SimpleSBM_fit function

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.

Details

It is not designed not be call by the user

Super classes

sbm::SBM -> sbm::SimpleSBM -> SimpleSBM_fit

Active bindings

• type: the type of SBM (distribution of edges values, network type, presence of covariates)

• penalty: double, value of the penalty term in ICL

• entropy: double, value of the entropy due to the clustering distribution

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

• ICL: double: value of the integrated classification log-likelihood

Methods

Public methods

• list("SimpleSBM_fit$new()")

• list("SimpleSBM_fit$doVEM()")

• list("SimpleSBM_fit$reorder()")

• list("SimpleSBM_fit$clone()")

Method new()

constructor for simpleSBM_fit for missSBM purpose

Usage

SimpleSBM_fit$new(networkData, clusterInit, covarList = list())

Arguments

• networkData: a structure to store network under missing data condition: either a matrix possibly with NA, or a missSBM:::partlyObservedNetwork

• clusterInit: Initial clustering: a vector with size ncol(adjacencyMatrix), providing a user-defined clustering with nbBlocks levels.

• covarList: An optional list with M entries (the M covariates).

Method doVEM()

method to perform estimation via variational EM

Usage

SimpleSBM_fit$doVEM(
  threshold = 0.01,
  maxIter = 100,
  fixPointIter = 3,
  trace = FALSE
)

Arguments

• threshold: stop when an optimization step changes the objective function by less than threshold. Default is 1e-4.

• maxIter: V-EM algorithm stops when the number of iteration exceeds maxIter. Default is 10

• fixPointIter: number of fix-point iterations in the Variational E step. Default is 5.

• trace: logical for verbosity. Default is FALSE.

Method reorder()

permute group labels by order of decreasing probability

Usage

SimpleSBM_fit$reorder()

Method 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.