BipartiteSBM function

R6 class for Bipartite SBM

R6 class for Bipartite SBM

R6 class for Bipartite SBM

Super class

sbm::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)

• 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

Public methods

• list("BipartiteSBM$new()")

• list("BipartiteSBM$rMemberships()")

• list("BipartiteSBM$rEdges()")

• list("BipartiteSBM$predict()")

• list("BipartiteSBM$show()")

• list("BipartiteSBM$plot()")

• list("BipartiteSBM$clone()")

Method new()

constructor for SBM

Usage

BipartiteSBM$new(
  model,
  nbNodes,
  blockProp,
  connectParam,
  dimLabels = c(row = "row", col = "col"),
  covarParam = numeric(length(covarList)),
  covarList = list()
)

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

Method 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

Method 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

Method 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...

Method show()

show method

Usage

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

Arguments

• type: character used to specify the type of SBM

Method plot()

basic matrix plot method for BipartiteSBM object or mesoscopic plot

Usage

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

Arguments

• type: character for the type of plot: either 'data' (true connection), 'expected' (fitted connection) 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 for the 'data' and 'expected', a list with the igraph object g, the layout and the plotOptions for the 'meso'

Method clone()

The objects of this class are cloneable with this method.

Usage

BipartiteSBM$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.