An S4 class to represent a degree corrected stochastic block model for co_clustering of bipartite graph.
Such model can be used to cluster graph vertex, and model a bipartite graph adjacency matrix X with the following generative model :
π∼Dirichlet(α)
Zri∼M(1,πr)
Zcj∼M(1,πc)
θkl∼Exponential(p)
γri∼U(Sk)
γci∼U(Sl)
Xij|ZcikZrjl=1∼P(γriθklγcj)
The individuals parameters γri,γcj allow to take into account the node degree heterogeneity.
These parameters have uniform priors over simplex Sk.
These classes mainly store the prior parameters value α,p of this generative model.
The DcLbm-class
must be used when fitting a simple Diagonal Gaussian Mixture Model whereas the DcLbmPrior-class
must be sued when fitting a CombinedModels-class
.
DcLbmPrior(p = NaN)
DcLbm(alpha = 1, p = NaN)
Exponential prior parameter (default to Nan, in this case p will be estimated from data as the average intensities of X)
Dirichlet prior parameter over the cluster proportions (default to 1)
a DcLbmPrior-class
a DcLbm-class
object
DcLbmFit-class
, DcLbmPath-class
Other DlvmModels:
CombinedModels
,
DcSbm
,
DiagGmm
,
DlvmPrior-class
,
Gmm
,
Lca
,
MoM
,
MoR
,
MultSbm
,
Sbm
,
greed()
DcLbmPrior()
#> An object of class "DcLbmPrior"
#> Slot "p":
#> [1] NaN
#>
DcLbmPrior(p = 0.7)
#> An object of class "DcLbmPrior"
#> Slot "p":
#> [1] 0.7
#>
DcLbm()
#> An object of class "DcLbm"
#> Slot "alpha":
#> [1] 1
#>
#> Slot "p":
#> [1] NaN
#>
DcLbm(p = 0.7)
#> An object of class "DcLbm"
#> Slot "alpha":
#> [1] 1
#>
#> Slot "p":
#> [1] 0.7
#>