I am hoping to implement an unsupervised technique that identifies distinct clusters of individuals based on longitudinal data: 100 continuous or categorical variables measured at different ages.

A lot of the functionality provided by R packages seems to have been developed for simpler cases (eg with just one variable measured at different time points), so I was wondering what the best way to approach such a problem with R might be and which techniques (eg Latent Class Modelling) are considered to perform best.

  • $\begingroup$ I have used Latent Transition Analysis with discretized continuous variables and other categoricals. Not sure if R supports it, but you can do it in SAS. In terms of unsupervised...I assume you want to use this for data mining or something? $\endgroup$ May 13, 2013 at 19:35
  • $\begingroup$ @toomuchpj The poLCA R package offers some facilities for Latent Class Analysis as well. $\endgroup$
    – chl
    May 13, 2013 at 21:16
  • $\begingroup$ Yes, I do want to use it for data mining and identifying distinct subgroups (eg start with high values of x1, ..., x50 at early ages, then increases in x51, ..., x100 etc.) I will have a look at poLCA - any other tecniques that could be relevant? $\endgroup$
    – Guest333
    May 14, 2013 at 9:59
  • 1
    $\begingroup$ Possible duplicate stats.stackexchange.com/questions/13442/… $\endgroup$
    – radek
    Jan 24, 2019 at 2:00

1 Answer 1


If you have a particular longitudinal variable you are interested in, you could take an unsupervised approach on the covariates using either a mixed-effects regression tree or latent growth curve structural equation modeling tree. For SEM trees, see this for more info: http://brandmaier.de/semtree/user-guide/


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