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R6 Class definition of a Simple SBM fit

Source: R/R6Class-SimpleSBM_fit.R
SimpleSBM_fit.Rd

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

Details

The list of parameters estimOptions essentially tunes the optimization process and the variational EM algorithm, with the following parameters

  • "nbCores integer for number of cores used. Default is 2 (1 under Positron, which does not allow forking)

  • "verbosity" integer for verbosity (0, 1). Default is 1

  • "plot" boolean, should the ICL be dynamically plotted or not. Default is TRUE, FALSE when "verbosity" is 0

  • "exploreFactor" control the exploration of the number of groups

  • "exploreMin" explore at least until exploreMin even if the exploration factor rule is achieved. Default 4. See the package blockmodels for details.

  • "exploreMax" Stop exploration at exploreMax even if the exploration factor rule is not achieved. Default Inf. See the package blockmodels for details.

  • "nbBlocksRange" minimal and maximal number or blocks explored

  • "fast" logical: should approximation be used for Bernoulli model with covariates. Default to TRUE

  • "explorationDirection" (only used for BipartiteSBM) a vector of two integers which controls the exploration direction for groups number. When provided, the exploring strategy is made to explore the provided group number. The two integer value representing the row group number and the column group number.

Super classes

SBM -> SimpleSBM -> SimpleSBM_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

SimpleSBM_fit$new()

constructor for a Simple SBM fit

Usage

SimpleSBM_fit$new(
  adjacencyMatrix,
  model,
  directed,
  dimLabels = c(node = "nodeName"),
  covarList = list(),
  nodesCovarList = list(matrix(nrow = 0, ncol = 0))
)

Arguments

adjacencyMatrix

square (weighted) matrix

model

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

directed

logical, directed network or not. In not, adjacencyMatrix must be symmetric.

dimLabels

list of labels of each dimension (in row, in columns)

covarList

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

nodesCovarList

optional list of one matrix with nodes covariates

SimpleSBM_fit$optimize()

function to perform optimization

Usage

SimpleSBM_fit$optimize(estimOptions = list())

Arguments

estimOptions

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

SimpleSBM_fit$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

SimpleSBM_fit$setModel(index)

Arguments

index

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

SimpleSBM_fit$reorder()

permute group labels by order of decreasing probability

Usage

SimpleSBM_fit$reorder()

SimpleSBM_fit$show()

show method

Usage

SimpleSBM_fit$show(type = "Fit of a Simple Stochastic Block Model")

Arguments

type

character used to specify the type of SBM

SimpleSBM_fit$clone()

The objects of this class are cloneable with this method.

Usage

SimpleSBM_fit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.