R6 class for Bipartite SBM

Super class

SBM -> BipartiteSBM

Active bindings

dimLabels: vector of two characters giving the label of each connected dimension (row, col)
blockProp: list of two vectors of block proportions (aka prior probabilities of each block)
connectParam: parameters associated to the connectivity of the SBM, e.g. matrix of inter/inter block probabilities when model is Bernoulli
probMemberships: matrix of estimated probabilities for block memberships for all nodes
nbBlocks: vector of size 2: number of blocks (rows, columns)
mask: Mask for the (potential) NAs in the adjacency matrix
nbDyads: number of dyads (potential edges in the network)
nbConnectParam: number of parameter used for the connectivity
memberships: list of size 2: vector of memberships in row, in column.
indMemberships: matrix for clustering memberships

Methods

Inherited methods

`BipartiteSBM$new()`

constructor for SBM

Usage

BipartiteSBM$new(
  model,
  nbNodes,
  blockProp = vector("list", 2),
  connectParam = list(mean = matrix(0, 0, 0)),
  dimLabels = c(row = "row", col = "col"),
  covarParam = numeric(length(covarList)),
  covarList = list(),
  nodesCovarList = vector("list", 2),
  nodesCovarParam = vector("list", 2)
)

Arguments

model: character describing the type of model
nbNodes: number of nodes in each dimension of the network
blockProp: parameters for block proportions (vector of list of vectors)
connectParam: list of parameters for connectivity with a matrix of means 'mean' and an optional scalar for the variance 'var'. The dimensions of mu must match blockProp lengths
dimLabels: optional labels of each dimension (in row, in column)
covarParam: optional vector of covariates effect
covarList: optional list of covariates data
nodesCovarList: optional list of two matrices with nodes covariates
nodesCovarParam: optional list of two matrices of parameters of effects of nodes covariates on clustering

`BipartiteSBM$rMemberships()`

a method to sample new block memberships for the current SBM

Usage

BipartiteSBM$rMemberships(store = FALSE)

Arguments

store: should the sampled blocks be stored (and overwrite the existing data)? Default to FALSE

Returns

the sampled blocks

`BipartiteSBM$rEdges()`

a method to sample a network data (edges) for the current SBM

Usage

BipartiteSBM$rEdges(store = FALSE)

Arguments

store: should the sampled edges be stored (and overwrite the existing data)? Default to FALSE

Returns

the sampled network

`BipartiteSBM$predict()`

prediction under the current parameters

Usage

BipartiteSBM$predict(covarList = self$covarList, theta_p0 = 0)

Arguments

covarList: a list of covariates. By default, we use the covariates with which the model was estimated.
theta_p0: double for thresholding...

`BipartiteSBM$show()`

show method

Usage

BipartiteSBM$show(type = "Bipartite Stochastic Block Model")

Arguments

type: character used to specify the type of SBM

`BipartiteSBM$plot()`

basic matrix plot method for BipartiteSBM object or mesoscopic plot

Usage

BipartiteSBM$plot(
  type = c("data", "expected", "graphon", "meso"),
  ordered = TRUE,
  plotOptions = list()
)

Arguments

type: character for the type of plot: either 'data' (true connection), 'expected' (fitted connection), 'graphon' (block connectivity on the unit square, blocks sized by the block proportions) or 'meso' (mesoscopic view). Default to 'data'.
ordered: logical: should the rows and columns be reordered according to the clustering? Default to TRUE.
plotOptions: list with the parameters for the plot. See help of the corresponding S3 method for details.

Returns

a ggplot2 object

`BipartiteSBM$clone()`

The objects of this class are cloneable with this method.

Usage

BipartiteSBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.