Designing simulation studies using R

Here are the slides from my presentation at this afternoon’s Quant. Methods brown bag. I gave a very quick introduction to using R for conducting simulation studies. I hope it was enough to get people intrigued about the possibilities of using R in their own work.

The second half of the presentation sketched out a quick-and-dirty simulation of the Behrens-Fisher problem, or more specifically the coverage rates of 95% confidence intervals using Welch’s degrees of freedom approximation, given independent samples with unequal variances. Here is the complete code. As I mentioned in the talk, there’s lots of room for improvement. The main point that I was trying to illustrate is that simulations have five distinct pieces:

1. a data generating model,
2. an estimation procedure,
3. performance criteria,
4. an experimental design (parameter values and sample dimensions), and
5. analysis and results.

It is useful to write simulation code that reflects the structure, so that it is easy for you (or other people) to read, revise, extend, or re-run it. And then post it on your blog.