I’m fitting a logistic regression model with rstanarm, and for some predictors I have prior knowledge, for others not. Can I mix both priors in the `prior`

-argument?

Here’s an example of how I specified my model, with `agitation`

being continuous and `dementia`

a 3-level factor (low, middle and high dementia severity). Now I know from literature research that a middle severity in dementia is associated with a 2fold higher chance of falling, and high severity has an odds ratio of 3.5. I have no prior knowledge about the other two predictors.

This is how I would specify my priors. Since the location is on the log-scale, I would use log(2) and log(3.5) as location parameter, while I leave the other two priors at default (i.e. 0):

```
stan_glm(
falls ~ age + dementia + agitation,
data = d,
family = binomial("logit"),
prior = normal(
location = c(0, log(2), log(3.5), 0),
scale = c(NULL, NULL, NULL, NULL)
)
)
```

Is this correct? Do I need to set `autoscale = FALSE`

? And what about the scale parameters?

I have tried to google for possible answers, but maybe I have used the wrong search terms…

Best

Daniel