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lcda

Latent class discriminant analysis for categorical data, including local and common-components variants.

Installation

CRAN:

install.packages("lcda")

Development version:

remotes::install_github("mchlbckr/lcda")

Usage

library(lcda)

# See ?lcda, ?cclcda, and ?cclcda2 for examples

Overview

Key functions:

• lcda(): fits separate latent class models per class.
• cclcda(): fits a common-components latent class model with class-specific mixing proportions.
• cclcda2(): fits a common-components model with class-conditional mixing proportions.

Data requirements:

• Manifest variables must be integer-coded and start at 1.
• Grouping labels must be integer-coded and start at 1.

Documentation

The package includes a vignette with a worked example:

vignette("lcda")

Reference

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.