Index of the R-Sessions

The R-Sessions are a series of blog entries on using R. A large part consists of an R-manual I once wrote. Other posts include some tricks I found out, as well as entries detailing functions and packages I wrote for R. The series already entails over forty posts, so I decided to create an index. It is found below. On a fixed page on this website (www.rensenieuwenhuis.nl/r-project/r-sessions-index/) I will continue to update this index with new editions of the R-Sessions.

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R Sessions 33: Select (nested) observations with equal number of occurences

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 data).

He asked me for a piece of code that creates a subset of the data that is balanced, i.e. all observations that are nested within equally sized groups. Or, as an alternative, all observations nested within groups with at least a minimum number of observations.

I solved it the quick and dirty way, and the solution involves creating additional variables, a new data.frame, and merging. It sure can be done much prettier, but it works.

So, I share it below:
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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

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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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.
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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.
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R-Sessions 23: Book: Data Analysis Using Regression and Multilevel/Hierarchical Models — Gelman & Hill (2007)

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman is known for his expertise on Bayesian statistics. Based on that knowledge he wrote a book in multilevel regression using R and WINbugs. This book aims to be a thorough description of (multilevel) regression techniques, implementation of these techniques in R and bugs, and a guide on interpreting the results of your analyses. Shortly put, the books excels on all three subjects.
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R-Sessions 22: Book: Introductory Statistics with R — Peter Dalgaard (2002)

Introductory Statistics with R

Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen in Denmark, and a member of the R-Project Core Development team. Also, he is an active participating and respected member of the R-help mailing-list. Based on these experiences, he set to write an introductory book on statistics and R.

The book start with relatively simple topics, easily working toward more complex statistical problems. Central techniques that are covered are analysis of variance and regression. Starting with bivariate analyses, multivariate analyses of both types are discussed to a high extent. Several types of linear (regression) models are introduced, covering polynomial regression, regression without an intercept, interactional model, two-way ANOVA with replication, and ANCOVA. A separate chapter focusses on logistic regression. Moreover, in many ways the equivalence or parallels of regression and ANOVA are discussed. Thereby, a greater understanding of the (differences between) techniques is stimulated.
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