# Notes on shrinkage & prediction in hierarchical models

**Ecology in silico**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Ecologists increasingly use mixed effects models, where some intercepts or slopes are fixed, and others are random (or varying). Often, confusion exists around whether and when to use fixed vs. random intercepts/slopes, which is understandable given their multiple definitions.

In an attempt to help clarify the utility of varying intercept models (and more generally, hierarchical modeling), specifically in terms of shrinkage and prediction, here is a GitHub repo with materials and a slideshow from our department’s graduate QDT (quantitative (th)ink tank) group.

For fun, I’ve included a simple example demonstrating the value of shrinkage when trying to rank NBA players by their free throw shooting ability, a situation with wildly varying amounts of information (free throw attempts) on each player. The example admittedly is not ecological, and sensitive readers may replace free throw attempts with prey capture attempts for topical consistency. Many if not most ecological datasets suffer from similar issues, with varying amounts of information from different sites, species, individuals, etc., so even without considering predation dynamics of NBA players, the example’s relevance should be immediate.

Spoiler alert: Mark Price absolutely dominated at the free throw line in the early nineties.

**leave a comment**for the author, please follow the link and comment on their blog:

**Ecology in silico**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.