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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2402.14621 |
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| _version_ | 1866914689214054400 |
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| author | Teuling, Niek Den Pauws, Steffen Heuvel, Edwin van den |
| author_facet | Teuling, Niek Den Pauws, Steffen Heuvel, Edwin van den |
| contents | Clustering of longitudinal data is used to explore common trends among subjects over time for a numeric measurement of interest. Various R packages have been introduced throughout the years for identifying clusters of longitudinal patterns, summarizing the variability in trajectories between subject in terms of one or more trends. We introduce the R package "latrend" as a framework for the unified application of methods for longitudinal clustering, enabling comparisons between methods with minimal coding. The package also serves as an interface to commonly used packages for clustering longitudinal data, including "dtwclust", "flexmix", "kml", "lcmm", "mclust", "mixAK", and "mixtools". This enables researchers to easily compare different approaches, implementations, and method specifications. Furthermore, researchers can build upon the standard tools provided by the framework to quickly implement new cluster methods, enabling rapid prototyping. We demonstrate the functionality and application of the latrend package on a synthetic dataset based on the therapy adherence patterns of patients with sleep apnea. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | latrend: A Framework for Clustering Longitudinal Data Teuling, Niek Den Pauws, Steffen Heuvel, Edwin van den Machine Learning Clustering of longitudinal data is used to explore common trends among subjects over time for a numeric measurement of interest. Various R packages have been introduced throughout the years for identifying clusters of longitudinal patterns, summarizing the variability in trajectories between subject in terms of one or more trends. We introduce the R package "latrend" as a framework for the unified application of methods for longitudinal clustering, enabling comparisons between methods with minimal coding. The package also serves as an interface to commonly used packages for clustering longitudinal data, including "dtwclust", "flexmix", "kml", "lcmm", "mclust", "mixAK", and "mixtools". This enables researchers to easily compare different approaches, implementations, and method specifications. Furthermore, researchers can build upon the standard tools provided by the framework to quickly implement new cluster methods, enabling rapid prototyping. We demonstrate the functionality and application of the latrend package on a synthetic dataset based on the therapy adherence patterns of patients with sleep apnea. |
| title | latrend: A Framework for Clustering Longitudinal Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.14621 |