BipartiteSBM_fit {sbm}R Documentation

R6 Class definition of an Bipartite SBM fit

Description

R6 Class definition of an Bipartite SBM fit

R6 Class definition of an Bipartite SBM fit

Details

This class is designed to give a representation and adjust an LBM fitted with blockmodels.

Super classes

sbm::SBM -> sbm::BipartiteSBM -> BipartiteSBM_fit

Active bindings

loglik

double: approximation of the log-likelihood (variational lower bound) reached

ICL

double: value of the integrated classification log-likelihood

penalty

double, value of the penalty term in ICL

entropy

double, value of the entropy due to the clustering distribution

storedModels

data.frame of all models fitted (and stored) during the optimization

Methods

Public methods

Inherited methods

Method new()

constructor for a Bipartite SBM fit

Usage
BipartiteSBM_fit$new(
  incidenceMatrix,
  model,
  dimLabels = c(row = "row", col = "col"),
  covarList = list()
)
Arguments
incidenceMatrix

rectangular (weighted) matrix

model

character ('bernoulli', 'poisson', 'gaussian')

dimLabels

labels of each dimension (in row, in columns)

covarList

and optional list of covariates, each of whom must have the same dimension as incidenceMatrix


Method optimize()

function to perform optimization

Usage
BipartiteSBM_fit$optimize(estimOptions = list())
Arguments
estimOptions

a list of parameters controlling the inference algorithm and model selection. See details.


Method setModel()

method to select a specific model among the ones fitted during the optimization. Fields of the current SBM_fit will be updated accordingly.

Usage
BipartiteSBM_fit$setModel(index)
Arguments
index

integer, the index of the model to be selected (row number in storedModels)


Method reorder()

permute group labels by order of decreasing probability

Usage
BipartiteSBM_fit$reorder()

Method show()

show method

Usage
BipartiteSBM_fit$show(type = "Fit of a Bipartite Stochastic Block Model")
Arguments
type

character used to specify the type of SBM


Method clone()

The objects of this class are cloneable with this method.

Usage
BipartiteSBM_fit$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.


[Package sbm version 0.4.6 Index]