ddtlcm

ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization - Published in JOSS (2024)

https://github.com/limengbinggz/ddtlcm

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Repository

Basic Info

Host: GitHub
Owner: limengbinggz
License: other
Language: R
Default Branch: main
Size: 41.1 MB

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Created almost 3 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog License Citation

README.md

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

An R package for tree-regularized latent class models with a DDT process prior on class profiles to overcome weak separation issues

Maintainer: Mengbing Li (mengbing@umich.edu)

Contributors: Briana Stephenson (bstephenson@hsph.harvard.edu); Zhenke Wu (zhenkewu@umich.edu)

| | Citation | Paper Link | ------------- | ------------- | ------------- | | Bayesian tree-regularized LCM | Li M, Stephenson B, Wu Z (2025). Tree-Regularized Bayesian Latent Class Analysis for Improving Weakly Separated Dietary Pattern Subtyping in Small-Sized Subpopulations. Annals of Applied Statistics. In press. |Link| | Method | 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| | Software | Li M, Wu B, Stephenson B, Wu Z (2024). ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization. Journal of Open Source Software, 9(99), 6220, https://doi.org/10.21105/joss.06220. |Link|

Gallery

Installation

```r

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

r 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)

A Quickstart

```r library(ddtlcm)

data(parameter_diet)

unlist the elements into variables in the global environment

list2env(setNames(parameterdiet, names(parameterdiet)), envir = globalenv())

N <- 496 seedparameter = 1 # random seed to generate node parameters given the tree seedresponse = 1 # random seed to generate multivariate binary observations from LCM

simulate data given the parameters

simdata <- simulatelcmgiventree(treephylo, N, classprobability, itemmembershiplist, Sigmabygroup, rootnodelocation = 0, seedparameter = seedparameter, seedresponse = seedresponse)

K <- 6 # number of latent classes, same as number of leaves on the tree resultdiet <- ddtlcmfit(K = K, data = simdata$responsematrix, itemmembershiplist = itemmembershiplist, totaliters = 100) print(resultdiet) ```

Examples

A simple workflow using semi-synthetic data is provided.

Tests

Unit tests are in place to ensure correct implementation of certain utility functions, and ensure the functions accept and return the correct classes of input and output. To run the unit tests in the repository, the R package testthat is required. After incorporating your changes to the package code, run the following line in a terminal for unit tests:

R CMD check --as-cran ddtlcm_0.2.1.tar.gz

In addition, automated tests are implemented in major functions to inform the user about any false input.

Reference Manual

See the manual on CRAN.

Contributing And Getting Help

Please report bugs by opening an issue. If you wish to contribute, please make a pull request. If you have questions, you can open a discussion thread.

If you are in need of support, please contact the maintainer at limengbinggz@gmail.com.

Note

When running some functions in the package, such as ddtlcm_fit, a warning that "Tree contains singleton nodes" may be displayed. This warning originates from the checkPhylo4 function in the phylobase package to perform basic checks on the validity of S4 phylogenetic objects. We would like to point out that seeing such warnings shall not pose any concerns about the statistical validity of the implemented algorithm. This is because any tree generaetd from a DDT process contains a singleton node (having only one child node) as the root node. To avoid repeated appearances of this warning, we recommend either of the followings:
- Wrapping around the code via suppressWarnings({ code_that_will_generate_singleton_warning });
- Setting options(warn = -1) globally. This may be dangerous because other meaningful warnings may be ignored.

Owner

Name: Mengbing Li
Login: limengbinggz
Kind: user

Repositories: 1
Profile: https://github.com/limengbinggz

JOSS Publication

ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization

Published

July 15, 2024

DOI

10.21105/joss.06220

Volume 9, Issue 99, Page 6220

Authors

Mengbing Li

Department of Biostatistics, University of Michigan

Bolin Wu
Department of Computer Science, University of Michigan

Briana Stephenson
Department of Biostatistics, Harvard University

Zhenke Wu

Department of Biostatistics, University of Michigan

Editor

Nikoleta Glynatsi

Citation (CITATION.cff)

cff-version: "1.2.0"
authors:
- family-names: Li
  given-names: Mengbing
  orcid: "https://orcid.org/0000-0002-2264-8006"
- family-names: Wu
  given-names: Bolin
- family-names: Stephenson
  given-names: Briana
- family-names: Wu
  given-names: Zhenke
  orcid: "https://orcid.org/0000-0001-7582-669X"
contact:
- family-names: Li
  given-names: Mengbing
  orcid: "https://orcid.org/0000-0002-2264-8006"
- family-names: Wu
  given-names: Zhenke
  orcid: "https://orcid.org/0000-0001-7582-669X"
doi: 10.5281/zenodo.12711232
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Li
    given-names: Mengbing
    orcid: "https://orcid.org/0000-0002-2264-8006"
  - family-names: Wu
    given-names: Bolin
  - family-names: Stephenson
    given-names: Briana
  - family-names: Wu
    given-names: Zhenke
    orcid: "https://orcid.org/0000-0001-7582-669X"
  date-published: 2024-07-15
  doi: 10.21105/joss.06220
  issn: 2475-9066
  issue: 99
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 6220
  title: "ddtlcm: An R package for overcoming weak separation in
    Bayesian latent class analysis via tree-regularization"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.06220"
  volume: 9
title: "ddtlcm: An R package for overcoming weak separation in Bayesian
  latent class analysis via tree-regularization"

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cran.r-project.org: ddtlcm

Latent Class Analysis with Dirichlet Diffusion Tree Process Prior

Homepage: https://github.com/limengbinggz/ddtlcm
Documentation: http://cran.r-project.org/web/packages/ddtlcm/ddtlcm.pdf
License: MIT + file LICENSE
Latest release: 0.2.1
published almost 2 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 147 Last month

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Dependent repos count: 36.6%

Average: 39.1%

Downloads: 85.9%

Maintainers (1)

mengbing@umich.edu

Last synced: 6 months ago

ddtlcm

Science Score: 100.0%

Scientific Fields

Repository

Basic Info

Statistics

Metadata Files

README.md

Gallery

Table of content

Installation

install bioconductor package ggtree for visualizing results:

Overview

A Quickstart

unlist the elements into variables in the global environment

simulate data given the parameters

Examples

Tests

Reference Manual

Contributing And Getting Help

Note

Owner

JOSS Publication

ddtlcm: An R package for overcoming weak separation in Bayesian latent class analysis via tree-regularization

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Editor

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Citation (CITATION.cff)

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install bioconductor package `ggtree` for visualizing results: