SAS and R

We'll get back to code examples later this week, but wanted to let you know about an R package with updated functionality in the meantime.

The appropriate analysis of sample surveys requires incorporation of complex design features, including stratification, clustering, weights, and finite population correction. These can be address in SAS and R for many common models. Section 6.8 of the book provides an overview of these methods.

Improved support for these designs in R is now available in the survey package. This includes support for multivariate analysis (factor analysis [section 6.7.2] and principal components), parallel processing on multicore computers, and access to database-backed design objects (which are particularly useful for large datasets).

In addition to the updates, package author Thomas Lumley has made the table of contents and preface available for his forthcoming book. Other helpful resources include Alan Zaslavsky's summary of survey analysis software.