BayesianMixture_SBM_model {mimiSBM}

mimiSBM model for fixed K and Q

Description

mimiSBM model for fixed K and Q

Usage

BayesianMixture_SBM_model(
  A,
  K,
  Q,
  beta_0 = rep(1/2, K),
  theta_0 = rep(1/2, Q),
  eta_0 = array(rep(1/2, K * K * Q), c(K, K, Q)),
  xi_0 = array(rep(1/2, K * K * Q), c(K, K, Q)),
  tol = 0.001,
  iter_max = 10,
  n_init = 1,
  alternate = TRUE,
  Verbose = TRUE,
  eps_conv = 1e-04,
  type_init = "SBM",
  nbCores = 2
)

Arguments

A

an array of dim=c(N,N,V)

K

number of clusters

Q

number of components

beta_0

hyperparameters for beta

theta_0

hyperparameters for theta

eta_0

hyperparameters for eta

xi_0

hyperparameters for xi

tol

convergence parameter on ELBO

iter_max

maximal number of iteration of mimiSBM

n_init

number of initialization of the mimi algorithm.

alternate

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.

Verbose

boolean for information on model fitting

eps_conv

parameter of convergence for tau.

type_init

select the type of initialization type_init=c("SBM","Kmeans","random")

nbCores

the number of cores used to parallelize the calculations

See the vignette for more details.

Value

model with estimation of coefficients.