With the introduction of our new package for influential data influence.ME, I’m currently writing a manual for the package. This manual will address topics for both the experienced, and the inexperienced users. I will also present much of the content …
Today I presented influence.ME at the useR! conference in Rennes. Influence.ME is an R package for detecting influential data in mixed models. I developed this package together with Ben Pelzer and Manfred te Grotenhuis. More information about influence.ME can be …
Intended to be a customized solution, it may have grown to be a little more. forward.lmer is an early installment of a full stepwise function for mixed effects regression models in R-Project. I may put in some work to extend …
Despite the reference to S and S-PLUS in the title of this book, it offers an excellent guide for the nlme-package in R-Project. Reason for this is the close resemblance between R and S. The nlme-package, available in R-Project for estimation of both linear and non-linear multilevel models, is written and maintained by the authors of this book.
Multilevel models, or mixed effect models, can easily be estimated in R. Several packages are available. Here, the lme() function from the nlme-package is described. The specification of several types of models will be shown, using a fictive example. A detailed description of the specification rules is given. Output of the specified models is given, but not described or interpreted.
Please note that this description is very closely related to the description of the specification of the lmer() function of the lme4-package. The results are similar and here exactly the same possibilities are offered.
In this example, the dependent variable is the standardized result of a student on a specific exam. This variable is called “normexam”. In estimating the score on the exam, two levels will be discerned: student and school. On each level, one explanatory variable is present. On individual level, we are taking into account the standardized score of the student on a LR-test (“standLRT”). On the school-level, we take into account the average intake-score (“schavg”).
Plotting the results of a multilevel analysis, without use of the extension package ‘Lattice’ can be quite complicated while using R. Using only the basic packages, as well as the multilevel packages (nlme and lme4) there are no functions readily available for this task. So, this is a good point in this manual to put some of our programming skills to use. This makes exactly clear how the results of a multilevel analysis are stored in R as well.
Unlike most statistical software packages, R often stores the results of an analysis in an object. The advantage of this is that while not all output is shown in the screen ad once, it is neither necessary to estimate the statistical model again if different output is required.
This paragraph will show the kind of data that is stored in a multilevel model estimated by R-Project and introduce some functions that make use of this data.
Building upon the paper written by Jelen et al. (1993) that I wrote about a few days ago, I’d like to bring to your attention a more recent paper by Dutch researchers. It also addresses attitudes toward abortion in Western Europe, but does so in a rather more advanced manner.
As might be expected from an article written 15 years later, much developments have been made in the research on public opinion regarding induced abortion, both on a theoretical level, as well as on a methodological level. Let’s take a look at the outcomes of those improvements.
Several functions already present in R-Project are very useful when analyzing multilevel models or when preparing data to do so. Three of these helper functions will be described: aggregating data, the behavior of the plot() function when applied to a multilevel model and finally setting contrasts for categorical functions. Note that none of these functions are related to multilevel analysis only.
