Maybe this is more of a statistical question than an R question, but I am going to ask it anyway :) Cortisol and Testosteron are known to interact in the body, and some literature suggest that especially the ratio between the two is a good predictor. So I want to add the ratio predictor (y~cort/test) to a glm, and later a multilevel model, however I have no experience with ratio variables.
Intuitively, I would think that a ratio is very similar to an interaction effect, only with an inverse scale for the second term (cort* 1/test). However, I found that even more than with normal interaction effects, the scaling of the variables becomes important. Furthermore there is the obvious problem (which is not too big of a problem in my case), of values that are close to zero on the second term. So what is an appropriate way to incorporate a ratio-predictor? Any tips on R procedure/package that I could use, or any other experiences with ratio-predictors are welcome. -- View this message in context: http://www.nabble.com/a-ratio-variable-predictor-tp21069805p21069805.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
