Introducing Influence.ME: Tools for detecting influential data in mixed models

I’m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, and Manfred te Grotenhuis. 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.

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useR! 2009 acceptance: presenting influence.ME

The organizing committee of the useR! 2009 conference just informed me, that my submission for presenting my extension package influence.ME, has been accepted! Influence.ME is a new R package that I’m currently developing, with the indispensable help of Ben Pelzer and Manfred te Grotenhuis. Although I did not yet introduce influence.ME on this blog, rest assured that I will do so within just a few weeks. Now is time for celebration!
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R-Sessions 32: Forward.lmer: Basic stepwise function for mixed effects in R

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 it, or I may not. Nevertheless, in a ‘forward sense of stepwise’, I think it can be pretty useful as it is. Also, it has an interesting take on the stepwise concept, I think.

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R-Sessions 31: Combining lmer output in a single table (UPDATED)

There are various ways of getting your output from R to your publication draft. Most of them are highly efficient, but unfortunately I couldn’t find a function that combines the output from several (lmer) models and presents it in a single table. lmer is the mixed effects model function from the lme4 package. So, I wrote a simple function that does exactly that.
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R-Sessions 30: Visualizing missing values

It always takes some time to get a grip on a new dataset, especially large ones. The code-books are often as indispensable as they are massive, and not always as clear as one would want. Routings, and resulting and strange patterns of missing values are at times difficult to find.

I found a nice way to plot missing values, using R. Basically, I thought it would be nice to calculate the percentage of missings on each variable, and do so for each year represented in the data. These numbers could be visualized using a levelplot(), which resulted in the graph below.

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R-Sessions 29: Running R-Project twice on Apple Mac OS X

Working with statistics can be quite time consuming. As anyone working with relatively advanced models and large amounts of data knows, especially the waiting can be excruciating. Your statistical software is locked up while crunching those numbers, while you’d actually prefer to run some minor procedures, such as post-estimations, testing some loops, or simply displaying the output of a previously estimated model. With Apple’s Mac OS X you now can run R-Project twice, making the most of your dual core processor. … Continue Reading

A Very Small Step for Read.isi …

My R-Project package Read.isi is named in the R Newsletter. Yes, I know, this is just a very small step for my package Read.isi, especially because (almost?) every new package is named. Nevertheless, I’m proud of it.
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R-Sessions 28: Impressive R Speeds

Yesterday, I received my new Apple MacBook. It’s running a Core 2 Duo at 2.4 Ghz and it’s fast. Really fast!

Apparently, it’s very cool to show of the speed of R-Project on your system. Optimized .DLL files help to speed up your R on Windows systems (and possibly other systems as well) with respect to matrix transformations, which has led to enormous speed increases. So, let’s perform a speed-test of our own.
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The R-Sessions Forum: Introducing a new forum on R-Project

UPDATE: Due to lack of interest, and the availability of other excellent sources for interaction regarding R, I decided to close the R-Sessions forum, which now is no longer available. Questions on R can also be send to the author of this blog, by placing comments below.

Today, I introduce the new R-Sessions Forum: a new forum on R-Project and statistics in general. This new forum is closely integrated with Curving Normality, but has its own dynamic. The R-Sessions Forum aims at providing a flexible stage for visitors interested in discussing both R-Project and general statistics.

Topics & Features

The new R-Sessions Forum has several interesting features and covers many topics. Amongst the new topics covered are integration with the R-Sessions on Curving Normality, a section for general question on R, a section for general statistical questions, package specific questions, R development, and of course we have a pub for your general chatter. The integration with the R-Sessions is intended to give more dynamic ways of interacting with the Curving Normality web-site. The other topics are more general in nature and provide ample opportunity to pose your own questions and discuss those with other participants.

Do we really need this forum?

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R-Sessions 27: Text Editors for R: Textmate

R-Project works best with a good text editor that is well integrated with R-Project. This edition of the R-Sessions will focus on TextMate, a paid application marketed as ‘The Missing Editor for Mac OS X’.

Designed explicitly for use by programmers on Mac OS X, TextMate makes a promising first impression. The interface looks very clean, text is rendered perfectly, and syntax colouring is provided for quite a large number of programming languages. Also, the colouring of the syntax looks very nice, by the use of light colours that don’t interfere with reading the text.

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