Aspect data in linear regression I have a dataset of various ecological variables on which I want to run linear regression. The variables are continuous, but also include aspect data (sun exposure), in grades. My problem is that the aspect values ranges from 0 to 400, with 0=400=north. How can I include "cyclic" values in the regression? Should I keep them as they are, or cos/sin-transform them?
Does anyone of you have experience with these problem? I have had difficulties to find any reference papers...
All ideas and references are welcome and appreciated! I hope I was not too unclear, if you need any further information, I'd be happy to provide, as far as I can.
 A: How you treat it - including whether you transform it in some way - depends on how you expect the aspect to relate to your response.
What is the response and how do you think aspect will tend to affect it?
You may find some value in the discussion here; it's not exactly the same kind of problem, but aspects of it have some potential relevance. 
In particular, if there's a reason to have $\cos$, there may be a point in not just having $\cos$ of the aspect but also $\sin$, and also other harmonics (essentially, a set of orthogonal periodic components).
A: you can write expression in raster calculator:
(Aspect<25) * (Aspect+400)+(Aspect>=25) * (Aspect*1)
it will add 400 grade to all value less than 25 grade but not effect on value equal and greater 25 grade. result will be North class in the end of legend but start point would be northeast.you will never get zero value i your map 
your north class range would be 375g to 425 g in the end of legend
legend would looks like this:
Norheast: 25g to 75g
East: 75g to 125g
southeast: 125g to 175g
south: 175g to 225g
southwest: 225g to 275g
west: 275g to 325g
Northwest: 325g to 375g
North: 375g to 425g
