Does anyone have any suggested papers/vignettes for how to tackle a regression situation in which both predictor and outcome variables are non-normally distributed, in a way that could be applied in brms? E.g., both may be best represented as a beta-distribution (alongside other categorical or normally distributed predictors), or more simply, one predictor may be beta distributed but the outcome variable is not? I have gotten to grips with a beta-distributed outcome variable already, but not how to run non-normal predictors.
I can’t quite tell if something like this is what is being done in “Estimating Non-Linear Models with brms” (not a beta distribution, but as an example of the general principles). In this case it looks like you would put some sort of function alongside the predictor into the regression equation.
Any suggestions would be much appreciated! I also understand that people may not like the term ‘outcome’ and ‘predictor’ variable, but I am just trying to make obvious what side of the regression equation the non-normally distributed variable would be.
Please also provide the following information in addition to your question:
- Operating System: MacOS 10.12.6
- brms Version: Most recent version