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

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

blockProp

block proportions (aka prior probabilities of each block)

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

networkData

the network data (adjacency or incidence matrix or list of such object)

expectation

expected values of connection under the current model

Methods

Public methods


Method 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()
)

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


Method 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


Method show()

print method

Usage

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

Arguments

type

character to tune the displayed name


Method print()

print method

Usage

SBM$print()


Method clone()

The objects of this class are cloneable with this method.

Usage

SBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.