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.
|Depends:||R (≥ 2.15.0), lme4 (≥ 1.0)|
|Imports:||Matrix (≥ 1.0), lattice|
|Author:||Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis|
|Maintainer:||Rense Nieuwenhuis <rense.nieuwenhuis at sofi.su.se>|
|Citation:||influence.ME citation info|
|CRAN checks:||influence.ME results|
|Windows binaries:||r-devel: influence.ME_0.9-8.zip, r-release: influence.ME_0.9-8.zip, r-oldrel: influence.ME_0.9-8.zip|
|OS X Mavericks binaries:||r-release: influence.ME_0.9-8.tgz, r-oldrel: influence.ME_0.9-8.tgz|
|Old sources:||influence.ME archive|
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