I am new to `brms`

, trying to figure out its behaviour in details. I was trying to run essentially an anova-like model with random effects (anova-like in the sense that all IVs are factors). For example,

```
brm(DV ~
A * C +
B * C +
(1|item) +
(1|participant),
...
```

`conditional_effects()`

and `hypothesis()`

are truly amazing analytic tools. However, when you deal with factors and, in particular, their interactions, that’s a bugger… It’s really hard to help students wrap their head around it. In addition to that, I am a bit stubborn and I really despise when people, for practical purposes, re-level their factors to figure out what contrasts are significant. There is no need for that. In frequentist framework, one could make use of Wald’s test etc. Here, I sense, the two functions can be of much use, but still one need to be careful around the intercept. All other levels are relative *change* to that reference.

If one have a simple dummy variable, `0 + Intercept + Dummy + ...`

is of help (another nice one!). But when you have factors with more levels and their interactions, things are far from straightforward.

What would be **the brms** way of making this less painful? Running another model without intercept and then use `hypothesis()`

? Something else?

- Operating System: macOS
- brms Version: 2.14.4

Thanks!