Comments on: influence.ME: Tools for detecting influential data in mixed models

By: Influence.ME: Simple Analysis | Curving Normality

Influence.ME: Simple Analysis | Curving Normality — Thu, 16 Jul 2009 11:02:16 +0000

[...] encouraged to discuss the content of the manual here. All information will be accessible from the influence.ME website as well. Note that updates to the manual will be made available on that website”, instead of [...]

By: Presenting influence.ME at useR! | Curving Normality

Presenting influence.ME at useR! | Curving Normality — Sat, 11 Jul 2009 10:22:09 +0000

[...] More information about influence.ME can be found on another section of my website. [...]

By: One outlier and you’re out: Influential data and racial prejudice | Curving Normality

Tue, 16 Jun 2009 11:02:16 +0000

[...] preparing a presentation on analyzing influential data in mixed effects models myself, my eye fell on an article in which important claims on racial prejudice were refuted. An [...]

By: Stijn Ruiter

Stijn Ruiter — Wed, 27 May 2009 18:33:24 +0000

Influence.ME suffers from ill-convergence of lmer models. Now, lmer models generally seem to converge more quickly when using poly() for all independent variables. However, influence.ME fails when this approach is followed in the target model. This problem is easily overcome when poly-variables are first constructed before they are entered into the model of interest.
Something like this:

data$x1.poly<-poly(data$x1,1)
data$x2.poly<-poly(data$x2,2)
lmer(y~(1|level2id)+x1.poly+x2.poly,data=data)

hope this helps to use the influence.ME macro…

By: Introducing Influence.ME: Tools for detecting influential data in mixed models – Curving Normality

Sun, 03 May 2009 18:45:11 +0000

[...] features, or exciting applications in research papers will be discussed here as well in due time. A static page on influence.ME is available as well, where all important information is [...]

By: useR! 2009 acceptance: presenting influence.ME – Curving Normality

useR! 2009 acceptance: presenting influence.ME – Curving Normality — Thu, 23 Apr 2009 10:24:05 +0000

[...] Influence.ME is an R package that provides a collection of tools for detecting influential data in mixed effects models. Testing for inﬂuence with mixed effects models is especially important in Social Science applications, for two reasons. First, models in the Social Sciences are frequently based on large numbers of individuals while the number of higher level units is often relatively small. Secondly, often the higher level units are remarkably similar, for instance in the case of neighboring countries. [...]

By: Manfred te Grotenhuis

Manfred te Grotenhuis — Wed, 22 Apr 2009 22:55:05 +0000

I just can’t wait!