Clustering Longitudinal Data Using R: A Monte Carlo Study


  • Peter Verboon Orcid
  • Ron Pat-El Orcid


The analysis of change within subjects over time is an ever more important research topic. Besides modelling the individual trajectories, a related aim is to identify clusters of subjects within these trajectories. Various methods for analyzing these longitudinal trajectories have been proposed. In this paper we investigate the performance of three different methods under various conditions in a Monte Carlo study. The first method is based on the non-parametric k-means algorithm. The second is a latent class mixture model, and the third a method based on the analysis of change indices. All methods are available in R. Results show that the k-means method performs consistently well in recovering the known clustering structure. The mixture model method performs reasonably well, but the change indices method has problems with smaller data sets.