MLVSBM | R Documentation |
Store all simulation parameters and list of fittedmodels. Methods for global inference and model selection are included.
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
estimate_level()
MLVSBM$estimate_level( level = "lower", Q_min = 1, Q_max = 10, Z = NULL, init = "hierarchical", depth = 1, nb_cores = NULL )
estimate_sbm_neighbours()
MLVSBM$estimate_sbm_neighbours( level = "lower", Q = NULL, Q_min = 1, Q_max = 10, fit = NULL, nb_cores = NULL, init = NULL )
estimate_sbm_from_neighbours()
MLVSBM$estimate_sbm_from_neighbours( level = "lower", Q = NULL, fits = NULL, nb_cores = NULL )
estimate_sbm()
MLVSBM$estimate_sbm(level = "lower", Q = Q, Z = NULL, init = "hierarchical")
mcestimate()
MLVSBM$mcestimate(Q, Z = NULL, init = "hierarchical", independent = FALSE)
estimate_from_neighbours()
MLVSBM$estimate_from_neighbours( Q, models = NULL, independent = FALSE, nb_cores = nb_cores )
estimate_neighbours()
MLVSBM$estimate_neighbours( Q, fit = NULL, independent = independent, nb_cores = NULL )
merge_split_membership()
MLVSBM$merge_split_membership( fitted = private$fitted[[length(private$fitted)]] )
mc_ms_estimate()
MLVSBM$mc_ms_estimate(Z = NA, independent = FALSE, nb_cores = NULL)
estimate_one()
MLVSBM$estimate_one( Q, Z = NULL, independent = FALSE, init = "hierarchical", nb_cores = NULL )
estimate_all_bm()
MLVSBM$estimate_all_bm( Q = NULL, Z = NULL, independent = FALSE, clear = TRUE, nb_cores = NULL )
new()
Constructor for R6 class MLVSBM
MLVSBM$new( n = NULL, X = NULL, A = NULL, Z = NULL, directed = NULL, sim_param = NULL, distribution = list("bernoulli", "bernoulli") )
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")
A MLVSBM object
findmodel()
Find a fitted model of a given size
MLVSBM$findmodel(nb_clusters = NA, fit = NA)
nb_clusters
A list of the size of the model
fit
if fit = "best" return the best model according to the ICL
A FitMLVSBM object
clearmodels()
delete all fitted models
MLVSBM$clearmodels()
addmodel()
Added a FitMLVSBM object to the list of fitted model
MLVSBM$addmodel(fit)
fit
The FitMLVSBM object to be added
simulate()
MLVSBM$simulate()
clone()
The objects of this class are cloneable with this method.
MLVSBM$clone(deep = FALSE)
deep
Whether to make a deep clone.
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