R6 class for Simple SBM

Super class

SBM -> SimpleSBM

Active bindings

dimLabels: a single character giving the label of the nodes
blockProp: vector 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
mask: Mask for the (potential) NAs in the adjacency matrix
nbBlocks: number of blocks
nbDyads: number of dyads (potential edges in the network)
nbConnectParam: number of parameter used for the connectivity
nodesCovariates: matrix of covariates on nodes
nodesCovariatesParam: the matrix of nodes covariates parameters
nbNodesCovariates: number of covariates on nodes
memberships: vector of clustering
indMemberships: matrix for clustering memberships

Methods

Inherited methods

`SimpleSBM$new()`

constructor for SBM

Usage

SimpleSBM$new(
  model,
  nbNodes,
  directed,
  blockProp,
  connectParam,
  dimLabels = c("node"),
  covarParam = numeric(length(covarList)),
  covarList = list(),
  nodesCovarList = list(node = matrix(0, 0, 0)),
  nodesCovarParam = list(node = matrix(0, 0, 0))
)

Arguments

model: character describing the type of model
nbNodes: number of nodes in the network
directed: logical, directed network or not.
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 size of mu must match blockProp length
dimLabels: optional label for the node (default is "nodeName")
covarParam: optional vector of covariates effect
covarList: optional list of covariates data
nodesCovarList: optional list of one matrix with nodes covariates
nodesCovarParam: optional list of one matrix of parameters of effects of nodes covariates on clustering

`SimpleSBM$rMemberships()`

a method to sample new block memberships for the current SBM

Usage

SimpleSBM$rMemberships(store = FALSE)

Arguments

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

Returns

the sampled blocks

`SimpleSBM$rEdges()`

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

Usage

SimpleSBM$rEdges(store = FALSE)

Arguments

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

Returns

the sampled network

`SimpleSBM$predict()`

prediction under the currently parameters

Usage

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

Arguments

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

Returns

a matrix of expected values for each dyad

`SimpleSBM$show()`

show method

Usage

SimpleSBM$show(type = "Simple Stochastic Block Model")

Arguments

type: character used to specify the type of SBM

`SimpleSBM$plot()`

basic matrix plot method for SimpleSBM object or mesoscopic plot

Usage

SimpleSBM$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

`SimpleSBM$clone()`

The objects of this class are cloneable with this method.

Usage

SimpleSBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.