Artificial Data for Illustrating (G)LMM Trees

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Description

Artificial dataset for illustrating (G)LMM trees for detecting treatment-subgroup interactions.

Usage

1
data("TreatmentSubgroups")

Format

A data frame containing 1000 observations on 8 variables.

u1,u2,u3,u4,u5

numeric. Continuous partitioning variables.

treatment

factor. Binary treatment variable.

cluster

factor. Indicator for cluster with 10 levels.

ynum

numeric. Continuous treatment outcome.

ybin

factor. Binary treatment outcome.

Details

The data contains four treatment effect subgroups with respect to the continuous partitioning variables (u1u5). The cluster variable has an additional random intercept that should be accounted for. The outcome is assessed either by a continuous variable (ynum) or a binary variable (ybin).

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 see lmertree.

See Also

lmertree

Examples

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data("TreatmentSubgroups", package = "glmertree")
summary(TreatmentSubgroups)

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