# Logistic regression with directional data as IV

I am looking for good references on using directional data (measure of direction in degrees) as an independent variable in regression; ideally, it would also be useful for hierarchical nonlinear models (the data are nested). I am also interested in directional data more generally.

I have found a text by Mardia, which I am going to get, but wondered if there were good articles.

I am more interested in practical articles about how to deal with this type of data than in theorems and proofs, or formal statements of distributions and such. Thanks

UPDATE I have got the Mardia text, which is quite comprehensive. After some more reading, I may be back with more questions.

-
I would suggest applying a transform which deals with periodicity. i.e. $\lim_{x \to 360} f(x) = f(0)$. An easy option is to take the sin and cos, and put them both as covariates in the model.