Logs
The “R Logs” show how to reproduce the results of (practically) all the analyses in my lecture notes on Generalized Linear Models. The material is organized by Chapters and Sections using exactly the same numbering system as the notes, so section 2.8 of the logs deals with the analysis of covariance models described in section 2.8 of the notes. Use the Table of Contents to jump directly to any section.
The transcripts are formatted versions of R input and output. The text boxes set in a typewriter font contain R function calls, followed by the resulting output. You can tell the input lines because they are preceded by R’s prompt, a greater than sign (>), or the continuation prompt, a plus sign (+). The rest of the text set in the standard font represents comments or annotations, except for references to R functions or packages, which are also set in a typewriter-style font.
The best way to use these transcripts is sitting by a computer, typing in the code as you read along, probably with a printed copy of the notes by the side. I also recommend that you try to answer the few questions and exercises posed along the way.
While interactive use is probably good for learning, for more serious work I recommend that
you prepare an R script collecting all your code and then run it, ideally sending the
output to a file.
Even better, use rmarkdown to combine a narrative written using Markdown with R input
and output. These logs were all produced using rmarkdown, and you will find the source
code on GitHub, just follow the link near the top-right of every log.
The purpose of these notes is to illustrate the use of R in statistical analysis, not to provide a primer or tutorial. I have, however, written a short tutorial that you can find here. Please consult the R online help and documentation for more information.
To get started download R for free from the R project home page at www.r-project.org. You will be asked to select a mirror site and will then be able to download binaries for Linux, macOS, or Windows.
I recommend that you also download R Studio, an excellent Integrated Development Environment
(IDE) for R, from it’s home at RStudio.com. This is
not strictly necessary, but it makes editing and running R as well as rmarkdown scripts
much easier. R Studio is free and works on Linux, macOS, and Windows.
P.S. The note on robust standard errors in R is here.