The method to plot the fitBipartite objects
fitBipartiteSBMPop.RdThe method to plot the fitBipartite objects
The method to plot the fitBipartite objects
Public fields
nA list with two dimensions, each of size M for the rows and cols
MNumber of networks
AList of incidence Matrix of size
n[[1]][m]xn[[2]][m]maskList of M masks, indicating NAs in the matrices. 1 for NA, 0 else
nonNAsList of M masks, indicating non NAs in the matrices. 1 - mask, so 0 for NA, 1 for non NA
nb_interA vector of length M the number of unique non NA entries
QNumber of clusters, vectors of size2
tauList of size M of list of two variational parameters.
n[[1]][m]xQmatrices andn[[2]][m]xQmatricesalphaMatrix of size QxQ, connection parameters
piList of M vectors of size Q, the mixture parameters
pimList of M vectors of size Q, the mixture parameters in case of free_mixture
eVector of size M, the sum of unique entries
emqrList of M QxQ matrix, the sum of edges between q and r in m, ie the edges that are observed.
nmqrlist of M QxQ matrix, the number of entries between q and r in m, ie all the possible edges.
alphamlist of M QxQ matrix, the classic sbm parameters.
free_mixture_rowA boolean indicating if there is a free mixture on the rows
free_mixture_colA boolean indicating if there is a free mixture on the columns
weightA vector of size M for weighted likelihood
distributionEmission distribution either : "poisson" or "bernoulli"
mlossLoss on the M step of the VEM
vlossLoss on the VE step of the VEM
vboundThe variational bound
entropyThe entropy of the variational distribution
net_idA vector containing the "ids" or names of the networks (if none given, they are set to their number in A list)
df_mixtureThe degrees of freedom for mixture parameters pi,used to compute penalty
df_connectThe degrees of freedom for connection parameters alpha,used to compute penalty
CpiA list of matrices of size Qd x M containing TRUE (1) or FALSE (0) if the d-th dimension cluster is represented in the network m
CalphaThe corresponding support on the connectivity parameters computed with Cpi.
logfactAA quantity used with the Poisson probability distribution
init_methodThe initialization method used for the first clustering
penaltyThe penalty computed based on the number of parameters
ZThe clusters memberships, a list of size M of two matrices : 1 for rows clusters memberships and 2 for columns clusters memberships
MAPMaximum a posteriori
MAP_parametersMAP params
ICLStores the ICL of the model
BICLStores the BICL of the model
fit_optsFit parameters, used to determine the fitting method/
step_counterCounts the number of passes
greedy_exploration_starting_pointStores the coordinates Q1 & Q2 from the greedy exploration to keep track of the starting_point
effective_clustering_listA list of size M storing the number of the clusters that contains at least one point. Used for safety checks.
clustering_is_completeA boolean used to know if the model real blocks match the expected blocks.
tested_tausA vector of taus values for taus given by init_clust
tested_taus_vboundA vector of vbound values for taus given by init_clust
has_convergedA boolean, indicating wether the current fit object VEM converged or not
Active bindings
nb_nodesReturns n a list of the number of nodes per network
nb_blocksReturns Q a vector with 2 coordinates, Q1 and Q2 for the row blocks and the column blocks
supportReturns the Cpi, a list of M boolean matrices indicating which blocks are populated
prob_membershipsReturns the tau, the probabilities of memberships "a posteriori", after seeing the data
parametersReturns the list of parameters of the model, alpha, pi and rho
pred_dyadsPredicted dyads from the estimated probabilities and parameters
membershipsThe block memberships
Methods
Method new()
Initializes the fitBipartiteSBMPop object
Usage
fitBipartiteSBMPop$new(
A = NULL,
Q = NULL,
Z = NULL,
mask = NULL,
net_id = NULL,
distribution = NULL,
free_mixture_row = TRUE,
free_mixture_col = TRUE,
Cpi = NULL,
Calpha = NULL,
init_method = "spectral",
weight = NULL,
greedy_exploration_starting_point = NULL,
fit_opts = list(algo_ve = "fp", minibatch = TRUE, verbosity = 1)
)Arguments
AList of incidence Matrix of size
n[[2]][m]xn[[2]][m]QA vector of size 2 with the number of row blocks and column blocks
ZThe clusters memberships, a list of size M of two matrices : 1 for rows clusters memberships and 2 for columns clusters memberships
maskList of M masks, indicating NAs in the matrices. 1 for NA, 0 else
net_idA vector containing the "ids" or names of the networks (if none given, they are set to their number in A list)
distributionEmission distribution either : "poisson" or "bernoulli"
free_mixture_rowA boolean indicating if there is a free mixture on the rows
free_mixture_colA boolean indicating if there is a free mixture on the columns
CpiA list of matrices of size Qd x M containing TRUE (1) or FALSE (0) if the d-th dimension cluster is represented in the network m
CalphaThe corresponding support on the connectivity parameters computed with Cpi.
init_methodThe initialization method used for the first clustering
weightA vector of size M for weighted likelihood
greedy_exploration_starting_pointStores the coordinates Q1 & Q2 from the greedy exploration to keep track of the starting_point
fit_optsFit parameters, used to determine the fitting method/ Method to compute the maximum a posteriori for Z clustering
Method vb_tau_alpha()
Method vb_tau_pi()
Method fixed_point_tau()
Not really a fixed point as tau^1 depends only tau^2.
Method update_pim()
Method update_alpham()
Method taus_order()
The goal of this function is to test different values of tau and select the best one in the sense of the BICL (or vbound) ?
Method m_step()
Method ve_step()
Method optimize()
Usage
fitBipartiteSBMPop$optimize(
max_step = self$fit_opts$max_vem_steps,
tol = self$fit_opts$tolerance,
...
)Method plot()
Usage
fitBipartiteSBMPop$plot(
type = "graphon",
oRow = NULL,
oCol = NULL,
mixture = FALSE,
net_id = NULL,
values = FALSE,
...
)Arguments
typeThe type of the plot. Could be "graphon", "meso" or "block".
oRowA reordering of the row blocks.
oColA reordering of the column blocks.
mixtureShould the block proportions of each network be plotted as well?
net_idUse to plot only one network in "graphon" view.
valuesWether or not to plot values on the alpha, pi and rho representation.
...Further argument to be passed