Lca: Latent Class Analysis Model Prior class

In comeetie/greed: Clustering and Model Selection with the Integrated Classification Likelihood

Latent Class Analysis Model Prior class

Description

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 :

π \sim \textrm{Dirichlet}(α),

\forall k, \forall j, \quad θ_{kj} \sim \textrm{Dirichlet}_{d_j}(β),

Z_i \sim \mathcal{M}_K(1,π),

\forall j=1, …, p, \quad X_{ij}|Z_{ik}=1 \sim \mathcal{M}_{d_j}(1, θ_{kj}),

These classes mainly store the prior parameters value (α,β) 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.

Usage

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()
LcaPrior(beta = 0.5)
Lca()
Lca(beta = 0.5)

comeetie/greed documentation built on Oct. 10, 2022, 5:37 p.m.