Control the model settings and various optimization parameters
Usage
NB_control(
niter = 100,
threshold = 1e-04,
sparsity_weights = NULL,
sparsity_penalties = NULL,
n_sparsity_penalties = 30,
min_ratio = 0.01,
fixed_tau = FALSE,
clustering_init = NULL,
verbose = TRUE,
heuristic = FALSE,
noise_covariance = c("diagonal", "spherical"),
clustering_approx = c("ward2", "kmeans", "sbm")
)Arguments
- niter
number of iterations in model optimization
- threshold
loglikelihood / elbo threshold under which optimization stops
- sparsity_weights
weights with which the penalty should be applied in case sparsity is required, non-0 values on the diagonal mean diagonal shall be penalized too (default is non-penalized diagonal and 1s off-diagonal)
- sparsity_penalties
list of penalties the user wants to test, other parameters are only used if penalties is not specified
- n_sparsity_penalties
number of penalties to test.
- min_ratio
ratio for sparsity between max penalty (0 edge penalty) and min penalty to test
- fixed_tau
whether tau should be fixed at clustering_init during optimization useful for calls to fixed_Q models in stability_selection
- clustering_init
proposal of initial value for clustering, when Q is unknown, can be a list with one clustering for each Q value
- verbose
telling if information should be printed during optimization
- heuristic
weather to use heuristic approach or not. Default is FALSE
- noise_covariance
variance can be variable specific ("diagonal", the default) or common ("spherical")
- clustering_approx
to use for clustering with heuristic inference method