Hello everyone, I’m trying to figure out how to include a polynomial in one of my models (related to a previous post), and it seems to have some strange behavior when it interacts with scale().

I have a predictor that goes from 1:10, and I would like to model both linear and quadratic effects of this predictor. When using poly(variable, 2) in the model, it seems to work all right-- the model converges, conditional_effects gives out reasonable predictions, etc. However, the estimated coefficients are quite large, since the poly() orthogonalization makes the predictor values much smaller than their original values.

I have tried to scale the predictors using scale(poly(variable,2)) in the model, and this also converges and now gives coefficients on par with my other predictors (e.g. between 0 and 1, ish). However, when I try to use conditional_effects() to get predictions, I get this error:

```
Error in poly(position, 2) :
'degree' must be less than number of unique points
```

From looking on other forums, it seems like this usually happens when the degree of the polynomial is unreasonably high, but here it is only 2. Also, since brms will happily run the model and converge, I am guessing the problem is with conditional_effects(). Any thoughts?

Note: my model is a logistic one; based on my earlier model it looked like conditional_effects() has some functionality for converting back to the original scale of the variables, so perhaps this is interfering in some way?

- Operating System: Mac OS Catalina
- brms Version: 2.15.0