# sampleMultiplexSBM: Sampling of Multiplex SBMs In GrossSBM/sbm: Stochastic Blockmodels

## Description

This function samples a Multiplex Stochastic Block Models, with various model for the distribution of the edges: Bernoulli, Poisson, or Gaussian models

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```sampleMultiplexSBM( nbNodes, blockProp, nbLayers, connectParam, model, type = c("directed", "undirected", "bipartite"), dependent = FALSE, dimLabels = NULL, seed = NULL ) ```

## Arguments

 `nbNodes` number of nodes in each functional group involved in the Multiplex network `blockProp` a vector for block proportion if the networks are simple, a list of parameters for block proportions for both functional groups if the networks are bipartite `nbLayers` a matrix with two columns and nbNetworks lines, each line specifying the index of the functional groups in interaction. `connectParam` list of parameters for connectivity (of length nbNetworks). Each element is a list of one or two elements: a matrix of means 'mean' and an optional matrix of variances 'var', the sizes of which must match `blockProp` length `model` a vector of characters describing the model for each network of the Multiplex relation between nodes (`'bernoulli'`, `'poisson'`, `'gaussian'`, ...). Default is `'bernoulli'`. `type` a string of character indicating whether the networks are directed, undirected or bipartite `dependent` connection parameters in each network `dimLabels` an optional list of labels for functional group involved in the network `seed` numeric to set the seed.

## Value

a list of two elements : `simulatedMemberships` are the clustering of each node in each Functional Group, `MultiplexNetwork` is the list of the simulated networks (each one being a simple or bipartite network)

## Examples

 `1` ```### ======================================= ```

GrossSBM/sbm documentation built on April 8, 2021, 5:53 a.m.