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

• BipartiteSBM$new()

• BipartiteSBM$rMemberships()

• BipartiteSBM$rEdges()

• BipartiteSBM$predict()

• BipartiteSBM$show()

• BipartiteSBM$plot()

• BipartiteSBM$clone()

Inherited methods

• sbm::SBM$print()
• sbm::SBM$rNetwork()

---

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.