Latent Class Analysis Model Prior class

An S4 class to represent a Latent Class Analysis model Such model can be used to cluster a data.frame \(X\) with several columns of factors with the following generative model : $$\pi \sim \textrm{Dirichlet}(\alpha),$$ $$\forall k, \forall j, \quad \theta_{kj} \sim \textrm{Dirichlet}_{d_j}(\beta),$$ $$Z_i \sim \mathcal{M}_K(1,\pi),$$ $$\forall j=1, \ldots, p, \quad X_{ij}|Z_{ik}=1 \sim \mathcal{M}_{d_j}(1, \theta_{kj}),$$ These classes mainly store the prior parameters value (\(\alpha,\beta\)) of this generative model. The Lca-class must be used when fitting a simple Latent Class Analysis whereas the LcaPrior-class must be used when fitting a CombinedModels-class.

LcaPrior(beta = 1)

Lca(alpha = 1, beta = 1)

Arguments

beta

Dirichlet prior parameter for all the categorical feature (default to 1)

alpha

Dirichlet prior parameter over the cluster proportions (default to 1)

Value

a LcaPrior-class object a Lca-class object

See also

LcaFit-class, LcaPath-class

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

Examples

LcaPrior()
#> An object of class "LcaPrior"
#> Slot "beta":
#> [1] 1
#> 
LcaPrior(beta = 0.5)
#> An object of class "LcaPrior"
#> Slot "beta":
#> [1] 1
#> 
Lca()
#> An object of class "Lca"
#> Slot "alpha":
#> [1] 1
#> 
#> Slot "beta":
#> [1] 1
#> 
Lca(beta = 0.5)
#> An object of class "Lca"
#> Slot "alpha":
#> [1] 1
#> 
#> Slot "beta":
#> [1] 0.5
#>