FitSBM {MLVSBM} | R Documentation |
An R6 Class object for unilevel network
Description
a fitted level of a unilevel network once $do_vem() is done
Public fields
vbound
vector of variational bound for convergence monitoring
Active bindings
adjacency
Get the adjacency matrix
mask
Get the mask matrix for dealing with NA
nb_nodes
Get the number of nodes of the level
nb_clusters
Get the number of blocks
distribution
Get the distribution used for the connections
directed
Get if the level is directed or not
mixture_parameter
Access the block proportions
connectivity_parameter
Access the connectivity matrix
membership
Access the variational parameters
entropy
Get the entropy of the model
bound
Get the variational bound of the model
df_mixture
Get the degree of freedom of the block proportion
df_connect
Get the degree of freedom of the connection parameters
connect
Get the number of observed dyads
ICL
Get the ICL model selection criterion
penalty
Get the penalty used for computing the ICL
Z
Access the vector of block membership (clustering)
X_hat
Get the connection probability matrix
X_likelihood
adjacency part of the log likelihood
Z_likelihood
block part of the log likelihood
likelihood
complete log likelihood
Methods
Public methods
Method new()
Constructor for FitSBM R6 class
Usage
FitSBM$new( Q = 1, X = NULL, M = NULL, directed = FALSE, distribution = "bernoulli" )
Arguments
Q
Number of blocks
X
Adjacency matrix
M
Mask matrix
directed
boolean
distribution
string (only "bernoulli")
Returns
A new FitSBM object
Method update_alpha()
Update the connection parameter for the M step
Usage
FitSBM$update_alpha(safeguard = 1e-06)
Arguments
safeguard
Parameter live in a compact [safeguard, 1-safeguard]
Method update_pi()
Update the upper level mixture parameter for the M step
Usage
FitSBM$update_pi(safeguard = 1e-06)
Arguments
safeguard
Parameter live in a compact [safeguard, 1-safeguard]
Method init_clustering()
init_clustering Initial clustering for VEM algorithm
Usage
FitSBM$init_clustering(safeguard = 1e-06, method = "hierarchical", Z = NULL)
Arguments
safeguard
Parameter live in a compact [safeguard, 1-safeguard]
method
Algorithm used to initiate the clustering, either "spectral", "hierarchical" or "merge_split" (if
Z
is provided)Z
Initial clustering if provided
Method m_step()
m_step Compute the M step of the VEM algorithm
Usage
FitSBM$m_step(safeguard = 1e-06)
Arguments
safeguard
Parameter live in a compact [safeguard, 1-safeguard]
Method ve_step()
Compute the VE step of the VEM algorithm
Usage
FitSBM$ve_step(threshold = 1e-06, fixPointIter = 100, safeguard = 1e-06)
Arguments
threshold
The convergence threshold
fixPointIter
The maximum number of fixed point iterations
safeguard
Parameter live in a compact [safeguard, 1-safeguard]
Method do_vem()
Launch a Variational EM algorithm
Usage
FitSBM$do_vem( init = "hierarchical", threshold = 1e-06, maxIter = 1000, fixPointIter = 100, safeguard = 1e-06, Z = NULL )
Arguments
init
The method for
self$init_clustering
threshold
The convergence threshold
maxIter
The max number of VEM iterations
fixPointIter
The max number of fixed point iterations for VE step
safeguard
Parameter live in a compact [safeguard, 1-safeguard]
Z
Initial clustering if provided
Method permute_empty_class()
permute_empty_class Put empty blocks numbers at the end
Usage
FitSBM$permute_empty_class()
Method clear()
Reset all parameters
Usage
FitSBM$clear()
Method clone()
The objects of this class are cloneable with this method.
Usage
FitSBM$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.