Latent Class Discriminant Analysis
In lcda: Latent Class Discriminant Analysis

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

Overview

The lcda package provides latent class discriminant analysis methods for categorical predictors. The main functions are:

lcda() for class-specific latent class models.
cclcda() for common-components latent class models.
cclcda2() for common-components models with class-conditional mixing weights.

All manifest variables and class labels must be integer-coded and start at 1.

Background

The methods in lcda implement local discrimination for discrete variables using latent class analysis (LCA). The key idea is to replace a single class-conditional distribution with a finite mixture of locally independent components. This lets each class capture heterogeneity while keeping the model tractable for categorical data.

Let K be the number of classes, M the number of latent components, D the number of manifest variables, and R_d the number of outcomes for variable d. The indicator x_dr equals 1 if variable d takes outcome r and 0 otherwise.

Models

LCDA (class-specific mixtures)

Each class has its own latent class model:

$$ f_k(x) = \sum_{m=1}^{M_k} w_{mk} \prod_{d=1}^D \prod_{r=1}^{R_d} \theta_{mkdr}^{x_{dr}} $$

Classification follows the Bayes decision rule:

$$ \hat{k}(x) = \arg\max_k \pi_k f_k(x) $$

CCLCDA (common components)

Common-components models share the component distributions across classes, while allowing class-specific mixing weights:

$$ f_k(x) = \sum_{m=1}^{M} w_{mk} \prod_{d=1}^D \prod_{r=1}^{R_d} \theta_{mdr}^{x_{dr}} $$

cclcda() first estimates the shared LCA on the pooled data and then derives class-conditional weights. cclcda2() estimates weights and response probabilities jointly in each EM step.

Estimation and model selection

Parameter estimation uses the EM algorithm with random starts (see nrep). Model selection can be guided by AIC, BIC, the likelihood ratio statistic (Gsq), and the Pearson chi-square statistic (Chisq). For common-components models, additional quality measures are provided:

Weighted entropy, measuring the purity of latent components.
Weighted Gini, an alternative impurity measure.
A chi-square test of independence between latent components and classes.

These are reported in the fitted model objects returned by cclcda() and cclcda2().

Example: CCL-CDA2 on Iris

library(lcda)

data(iris)

iris_cat <- within(iris, {
  Sepal.Length <- as.integer(cut(Sepal.Length, breaks = c(-Inf, 5.1, 5.8, 6.4, Inf)))
  Sepal.Width <- as.integer(cut(Sepal.Width, breaks = c(-Inf, 2.8, 3.0, 3.3, Inf)))
  Petal.Length <- as.integer(cut(Petal.Length, breaks = c(-Inf, 1.6, 4.35, 5.1, Inf)))
  Petal.Width <- as.integer(cut(Petal.Width, breaks = c(-Inf, 0.3, 1.3, 1.8, Inf)))
  Species3 <- as.integer(Species)
})

model <- cclcda2(
  Species3 ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
  data = iris_cat,
  m = 1
)

model$bic

References

Bücker, M., Szepannek, G., Weihs, C. (2010). Local Classification of Discrete Variables by Latent Class Models. In: Locarek-Junge, H., Weihs, C. (eds) Classification as a Tool for Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10745-0_13

Bücker, M. (2008). Lokale Diskrimination diskreter Daten. Diplomarbeit, Fakultaet Statistik, TU Dortmund.