean for levels direction

directed

Access the list of the distribution used for each levels

nb_levels

Access the number of levels in the network

Methods

Public methods

• GenMLVSBM$estimate_level()

• GenMLVSBM$estimate_sbm_neighbours()

• GenMLVSBM$estimate_sbm_from_neighbours()

• GenMLVSBM$estimate_sbm()

• GenMLVSBM$mcestimate()

• GenMLVSBM$estimate_neighbours()

• GenMLVSBM$estimate_one()

• GenMLVSBM$estimate_all_bm()

• GenMLVSBM$new()

• GenMLVSBM$findmodel()

• GenMLVSBM$clearmodels()

• GenMLVSBM$addmodel()

• GenMLVSBM$simulate()

• GenMLVSBM$clone()

---

Method estimate_level()

Usage

GenMLVSBM$estimate_level(
  level = NULL,
  Q_min = 1,
  Q_max = 10,
  Z = NULL,
  init = "hierarchical",
  depth = 1,
  nb_cores = NULL
)

---

Method estimate_sbm_neighbours()

Usage

GenMLVSBM$estimate_sbm_neighbours(
  level = NULL,
  Q = NULL,
  Q_min = 1,
  Q_max = 10,
  fit = NULL,
  nb_cores = NULL,
  init = NULL
)

---

Method estimate_sbm_from_neighbours()

Usage

GenMLVSBM$estimate_sbm_from_neighbours(
  level = NULL,
  Q = NULL,
  fits = NULL,
  nb_cores = NULL
)

---

Method estimate_sbm()

Usage

GenMLVSBM$estimate_sbm(level = NULL, Q = Q, Z = NULL, init = "hierarchical")

---

Method mcestimate()

Usage

GenMLVSBM$mcestimate(Q, Z = NULL, init = "hierarchical", independent = FALSE)

---

Method estimate_neighbours()

Usage

GenMLVSBM$estimate_neighbours(
  level,
  fit = NULL,
  Q,
  independent = independent,
  nb_cores = NULL
)

---

Method estimate_one()

Usage

GenMLVSBM$estimate_one(
  Q,
  Z = NULL,
  independent = FALSE,
  init = "hierarchical",
  nb_cores = NULL
)

---

Method estimate_all_bm()

Usage

GenMLVSBM$estimate_all_bm(
  Q = NULL,
  Z = NULL,
  independent = FALSE,
  clear = TRUE,
  nb_cores = NULL
)

---

Method new()

Constructor for R6 class MLVSBM

Usage

GenMLVSBM$new(
  n = NULL,
  X = NULL,
  A = NULL,
  L = NULL,
  Z = NULL,
  directed = NULL,
  sim_param = NULL,
  distribution = NULL
)

Arguments

n

A list of size 2, the number of nodes

X

A list of L adjacency matrices

A

A list of L-1 affiliation matrices

Z

A list of L vectors, the blocks membership

directed

A vector of L booleans

sim_param

A list of MLVSBM parameters for simulating networks

distribution

The distributions of the interactions ("bernoulli")

Returns

A MLVSBM object

---

Method findmodel()

Find a fitted model of a given size

Usage

GenMLVSBM$findmodel(nb_clusters = NA, fit = NA)

Arguments

nb_clusters

A list of the size of the model

fit

if fit = "best" return the best model according to the ICL

Returns

A FitMLVSBM object

---

Method clearmodels()

delete all fitted models

Usage

GenMLVSBM$clearmodels()

---

Method addmodel()

Added a FitMLVSBM object to the list of fitted model

Usage

GenMLVSBM$addmodel(fit)

Arguments

fit

The FitMLVSBM object to be added

---

Method simulate()

Usage

GenMLVSBM$simulate()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

GenMLVSBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.