I have data that with my best efforts cannot be transformed into something resembling a normal distribution. Or more specifically, the residuals of the linear model I want to run on the data is in no way normally distributed. (I've done exploration of different transformations using the bestNormalize package in R).
My outcome variable is continuous and the covariates are either one dichotomous variable or two dichotomous variables and their interaction.
One of the complicating factors is that the data is nested, i.e. there are multiple observations per each subject in the data.
I was, therefore, searching for a non-parametric version of a linear mixed model. I ran into this very informative post by Jonas Lindeløv, which states that non-parametric tests are linear models on the signed ranks of the outcome variable of interest. He shows in his post that you just first calculate the signed ranks of your data (formula is in the legend of the figure in the post).
So this made me think I could just simply calculate the signed ranks of my outcome variable and run a simple linear mixed model.
My questions now are:
- Can this method really be simply extended to linear mixed models?
- Should you now regard the signed ranks formula as a type of transformation? Should, therefore, the residuals of your linear model on the signed ranks be assumed to be normally distributed? (I know non-parametric test don't have this assumption, but this method does allow you to look at the distribution of the residuals...).
- Am I overlooking other very obvious solutions for nested non-normally distributed data?