Artificial dataset for illustrating (G)LMM trees for detecting treatment-subgroup interactions.
A data frame containing 1000 observations on 8 variables.
numeric. Continuous partitioning variables.
factor. Binary treatment variable.
factor. Indicator for cluster with 10 levels.
numeric. Continuous treatment outcome.
factor. Binary treatment outcome.
The data contains four treatment effect subgroups with respect to the
continuous partitioning variables (
variable has an additional random intercept that should be accounted for.
The outcome is assessed either by a continuous variable (
a binary variable (
There are two large subgroups where Treatment 1 is better than Treatment 2 and
vice versa, respectively. Additionally, there are two smaller subgroups where
both treatments lead to comparable outcomes. For the corresponding (G)LMM trees
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