influence.ME: Tools for detecting influential data in mixed models
influence.ME is an R package providing statistical tools for determining influential data in mixed models. 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 data is, several measures of influence are commonly used, such as DFBETAS and Cook’s Distance.
Influence.ME is available from within the R software, which is freely to download and use.
This is a fixed page on influence.ME. Several resources are available from here. Announcements will be made on my blog, but all important information will be accessible from this page.
Reference documents on influence.ME
- Manual
- Overview of the package
- Example Analyses
- Installation Guide
- Change Log
- References
I just can’t wait!
[...] 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. [...]
[...] 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 [...]
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…
[...] 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 [...]
[...] More information about influence.ME can be found on another section of my website. [...]
[...] 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 [...]