Abstract
GLMMTree is a tree-based algorithm that can detect interaction and find subgroups in the GLMM to improve fixed effect estimation. This study uses GLMM trees in real data applications of poverty in Indonesia. Using this data, we found that the GLMMTree algorithm method performs similarly to GLMM. 2 significant predictors affect poverty in Indonesia: the unemployment rate and the GRDP at a constant price. GLMMTree algorithm enriches the analysis by finding two variables, namely the percentage of households with electricity lighting access and the percentage of households with clean drinking water sources, that interact with predictor variables in the model.
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