Lca function

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 : [REMOVE_ME]$\pi \sim \textrm{Dirichlet}(\alpha), [REMOVE_ME_2]$

[REMOVE_ME]$\forall k, \forall j, \quad \theta_{kj} \sim \textrm{Dirichlet}_{d_j}(\beta), [REMOVE_ME_2]$

[REMOVE_ME]$Z_i \sim \mathcal{M}_K(1,\pi), [REMOVE_ME_2]$

[REMOVE_ME]$\forall j=1, \ldots, p, \quad X_{ij}|Z_{ik}=1 \sim \mathcal{M}_{d_j}(1, \theta_{kj}), [REMOVE_ME_2]$

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. 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)

Returns

a LcaPrior-class object

a Lca-class object

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 :

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.

Examples

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

See Also

LcaFit-class, LcaPath-class

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