BayesianMixture_SBM_model {mimiSBM} | R Documentation |
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.