Please also provide the following information in addition to your question:

- Operating System: Windows
- brms Version: GitHub version

I just recently learned about the monotonic effect of the ordinal predictor. In the manuscript, the examples are for a linear response.

I need to apply it to an ordinal outcome in repeated data and wrote the below syntax

```
getprior<-get_prior( bodn ~ mo(chf)+1|id,family=cumulative(),
data =bodn)
fit_bodn<-brm(
formula = bodn~ mo(chf)+1|idno,family=cumulative(),
data =bodn,prior=getprior)
```

But, I just got the results for intercepts not for the monotonic effect.

```
Group-Level Effects:
~idno (Number of levels: 898)
Estimate Est.Error l-95% u-95% Rhat
sd(Intercept) 4.44 0.15 4.16 4.75 1.00
sd(sevchf) 1.06 12.93 0.36 41.91 1.00
cor(chf) -0.01 0.58 -0.95 0.95 1.00
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1] -12.26 0.57 -13.43 -11.21 1.00 1399 2691
Intercept[2] -8.76 0.32 -9.41 -8.17 1.00 1066 2270
Intercept[3] -6.14 0.22 -6.57 -5.72 1.00 703 1945
Intercept[4] -3.91 0.18 -4.26 -3.56 1.01 548 1452
Intercept[5] -2.10 0.17 -2.44 -1.77 1.01 495 1282
Intercept[6] -0.29 0.16 -0.61 0.03 1.01 454 1107
Intercept[7] 1.22 0.17 0.90 1.56 1.01 472 1185
Intercept[8] 2.84 0.18 2.50 3.20 1.01 546 1116
Intercept[9] 4.42 0.19 4.06 4.79 1.01 662 1762
Intercept[10] 5.97 0.22 5.55 6.39 1.01 814 1714
Intercept[11] 7.72 0.26 7.22 8.23 1.00 1126 2207
Intercept[12] 9.82 0.34 9.16 10.48 1.00 1610 2240
Intercept[13] 11.90 0.48 10.99 12.87 1.00 2381 2930
Intercept[14] 14.86 0.98 13.21 17.02 1.00 4245 3347
Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
```

What I am doing wrong here? How should I model the ordinal response with monotonic ordinal predictor n repeated data?

Thanks