influence.ME: Tools for Detecting Influential Data in Mixed Effects Models

Authors

Rense Nieuwenhuis, University of Twente
Manfred te Grotenhuis, Radboud University
Ben Pelzer, Radboud University

Date of this Version

12-2012

Document Type

Article

Citation

The R Journal (December 2012) 4(2)

Comments

Copyright 2012, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

influence.ME provides tools for detecting influential data in mixed effects models. The application of these models has become common practice, but the development of diagnostic tools has lagged behind. influence.ME calculates standardized measures of influential data for the point estimates of generalized mixed effects models, such as DFBETAS, Cook’s distance, as well as percentile change and a test for changing levels of significance. influence.ME calculates these measures of influence while ac counting for the nesting structure of the data. The package and measures of influential data are introduced, a practical example is given, and strategies for dealing with influential data are suggested.