rlbm: Generate a data matrix using a Latent Block Model

In greed: Clustering and Model Selection with the Integrated Classification Likelihood

Generate a data matrix using a Latent Block Model

Description

rlbm returns the adjacency matrix and the cluster labels generated randomly with a Latent Block Model.

Usage

rlbm(Nr, Nc, pir, pic, mu)

Arguments

Nr	desired Number of rows
Nc	desired Number of column
pir	A numeric vector of length Kr with rows clusters proportions (will be normalized to sum up to 1).
pic	A numeric vector of length Kc with columns clusters proportions (will be normalized to sum up to 1).
mu	A numeric matrix of dim Kr x Kc with the connectivity pattern to generate. elements in [0,1].

Details

This function takes the desired graph size, cluster proportions and connectivity matrix as input and sample a graph accordingly together with the clusters labels.

Value

A list with fields:

• x: the generated data matrix as a dgCMatrix

• clr: vector of row clusters labels

• clc: vector of column clusters labels

• Kr: number of generated row clusters

• Kc: number of generated column clusters

• Nr: number of rows

• Nc: number of column

• pir: row clusters proportions

• pic: column clusters proportions

• mu: connectivity matrix

Examples

simu <- rlbm(500, 1000, rep(1 / 5, 5), rep(1 / 10, 10), matrix(runif(50), 5, 10))

greed documentation built on Oct. 4, 2022, 1:06 a.m.