Sbm: Stochastic Block Model Prior class

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

Stochastic Block Model Prior class

Description

An S4 class to represent a Stochastic Block Model. Such model can be used to cluster graph vertex, and model a square adjacency matrix X with the following generative model :

π \sim Dirichlet(α)

Z_i \sim \mathcal{M}(1,π)

θ_{kl} \sim Beta(a_0,b_0)

X_{ij}|Z_{ik}Z_{jl}=1 \sim \mathcal{B}(θ_{kl})

These classes mainly store the prior parameters value α,a_0,b_0 of this generative model. The Sbm-class must be used when fitting a simple Sbm whereas the SbmPrior-class must be used when fitting a CombinedModels-class.

Usage

SbmPrior(a0 = 1, b0 = 1, type = "guess")

Sbm(alpha = 1, a0 = 1, b0 = 1, type = "guess")

Arguments

a0	Beta prior parameter over links (default to 1)
b0	Beta prior parameter over no-links (default to 1)
type	define the type of networks (either "directed", "undirected" or "guess", default to "guess"), for undirected graphs the adjacency matrix is supposed to be symmetric.
alpha	Dirichlet prior parameter over the cluster proportions (default to 1)

Value

a SbmPrior-class object

a Sbm-class object

References

Nowicki, Krzysztof and Tom A B Snijders (2001). “Estimation and prediction for stochastic block structures”. In:Journal of the American statistical association 96.455, pp. 1077–1087

See Also

greed

SbmFit-class,SbmPath-class

Other DlvmModels: CombinedModels, DcLbm, DcSbm, DiagGmm, DlvmPrior-class, Gmm, Lca, MoM, MoR, MultSbm, greed()

Examples

Sbm()
SbmPrior()
SbmPrior(type = "undirected")
Sbm()
Sbm(type = "undirected")

comeetie/greed documentation built on Oct. 10, 2022, 5:37 p.m.