# 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.

• Aug 16 '19 at 2:12
• Thanks! I'm curious - how did you find my question from 8 years ago and remember to put this there? I mean, I appreciate it, but I'm amazed! Aug 16 '19 at 11:31
• From time to time (often) I just go through, say som old questions from some user, and upvotes, edits, flags, comments as needed, or maybe even answering ... and I think this site can become more useful with crossreferencing, and such comments are stored as crossreferences. Aug 21 '19 at 12:34

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.