MLVSBM {MLVSBM} | R Documentation |
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
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, init = 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$estimate_all_bm( Q = NULL, Z = NULL, independent = FALSE, clear = TRUE, nb_cores = NULL )
Method new()
Constructor for R6 class MLVSBM
Usage
MLVSBM$new( n = NULL, X = NULL, A = NULL, Z = NULL, directed = NULL, sim_param = NULL, distribution = list("bernoulli", "bernoulli") )
Arguments
n
A list of size 2, the number of nodes
X
A list of 2 adjacency matrices
A
The affiliation matrix
Z
A list of 2 vectors, the blocks membership
directed
A list of 2 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
MLVSBM$findmodel(nb_clusters = NA, fit = NA)
Arguments
nb_clusters
A list of the size of the model
fit
if fit = "best" return the best model according to the ICL
Returns
A FitMLVSBM object
Method clearmodels()
delete all fitted models
Usage
MLVSBM$clearmodels()
Method addmodel()
Added a FitMLVSBM object to the list of fitted model
Usage
MLVSBM$addmodel(fit)
Arguments
fit
The FitMLVSBM object to be added
Method simulate()
Usage
MLVSBM$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.