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I can’t run regression trees without geeglm. I have longitudinal data so rpart wouldn’t work. Is there a way to get regression trees with geeglm?

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I'm not aware of any decision tree based on geeglm. However, you can use the glmtree() function from the partykit package and specify the cluster= argument. This essentially corresponds to using geeglm() with an independence working model and a clustered covariance matrix estimate.

Alternatively, you can use glmertree() from the package glmertree which combines the glmer() function from lme4 for estimating the random effects and glmtree() from partykit to estimate the tree structure. For more details see: Fokkema et al. (2018). "Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees." Behavior Research Methods, 50(5), 2016-2034. doi:10.3758/s13428-017-0971-x

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