# Analyzing the interactional term in brms

Hi everyone. I just learned how to do the Bayesian ordinal logistic regression model after reading Bürkner & Vuorre’s (2019) tutorial. I followed their steps, tried different models, compared them, and found that the following model with the interactional terms has the best fit:

model<-brm(formula=Scale~Condition*Language+(1|Subject)+(1|Item),data=data,family=cumulative(“probit”))

Scale is the Likert scale from 1 to 9
Condition is the categorical variable with four levels: Condition_a (used as the reference level), Condition_b, Condition_c, Condition_d
Language is the categorical variable with two levels: Language_English (used as the reference level) and Language_Spanish

Honestly, I don’t know how to further analyze the interactional term in Bayesian modelling. I mean, if I would like to treat Condition as the moderator, and see at each level of Condition whether Language has an effect, what should I do here?

I saw a similar post on this forum where someone suggested using hypothesis() function. Honestly, after reading that post and the usage of hypothesis(), I am not quite sure that I fully understand it.

For instance, if I use

hypothesis(model, (“Condition_b + Condition_b*Language_Spanish = 0”))

The result looks like this:

Hypothesis Tests for class b:
Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
3.88 0.21 3.48 4.3 NA NA *

What is being tested here? How do I interpret these numbers?

Howdy. You can start by using `conditional_effects()` to view plots of the outcome at various levels of the predictors. See `conditional_effects()` in the brms manual for examples of using it to view interactions.
You can do the same thing yourself by creating datasets with the predictors at various values and then using `fitted()` to view the mean values of the outcome at each combination of predictors.
I’ve never used `hypothesis()`, so I can’t help you there.