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 …
R-Project
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.
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.
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 prooud of it.
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.
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-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.
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.
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.
For those who have theoretical knowledge on statistics and regression techniques, and who want to learn to use R-Project to analyze some data, John Fox wrote just the book.
The introductory chapter shows the most basic aspects of R-Project. Halfway this chapter the reader finds himself analyzing real data using regression techniques. The following chapters introduce the reader to other aspects of the analytical process: reading data into your statistical program, exploring the data and performing some bivariate tests. Then, three full chapters are devoted to regression techniques. While working on practical examples, the reader is introduced to more fundamental aspects of the R-Project software where needed.
