Package: influence.ME
Type: Package
Title: Tools for Detecting Influential Data in Mixed Effects Models
Version: 0.9-8
Date: 2016-10-31
Author: Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis
Maintainer: Rense Nieuwenhuis <rense.nieuwenhuis@sofi.su.se>
Description: 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.
License: GPL-3
URL: http://www.rensenieuwenhuis.nl/r-project/influenceme/
Depends: R(>= 2.15.0), lme4(>= 1.0)
Imports: Matrix(>= 1.0), lattice
LazyLoad: yes
LazyData: yes
NeedsCompilation: no
Packaged: 2016-10-31 06:58:42 UTC; rensenieuwenhuis
Repository: CRAN
Date/Publication: 2016-10-31 10:07:54

when single units are omitted from the data, models
influence are common practice, such as Cook's Distance.
Packaged: 2017-06-07 06:56:57 UTC; rensenieuwenhuis

Package: influence.ME
Type: Package
Title: Tools for detecting influential data in mixed effects models
Version: 0.9.1
Date: 2012-12-24
Author: Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis
Maintainer: Rense Nieuwenhuis <r.nieuwenhuis@utwente.nl>
Description: influence.ME provides a collection of tools for
calculating measures of influential data for 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 Cooks' Distance.  In addition, we
provide a measure of percentage change of the fixed point
estimates and a simple test to detect changing levels of
significance.
License: GPL-3
URL: http://www.rensenieuwenhuis.nl/r-project/influenceme/
Depends: lme4, lattice
Imports: Matrix(>= 1.0)
LazyLoad: yes
LazyData: yes
Packaged: 2012-12-24 10:46:53 UTC; nieuwenhuisrr
Repository: CRAN
Date/Publication: 2012-12-24 12:01:58