Continuing in my exploration of the Russell 2000 (Russell 2000 Softail Fat Boy), I thought I would try to approach the topic with a low volatility paradox mindset. Since 2005, beta of the Russell 2000 compared to the S&P 500 has exceeded 1.2 ...

Model level fit summaries can be tricky in R. A quick read of model fit summary data for factor levels can be misleading. We describe the issue and demonstrate techniques for dealing with them.When modeling you often encounter what are commonly called categorical variables, which are called factors in R. Possible values of categorical variables Related posts:

If you’ve ever written code that generates a whole whack of files, you may have came across the following problem when processing them. Using a naming convention wherein files are numbered will gum up any ordering which is based on string sorting (ls, for example). What you end up with is something like this: Which

This post demonstrates the simplest Species Distribution Model based on logistic regression for presence/absence data. I heavily simplified the example from Kéry (2010): Introduction to WinBUGS for Ecologists, Chapter 20.Read more →

Dispersal is a key process in many domains, and particularly in ecology. Individuals move in space, and this movement can be modelled as a random process following some kernel. The dispersal kernel is simply a probability distribution describing the distance travelled in a given time frame. Since space is continuous, it is natural to use