Longitudinal clustering in kinesiology 1: the bare essentials of the kml and kml3d packages in R
DOI:
https://doi.org/10.63750/nswme776Keywords:
repeated measures, longitudinal clustering, cluster analysis, multivariate repeated measures, R, longitudinal k-meansAbstract
Longitudinal designs are common in the field of kinesiology, where there can often be high variability due to varying response patterns between participants. In studies where outcome changes are seen, there may be hidden groups of non-responders. Alternatively, in studies where no overall changes are seen, there may be hidden groups of responders. Longitudinal clustering is a method that is used to find unique trajectories in longitudinal designs. Cluster trajectories are a result of data quality, and clustering is not meant to be hypothesis driven. However, It may complement hypothesis-driven methods to better understand overall and nuanced change patterns. The purpose of this article is to demonstrate how to analyze a synthetic dataset using the kml and kml3d packages in R. It is meant to be relatively non-technical, easy to use, and interpret for inexperienced coders.
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Copyright (c) 2026 Keston Lindsay (Author)
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