MLVSBM: A R6Class for multilevel object

Description Active bindings Methods

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

Methods

Public methods


Method estimate_level()

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

Method estimate_sbm_neighbours()

Usage
MLVSBM$estimate_sbm_neighbours(
  level = "lower",
  Q = NULL,
  Q_min = 1,
  Q_max = 10,
  fit = NULL,
  nb_cores = NULL
)

Method estimate_sbm_from_neighbours()

Usage
MLVSBM$estimate_sbm_from_neighbours(
  level = "lower",
  Q = NULL,
  fits = NULL,
  nb_cores = NULL
)

Method estimate_sbm()

Usage
MLVSBM$estimate_sbm(level = "lower", Q = Q, Z = NULL, init = "hierarchical")

Method mcestimate()

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

Method estimate_from_neighbours()

Usage
MLVSBM$estimate_from_neighbours(
  Q,
  models = NULL,
  independent = FALSE,
  nb_cores = nb_cores
)

Method estimate_neighbours()

Usage
MLVSBM$estimate_neighbours(
  Q,
  fit = NULL,
  independent = independent,
  nb_cores = NULL
)

Method merge_split_membership()

Usage
MLVSBM$merge_split_membership(
  fitted = private$fitted[[length(private$fitted)]]
)

Method mc_ms_estimate()

Usage
MLVSBM$mc_ms_estimate(Z = NA, independent = FALSE, nb_cores = NULL)

Method estimate_one()

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

Method estimate_all_bm()

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

Method new()

Usage
MLVSBM$new(
  n = NULL,
  X = NULL,
  A = NULL,
  Z = NULL,
  directed = NULL,
  sim_param = NULL,
  distribution = list("bernoulli", "bernoulli")
)

Method findmodel()

Usage
MLVSBM$findmodel(nb_clusters = NA, fit = NA)

Method clearmodels()

Usage
MLVSBM$clearmodels()

Method addmodel()

Usage
MLVSBM$addmodel(fit)

Method simulate()

Usage
MLVSBM$simulate()

Method clone()

The objects of this class are cloneable with this method.

Usage
MLVSBM$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.


Chabert-Liddell/MLVSBM documentation built on Sept. 22, 2020, 3:38 p.m.