Recently, I was contacted with an question about R code. A befriended researcher was working with nested data, which was unbalanced. He was working with data in a ‘long’ format: all observations nested within the same group had the same identification number. But, the number of observations in each of the groups differed (hence: unbalanced [...]
Posts under ‘R-Sessions’
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 [...]
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 [...]
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.
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.
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! I tested it with using R-Project, doing some timings on matrix transformations.
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.
The R-Sessions Forum: Introducing a new forum on R-Project
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 , 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.
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.
R Sessions 26: Text editors for R: Internal editor on OS X
Since R-Project is essentially syntax based, one needs a good text editor to write some code before it is executed in R. And, since we are all writing high quality code, we need a high quality text editor. This is the first in a series on text editors for using with R-Project on MacOSX.
The first editor to look at, is the internal one. The Mac OS X version of R-Project comes with quite a strong, although basic, text editor.
R-Sessions 25: Book – Mixed Effects Models in S and S-PLUS (Pinheiro & Bates, 2000)
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.
Influence.ME: an R package providing tools for detecting influential data in mixed models.