xiahui625649 / bcclong

贝叶斯一致性聚类（重复抽样多次聚类，评估聚类结果可靠性），包括各种类型数据，如纵向性状等

BCCLong

The goal of BCCLong is to compute a Bayesian Consensus Clustering (BCC) model for mixed-type longitudinal data

Description

Statistical methods for clustering a single longitudinal trajectory have been well-developed and widely used in many different medical research areas. However, it is very common these days to encounter situations where several longitudinal markers or responses are collected simultaneously in a study and there is a growing interest to examine how multiple longitudinal characteristics could collectively contribute to disaggregating disease heterogeneity. Therefore, the BCClong package has been created. BCClong is an R package for performing Bayesian Consensus Clustering (BCC) model for clustering continuous, discrete and categorical longitudinal data, which are commonly seen in many clinical studies [Lu et al., 2021]https://doi.org/10.1002/sim.9225.

Installation

You can install the development version of BCC from GitHub with:

# install.packages("devtools")
devtools::install_github("ZhiwenT/BCClong", build_vignettes = TRUE)
library("BCClong")

Overview

To list all the functions available in the package:

ls("package:BCClong")

Currently, there are 5 function in this package which are BCC.multi, BayesT, model.selection.criteria, traceplot, trajplot.

BCC.multi function performs clustering on mixed-type (continuous, discrete and categorical) longitudinal markers using Bayesian consensus clustering method with MCMC sampling and provide a summary statistics for the computed model. This function will take in a data set and multiple parameters and output a BCC model with summary statistics.

BayesT function assess the model goodness of fit by calculate the discrepancy measure T(, ) with following steps (a) Generate T.obs based on the MCMC samples (b) Generate T.rep based on the posterior distribution of the parameters (c) Compare T.obs and T.rep, and calculate the P values.

model.selection.criteria function calculates DIC and WAIC for the fitted model traceplot function visualize the MCMC chain for model parameters trajplot function plot the longitudinal trajectory of features by local and global clustering

more information can be found by using the code below after installation

?BCClong::BCC.multi
?BCClong::BayesT
?BCClong::model.selection.criteria
?BCClong::traceplot
?BCClong::trajplot

The package tree structure is provide below

- BCClong
  |- BCClong.Rproj
  |- DESCRIPTION
  |- NAMESPACE
  |- LICENSE
  |- README
  |- NEWS
  |- inst
    |- extdata
      |- Epileptic.rds
      |- PBCseq.rds
      |- MeanAdj.png
      |- procedure.png
    |-CITATION
  |- man
    |- BayesT.Rd
    |- BCC.multi.Rd
    |- model.selection.criteria.Rd
    |- traceplot.Rd
    |- trajplot.Rd
  |- R
    |- bcclong.R
    |- DiscrepancyMeasure.R
    |- modelSelection.R
    |- RcppExports.R
    |- Traceplot.R
    |- Trajplot.R
  |- src
    |- c_which.h
    |- c_which.cpp
    |- BCC.cpp
    |- Likelihood.cpp
    |- RcppExports.cpp
    |- Makevars
    |- Makevars.win
  |- vignettes
    |- ContinuousData.Rmd
    |- MixedTypeData.Rmd

Tutorials

For tutorials and plot interpretation, refer to the vignette:

browseVignettes("BCClong")

Three options include a HTMl version, source R markdown file and R code file. There are two tutorials in this package, one is for dataset with continuous data only, and the second one is for dataset with mixed type of data. Tutorial can also be found from the link below. Make sure to open the html file in browser, the github website only shows the source code.

For multiple continuous longitudinal markers only:

https://htmlpreview.github.io/?https://github.com/ZhiwenT/BCClong/blob/main/vignettes/ContinuousData.html

For multiple mixed type longitudinal markers:

https://htmlpreview.github.io/?https://github.com/ZhiwenT/BCClong/blob/main/vignettes/MixedTypeData.html

Citation for Package

citation("BCClong")

Tan, Z., Shen, C., Lu, Z. (2022) BCClong: an R package for performing Bayesian Consensus Clustering model for clustering continuous, discrete and categorical longitudinal data. URL https://github.com/ZhiwenT/BCClong

References

• [Lu, Z., & Lou, W. (2021). Bayesian consensus clustering for Multivariate Longitudinal Data. Statistics in Medicine, 41(1), 108–127.]https://doi.org/10.1002/sim.9225

• [Tan, Z., Shen, C., Subbarao, P., Lou, W. and Lu, Z., 2022. A Joint Modeling Approach for Clustering Mixed-Type Multivariate Longitudinal Data: Application to the CHILD Cohort Study. arXiv preprint.]https://doi.org/10.48550/arXiv.2210.08385

Maintainer

• Zhiwen Tan ([email protected]).

Contributions

BCClong welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.