Tools for Detecting Influential Data in Mixed Effects Models
Compute the Cook's distance measure of influential data on mixed effec...
Compute the DFBETAS measure of influential data
Exclude the influence of a grouped set of observations in mixed effect...
Returns the levels of a grouping factor in a mixed effects regression ...
Influence.ME: Tools for detecting influential data in mixed effects mo...
influence returns mixed model estimates, iteratively excluding the inf...
Compute the percentage change, as measure of influential data
Dotplot visualization of measures of influence
Standard errors of fixed estimates
Test for changes in the level of statistical significance resulting fr...
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 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 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.