An S4 class to represent a Mixture of Multinomial model.
Such model can be used to cluster a data matrix \(X\) with the following generative model :
$$ \pi \sim Dirichlet(\alpha)$$
$$ Z_i \sim \mathcal{M}(1,\pi)$$
$$ \theta_{k} \sim Dirichlet(\beta)$$
$$ X_{i.}|Z_{ik}=1 \sim \mathcal{M}(L_i,\theta_{k})$$
With \(L_i=\sum_d=1^DX_{id}\). These classes mainly store the prior parameters value (\(\alpha,\beta\)) of this generative model.
The MoM-class must be used when fitting a simple Mixture of Multinomials whereas the MoMPrior-class must be sued when fitting a MixedModels-class.
MoMPrior(beta = 1)
MoM(alpha = 1, beta = 1)Dirichlet over vocabulary prior parameter (default to 1)
Dirichlet prior parameter over the cluster proportions (default to 1)
a MoMPrior-class object
a MoM-class object
MoM-class: MoM class constructor
MoMPrior: MoMPrior class constructor
MoM: MoM class constructor
betaDirichlet over vocabulary prior parameter (default to 1)
alphaDirichlet prior parameter over the cluster proportions (default to 1)
Other DlvmModels:
DcLbmPrior-class,
DcSbmPrior-class,
DiagGmmPrior-class,
DlvmPrior-class,
Gmm,
LcaPrior-class,
MixedModels-class,
MoRPrior-class,
MultSbmPrior-class,
SbmPrior-class,
greed()
MoMPrior()
#> An object of class "MoMPrior"
#> Slot "beta":
#> [1] 1
#>
MoMPrior(beta = 0.5)
#> An object of class "MoMPrior"
#> Slot "beta":
#> [1] 1
#>
MoM()
#> An object of class "MoM"
#> Slot "alpha":
#> [1] 1
#>
#> Slot "beta":
#> [1] 1
#>
MoM(beta = 0.5)
#> An object of class "MoM"
#> Slot "alpha":
#> [1] 1
#>
#> Slot "beta":
#> [1] 0.5
#>