SimpleSBM_fit {missSBM} | R Documentation |
This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.
Description
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
Inherited methods
Method new()
constructor for simpleSBM_fit for missSBM purpose
Usage
SimpleSBM_fit$new(networkData, clusterInit, covarList = list())
Arguments
networkData
a structure to store network under missing data condition: either a matrix possibly with NA, or a missSBM:::partlyObservedNetwork
clusterInit
Initial clustering: a vector with size
ncol(adjacencyMatrix)
, providing a user-defined clustering withnbBlocks
levels.covarList
An optional list with M entries (the M covariates).
Method doVEM()
method to perform estimation via variational EM
Usage
SimpleSBM_fit$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.