influence.ME: Tools for Detecting Influential Data in Mixed Effects Models
Provides a collection of tools for
detecting influential cases in generalized mixed effects
models. It analyses models that were estimated using lme4. The
basic rationale behind identifying influential data is that
when iteratively single units are omitted from the data, models
based on these data should not produce substantially different
estimates. To standardize the assessment of how influential a
(single group of) observation(s) is, several measures of
influence are common practice, such as DFBETAS and Cook's Distance.
In addition, we provide a measure of percentage change of the fixed point
estimates and a simple procedure to detect changing levels of significance.