I’m sorry for not being very clear in my question. I’ll try to explain it a bit better here.
My confusion lies in how to set the priors for my categorical predictors. When the variable is numeric/continuous, I find it quite straight forward to set a prior. But what I don’t understand yet is how to set priors on a variable when the categories are A,B,C,D,E,F.
- Are the categorical variables treated by brms as numeric, in that the category “A” becomes “1” (B=2, C=3, …)?
- Is the prior on a categorical variable equivalent to the probability of receiving category X versus all other categories?
- Are the priors set on all of the categories as a whole (A-F) or on each individual category level (e.g. category C of variable X)? For example, say we have the variable “school” with 4 different schools. Do I set a prior for each of the 4 schools? And if so, what does the prior actually do? For example, if I set a prior on school 1 that was
normal(0,1), does this mean that the coefficient of school 1 has a normally distributed mean probability of 0 with a sd of 1?
Hope I’ve used the right lingo in the right places…