mimiSBM.Rd
mimiSBM is a model that allows both clustering of individuals and grouping of views by component. This Bayesian model estimates the probability of individuals belonging to each cluster (cluster crossing all views) and the membership component for all views. In addition, the connectivity tensor between classes, conditional on the components, is also estimated.
mimiSBM(
A,
Kset,
Qset,
beta_0 = 1/2,
theta_0 = 1/2,
eta_0 = 1/2,
xi_0 = 1/2,
criterion = "ILVB",
tol = 0.001,
iter_max = 10,
n_init = 1,
alternate = F,
Verbose = F,
eps_conv = 1e-04,
type_init = "SBM"
)
an array of dim=c(N,N,V)
Set of number of clusters
Set of number of components
hyperparameters for beta
hyperparameters for theta
hyperparameters for eta
hyperparameters for xi
model selection criterion, criterion=c("ILVB","ICL_approx","ICL_variationnel","ICL_exact")
convergence parameter on ELBO
maximal number of iteration of mimiSBM
number of initialization of the mimi algorithm.
boolean indicated if we put an M-step after each part of the E-step, after u optimization and after tau optimization. If not, we optimize u and tau and after the M-step is made.
boolean for information on model fitting
parameter of convergence for tau.
select the type of initialization type_init=c("SBM","Kmeans","random")
The best model, conditionnally to the criterion, and its parameters.
set.seed(42)
K = c(2,3); pi_k = rep(1/4,4) ; rho = rep(1/2,2)
res <- rSMB_partition(N = 50,V = 5,K = K ,pi_k = pi_k ,rho = rho,p_switch = 0.1)
A = res$simulation$A ; Kset = 4 ; Qset = 2
model <- mimiSBM(A,Kset,Qset,n_init = 1, Verbose=FALSE)
#> initialization of network1
#>
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#> initialization of network2
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#> initialization of network3
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#> initialization of network4
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#> initialization of network5
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#> Components verification