missSBM_fit function

An R6 class to represent an SBM fit with missing data

The function estimateMissSBM() fits a collection of SBM for varying number of block. Each fitted SBM is an instance of an R6 object with class missSBM_fit, described here.

Fields are accessed via active binding and cannot be changed by the user.

This class comes with a set of R6 methods, some of them being useful for the user and exported as S3 methods. See the documentation for show(), print(), fitted(), predict(), plot().

Examples

## Sample 75% of dyads in  French political Blogosphere's network data
adjMatrix <- missSBM::frenchblog2007 %>%
  igraph::as_adj (sparse = FALSE) %>%
  missSBM::observeNetwork(sampling = "dyad", parameters = 0.75)
collection <- estimateMissSBM(adjMatrix, 3:5, sampling = "dyad")
my_missSBM_fit <- collection$bestModel
class(my_missSBM_fit)
plot(my_missSBM_fit, "imputed")

Active bindings

• fittedSBM: the fitted SBM with class SimpleSBM_fit_noCov, SimpleSBM_fit_withCov or SimpleSBM_fit_MNAR inheriting from class sbm::SimpleSBM_fit

• fittedSampling: the fitted sampling, inheriting from class networkSampling and corresponding fits

• imputedNetwork: The network data as a matrix with NAs values imputed with the current model

• monitoring: a list carrying information about the optimization process

• entropyImputed: the entropy of the distribution of the imputed dyads

• entropy: the entropy due to the distribution of the imputed dyads and of the clustering

• vExpec: double: variational expectation of the complete log-likelihood

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

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

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

Methods

Public methods

• list("missSBM_fit$new()")

• list("missSBM_fit$doVEM()")

• list("missSBM_fit$show()")

• list("missSBM_fit$print()")

• list("missSBM_fit$clone()")

Method new()

constructor for networkSampling

Usage

missSBM_fit$new(partlyObservedNet, netSampling, clusterInit, useCov = TRUE)

Arguments

• partlyObservedNet: An object with class partlyObservedNetwork.

• netSampling: The sampling design for the modelling of missing data: MAR designs ("dyad", "node") and MNAR designs ("double-standard", "block-dyad", "block-node" ,"degree")

• clusterInit: Initial clustering: a vector with size ncol(adjacencyMatrix), providing a user-defined clustering. The number of blocks is deduced from the number of levels in with clusterInit.

• useCov: logical. If covariates are present in partlyObservedNet, should they be used for the inference or of the network sampling design, or just for the SBM inference? default is TRUE.

Method doVEM()

a method to perform inference of the current missSBM fit with variational EM

Usage

missSBM_fit$doVEM(
  control = list(threshold = 0.01, maxIter = 100, fixPointIter = 3, trace = TRUE)
)

Arguments

• control: a list of parameters controlling the variational EM algorithm. See details of function estimateMissSBM()

Method show()

show method for missSBM_fit

Usage

missSBM_fit$show()

Method print()

User friendly print method

Usage

missSBM_fit$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

missSBM_fit$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.