Latent profile analysis (LPA) is for identifying latent classes of observations based on continuous manifest variables. LPA is different from latent class analysis, which works with categorical manifest variables.
The "mclust" package in R is a general finite mixture modeling package that can accommodate latent categorical variables (classes) and continuous manifest variables. A software review in The American Statistician (found here: http://statisticalinnovations.com/products/LGreview.AmericanStat.pdf) states that it can handle continuous variables (page 85, right column), so it seems that all the pieces are there to get an LPA to run. I haven't done it personally though ,so I don't known how difficult it may be.
Thanks very much, Daniel. It sounds promising. I will try that and will update this post with the results.
If the heterogeneity in your sample (i.e., the latent sub group) can potentially be observed by a combination of predictors and you have no good hypothesis about their relation with the continuous outcomes, you may want to have a look at SEM Trees:
Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological methods, 18(1), 71.
We also have an R package for running SEM Tree analyses.
Thanks, Andreas.
I have a theory for the number of clusters and hypotheses that relate the clusters to the continuous variables. I was wondering if SEM Tree and/or LPA /LCA are the best approaches for testing my theory and hypotheses. Any input is very welcome.
I've been using TidyLPA to great success
https://github.com/jrosen48/tidyLPA
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