A Handbook of Statistical Analyses Using R – Everitt and Hothorn (2006)
Author(s): Brian S. Torvitt; Torsten Hothorn
Publisher/Date: Chapman & Hall/2006
Statistics level: Intermediate to advanced
Programming level: Intermediate
Overall recommendation: Highly recommended
A Handbook of Statistical Analyses Using R addresses a list of several common statistical analyses in great detail. Over a course of 15 chapters, the handbook takes the reader from an introduction to R through a discussion of statistical inference, to linear and logistic regression, tree analysis, survival analysis, longitudinal analysis, meta-analysis, factoring, scaling, and clustering. The handbook has a peer-reviewed journal style that will be familiar to academic researchers and each chapter stands on its own. This approach makes the text exceptionally useful in the academic setting as a professor can distribute and assign the first chapter of the book to her Research Methods 101 course; the final chapters on scaling and dimensionality to her Psychometrics Methods course; the last chapter on clustering to her Marketing Research course; and require the entire book for her graduate methods course. For custom research shops making the transition to R or who frequently hire new entry level R users, this book will work well as a reference and training manual.
The handbook does show typical first edition flaws. There are sporadic mistakes in grammar such as misspellings and incorrect words. The overall organization of the book is strong, but the chapter level organization is less effective. Each chapter begins with a discussion of all of the datasets used in that chapter and is followed by examples and applications based on those datasets. In chapters where there are several examples, the discussion of the data is too detached from its corresponding example. When the reader reaches the example based on the first dataset they have likely forgotten the relevant details about that data's structure. Grouping the data discussions with the examples they accompanied would have made the example based approach more effective.
State-of-the-art in parallel computing with R: New paper
Importing Generic Function from Other Package: A Tip on Writing a R Package
Visualizing Eigenfactors
These interactive graphics are simply beautiful. And they just so happen to be profoundly informative about the structure of modern science as well. Here’s to the hope that we will see more work from Moritz Stefaner soon that shows how our aesthetic and scientific demands can be met simultaneously.
HT to Infosthetics.
Runing R in the Command Mode in the Window system
MCMCglmm package for R
MCMCglmm is a package for fitting Generalised Linear Mixed Models using Markov chain Monte Carlo techniques. Most commonly used distributions like the normal and the Poisson are supported together with some useful but less popular ones like the zero-inflated Poisson and the multinomial. Missing values and left, right and interval censoring are accommodated for all traits. The package also supports multi-trait models where the multiple responses can follow different types of distribution. The package allows various residual and random effect variance structures to be specified including heterogeneous variances, unstructured covariance matrices and random regression (e.g. random slope models). Three special types of variance structure that can be specified are those associated with pedigrees (animal models), phylogenies (the comparative method) and measurement error (meta-analysis). The package makes heavy use of results in Sorensen and Gianola [2002] and Davis [2006] which taken together result in what is hopefully a fast and effcient routine. Most small to medium sized problems should take seconds to a few minutes, but large problems (> 20,000 records) are possible. My interest is in evolutionary biology so there are also several functions for applying tensor analysis [Rice, 2004] to real data and functions for visualising and comparing matrices.
InDesign: Export Selection to PDF
Controlling margins and axes with oma and mgp
When creating graphs, we're usually most concerned with what happens near the center of our displays, as this is where most of the important information is generally held. But sometimes, either for aesthetics or clarity, we want to adjust what's outside of the box - in the margins, labels or tick marks. The par() function offers several ways to do this and I'll discuss two that deal primarily with spatial orientation - rather than content - below.
The oma, omd, and omi options
New CRAN Task View on HPC
Data Analysis and Graphics Using R – Maindonald and Braun (2003)
Author(s): John Maindonald; John Braun
Publisher/Date: Cambridge University Press/2003
Statistics level: Intermediate to advanced
Programming level: Beginner to intermediate
Overall recommendation: Highly recommended
Data Analysis and Graphics Using R (DAAG) covers an exceptionally large range of topics. Because of the book's breadth, new and experienced R users alike will find the text helpful as a learning tool and resource, but it will be of most service to those who already have a basic understanding of statistics and the R system.
Although the text includes both an Introduction to R section (chapter one) and a discussion of the basics of quantitative data analysis (chapters two through four), these chapters will be most useful as overviews (or reviews for more experienced readers), as they lack the detail required to take a reader from no knowledge of these subjects to a functional understanding. For example, chapter one discusses importing data in .txt and .csv format, but the foreign package is not discussed until chapter fourteen - the final chapter of the book. In practice, .txt data structures are not common enough to justify relegating a discussion of the foreign package to the supplemental materials and a researcher stuck with a .sav or .dbf file would not leave chapter one with enough knowledge to import their data into R.
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