| SBM {sbm} | R Documentation |
R6 virtual class for SBM representation (mother class of SimpleSBM, BipartiteSBM, MultipartiteSBM)
Description
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
modelNamecharacter, the family of model for the distribution of the edges
directedmode of the network data (directed or not or not applicable)
dimLabelsvector or list of characters, the label of each dimension
nbNodesvector describing the number of the successive elements connecting the network
nbCovariatesinteger, the number of covariates
blockPropblock proportions (aka prior probabilities of each block)
connectParamparameters associated to the connectivity of the SBM, e.g. matrix of inter/inter block probabilities when model is Bernoulli
covarParamvector of regression parameters associated with the covariates.
covarListlist of matrices of covariates
covarArraythe array of covariates
covarEffecteffect of covariates
networkDatathe network data (adjacency or incidence matrix or list of such object)
expectationexpected values of connection under the current model
Methods
Public methods
Method new()
constructor for SBM
Usage
SBM$new(
model = vector("character", 0),
directed = vector("logical", 0),
dimension = vector("numeric", 0),
dimLabels = vector("character", 0),
blockProp = vector("numeric", 0),
connectParam = vector("list", 0),
covarParam = numeric(length(covarList)),
covarList = list()
)Arguments
modelcharacter describing the type of model
directedlogical describing if the network data is directed or not
dimensiondimension of the network data
dimLabelslabels of each dimension
blockPropparameters for block proportions (vector or list of vectors)
connectParamlist of parameters for connectivity
covarParamoptional vector of covariates effect
covarListoptional list of covariates data
Method rNetwork()
a method to sample a network data for the current SBM (blocks and edges)
Usage
SBM$rNetwork(store = FALSE)
Arguments
storeshould 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$show(type = "Stochastic Block Model")
Arguments
typecharacter to tune the displayed name
Method print()
print method
Usage
SBM$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
SBM$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.