Max Planck Institute of Psychiatry, 80804 Munich, Germany.
International Max Planck Research School for Translational Psychiatry, 80804 Munich, Germany.
Bioinformatics. 2024 Mar 29;40(4). doi: 10.1093/bioinformatics/btae137.
Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization.
The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.
准确地对混合数据进行聚类,包括二进制、分类和连续变量,对于临床问卷分析中的有效患者分层至关重要。为此,我们提出了 longmixr,这是一个全面的 R 包,提供了使用有限混合模型技术对混合纵向数据进行聚类的强大框架。通过合并一致聚类,longmixr 确保了可靠和稳定的聚类结果。此外,该包还包括一个详细的实例,以方便聚类的探索和可视化。
该 R 包可在 https://cran.r-project.org/package=longmixr 上免费获取,详细文档包括案例实例,可在 https://cellmapslab.github.io/longmixr/ 获取。