| Sbm {greed} | R Documentation |
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 :
\pi \sim Dirichlet(\alpha)
Z_i \sim \mathcal{M}(1,\pi)
\theta_{kl} \sim Beta(a_0,b_0)
X_{ij}|Z_{ik}Z_{jl}=1 \sim \mathcal{B}(\theta_{kl})
These classes mainly store the prior parameters value \alpha,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
Other DlvmModels:
CombinedModels,
DcLbm,
DcSbm,
DiagGmm,
DlvmPrior-class,
Gmm,
Lca,
MoM,
MoR,
MultSbm,
greed()
Examples
Sbm()
SbmPrior()
SbmPrior(type = "undirected")
Sbm()
Sbm(type = "undirected")