Edit: I just read a related post (How to include $x$ and $x^2$ into regression, and whether to center them?) which mentions that centering a variable creates a new variable.
However, as the comments point out, taking the logarithm of negative values doesn't make sense (stupid me for not thinking this through) so I changed the first option.
I'm working with a multiple regression where log transforming a few of my predictors drastically improves the model assumptions. However, this improvement is for un-centered data and centered data on the mean would be much more interpretable.
I understand that centering data does not affect the distribution (it only shifts the mean), and would like to ask when I should center my data. Is there any general rule of thumb?
1] Do I center the predictor about its mean first and then search for a different transformation which improves model assumptions should they be violated?
2] Do I perform the log transformation first, then center by the mean of these log transformed values? How would this change model interpretation compared to option 1]?