GenMLVSBM: R6Class for multilevel object

In Chabert-Liddell/MLVSBM: A Stochastic Block Model for Multilevel Networks

R6Class for multilevel object

Description

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.

Chabert-Liddell/MLVSBM documentation built on March 25, 2023, 11:45 a.m.