Say I simply model how two categories (`group`

) predict an ordinal outcome (coded as integers -3 to 3) using a latent probit model:

```
# Data
library(brms)
D = data.frame(
response = ordered(sample(-3:3, 50, replace=T)),
group = rep(c('A', 'B'), each=25))
# Fit
fit = brm(response ~ group, D, family=cumulative('probit'), chains = 1)
```

The parameter estimates (`fixef(fit)`

) are:

```
Estimate Est.Error Q2.5 Q97.5
Intercept[1] -1.5296313 0.4675201 -2.50225569 -0.6709311
Intercept[2] -0.3400179 0.4008245 -1.11796242 0.4344084
Intercept[3] 0.1475556 0.3957674 -0.61386887 0.9406194
Intercept[4] 0.7612172 0.4036700 0.01900015 1.5740368
Intercept[5] 1.2638932 0.4173757 0.49971783 2.1210693
Intercept[6] 2.0226779 0.4736091 1.12749921 2.9540043
groupB 0.6102738 0.5117214 -0.36214454 1.6832346
```

Is there a way to get the “absolute” posterior for the latent group `A`

and `B`

on the scale of the response variable and not as a contrast to the other group? I.e., something similar to doing `intercept + groupB`

for group B in a normal model. **I am particularly interested in doing a one-sided test on each of the groups whether they rate greater than 0**. My intuitions fail me a bit whether this is even possible/meaningful since response-zero is somewhere in between the latent thresholds `Intercept[3]`

and `Intercept[4]`

.

- Operating System: Windows 10
- brms Version: 2.7