Because arrests were fairly prevalent in the sample (30%) and the final model’s predictions were fairly sensitive and specific in predicting arrests, we examined the combination of risk factors that resulted in the greatest risk of arrest. We used a conditional inference tree with the ctree function of the party package (Hothorn, Hornik, & Zeileis, 2006) in R to determine the most common risk profiles among those who had been arrested. Using a conditional inference tree to identify the risk profiles associated with arrest may improve classification of those at greatest risk of arrest, which may lead to targeted, cost-effective interventions.
