larryshamalama / ddtlcm

ddtlcm: Dirichlet diffusion tree-latent class model (DDT-LCM)

An R package for Tree-regularized latent class mModels with a DDT process prior on class profiles

Maintainer: Mengbing Li ([email protected])

Contributors: Briana Stephenson ([email protected]); Zhenke Wu ([email protected])

	Citation	Paper Link
Bayesian tree-regularized LCM	Li M, Stephenson B, Wu Z (2023). Tree-Regularized Bayesian Latent Class Analysis for Improving Weakly Separated Dietary Pattern Subtyping in Small-Sized Subpopulations. ArXiv:2306.04700.	Link

Table of content

• 1. Installation
• 2. Overview
• 2. Example

Installation

# install bioconductor package `ggtree` for visualizing results:
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ggtree")

install.packages("devtools",repos="https://cloud.r-project.org")
devtools::install_github("limengbinggz/ddtlcm")

Overview

ddtlcm is designed for analyzing multivariate binary observations over grouped items in a tree-regularized Bayesian LCM framework. Between-class similarities are guided by an unknown tree, where classes positioned closer on the tree are more similar a priori. This framework facilitates the sharing of information between classes to make better estimates of parameters using less data. The model is built upon equipping LCMs with a DDT process prior on the class profiles, with varying degrees of shrinkage across major item groups. The model is particularly promising for addressing weak separation of latent classes when sample sizes are small. The posterior inferential algorithm is based on a hybrid Metropolis-Hastings-within-Gibbs algorithm and can provide posterior uncertainty quantifications.

ddtlcm works for

• multivariate binary responses over pre-specified grouping of items

• The functions' relations in the package ddtlcm can be visualized by

library(DependenciesGraphs) # if not installed, try this-- devtools::install_github("datastorm-open/DependenciesGraphs")
library(QualtricsTools) # devtools::install_github("emmamorgan-tufts/QualtricsTools")
dep <- funDependencies('package:ddtlcm','ddtlcm_fit')
plot(dep)

Examples

• A simple workflow using semi-synthetic data is provided in

• ddtlcm estimates the tree over classes and class profiles simultaneously