R6 virtual class for SBM representation (mother class of SimpleSBM, BipartiteSBM, MultipartiteSBM)

Active bindings

modelName: character, the family of model for the distribution of the edges
directed: mode of the network data (directed or not or not applicable)
dimLabels: vector or list of characters, the label of each dimension
nbNodes: vector describing the number of the successive elements connecting the network
nbCovariates: integer, the number of covariates
connectParam: parameters associated to the connectivity of the SBM, e.g. matrix of inter/inter block probabilities when model is Bernoulli
covarParam: vector of regression parameters associated with the covariates.
covarList: list of matrices of covariates
covarArray: the array of covariates
covarEffect: effect of covariates
nbNodesCovariates: the dimension of the nodes covariates
nodesCovariates: the list of nodes covariates
nodesCovariatesParam: the list of nodes covariates parameters
networkData: the network data (adjacency or incidence matrix or list of such object)
expectation: expected values of connection under the current model

Methods

`SBM$new()`

constructor for SBM

Usage

SBM$new(
  model = vector("character", 0),
  directed = vector("logical", 0),
  dimension = vector("numeric", 0),
  dimLabels = vector("character", 0),
  blockProp = vector("numeric", 0),
  connectParam = vector("list", 0),
  covarParam = numeric(length(covarList)),
  covarList = list(),
  nodesCovarList = list(),
  nodesCovarParam = list()
)

Arguments

model: character describing the type of model
directed: logical describing if the network data is directed or not
dimension: dimension of the network data
dimLabels: labels of each dimension
blockProp: parameters for block proportions (vector or list of vectors)
connectParam: list of parameters for connectivity
covarParam: optional vector of covariates effect
covarList: optional list of covariates data
nodesCovarList: optional list of covariates on nodes (rows and cols),
nodesCovarParam: optional list of vector of node covariates effects on the clustering

`SBM$rNetwork()`

a method to sample a network data for the current SBM (blocks and edges)

Usage

SBM$rNetwork(store = FALSE)

Arguments

store: should the sampled network be stored (and overwrite the existing data)? Default to FALSE

Returns

a list with the sampled block and network

`SBM$show()`

print method

Usage

SBM$show(type = "Stochastic Block Model")

Arguments

type: character to tune the displayed name

`SBM$print()`

print method

Usage

SBM$print()

`SBM$clone()`

The objects of this class are cloneable with this method.

Usage

SBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.