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 with the following generative model :
These classes mainly store the prior parameters value 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")