missSBM_fit | R Documentation |
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()
.
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
new()
constructor for networkSampling
missSBM_fit$new(partlyObservedNet, netSampling, clusterInit, useCov = TRUE)
partlyObservedNet
An object with class partlyObservedNetwork
.
netSampling
The sampling design for the modelling of missing data: MAR designs ("dyad", "node") and MNAR designs ("double-standard", "block-dyad", "block-node" ,"degree")
clusterInit
Initial clustering: a vector with size ncol(adjacencyMatrix)
, providing a user-defined clustering. The number of blocks is deduced from the number of levels in with clusterInit
.
useCov
logical. If covariates are present in partlyObservedNet, should they be used for the inference or of the network sampling design, or just for the SBM inference? default is TRUE.
doVEM()
a method to perform inference of the current missSBM fit with variational EM
missSBM_fit$doVEM( control = list(threshold = 0.01, maxIter = 100, fixPointIter = 3, trace = TRUE) )
control
a list of parameters controlling the variational EM algorithm. See details of function estimateMissSBM()
show()
show method for missSBM_fit
missSBM_fit$show()
print()
User friendly print method
missSBM_fit$print()
clone()
The objects of this class are cloneable with this method.
missSBM_fit$clone(deep = FALSE)
deep
Whether to make a deep clone.
## Sample 75% of dyads in French political Blogosphere's network data
adjMatrix <- missSBM::frenchblog2007 %>%
igraph::as_adj (sparse = FALSE) %>%
missSBM::observeNetwork(sampling = "dyad", parameters = 0.75)
collection <- estimateMissSBM(adjMatrix, 3:5, sampling = "dyad")
my_missSBM_fit <- collection$bestModel
class(my_missSBM_fit)
plot(my_missSBM_fit, "imputed")
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