Hello all,

I am building a multilevel ordinal logistic regression model for some data with 1-7 Likert scale dependent variable (Rating) and 3 categorical independent variables (X = 2 levels, Y = 2 levels, Z = 3 levels).

The model is: `brm(Rating ~ X * Y * Z + (1 + X * Y * Z | Item) + (1 + X * Z | Subj), data =df , family = cumulative("logit"), prior = set_prior("normal(0,2)", class = "b"), cores = 4, warmup = 2000, iter = 6000)`

X and Y are sum coded and I have set custom contrasts for Z (levels = n, e, and l) using the hypr package to test particular comparisons in factor Z: `hypr(None_vs_Early = n~e, None_vs_Late = n~l, Early_vs_Late = e~l`

```
hypr object containing 3 null hypotheses:
H0.None_vs_Early: 0 = n - e
H0.None_vs_Late: 0 = n - l
H0.Early_vs_Late: 0 = e - l
Hypothesis matrix (transposed):
None_vs_Early None_vs_Late Early_vs_Late
e -1 0 1
l 0 -1 -1
n 1 1 0
Contrast matrix:
None_vs_Early None_vs_Late Early_vs_Late
e -1/3 0 1/3
l 0 -1/3 -1/3
n 1/3 1/3 0
```

When I look at the output from describe_posterior though, all of my CIs for Z are super wide.

```
Parameter Median CI CI_low CI_high ESS Rhat
Intercept.1. -4.11811 95 -4.5132 -3.742 2229 1
Intercept.2. -2.94517 95 -3.3151 -2.594 1996 1
Intercept.3. -2.12786 95 -2.4858 -1.781 1918 1
Intercept.4. -1.24716 95 -1.5938 -0.897 1862 1
Intercept.5. 0.08861 95 -0.2466 0.443 1830 1
X 1.31585 95 0.8674 1.772 3887 1
Z-nVe 0.60277 95 -1.6509 2.850 10962 1
-nVl 0.58116 95 -1.7331 2.755 11061 1
-eVl -0.00787 95 -2.2469 2.224 10937 1
Y -0.24842 95 -0.8779 0.402 2203 1
X.Z-nVe -0.19365 95 -2.4201 2.250 11564 1
.Z-nVl -0.26712 95 -2.6509 1.965 11745 1
.Z-eVl -0.07948 95 -2.4587 2.197 11635 1
X.Y 0.89522 95 0.0615 1.737 4043 1
-nVe.Y -0.31110 95 -2.5902 2.027 12310 1
nVl.Y 0.00225 95 -2.3935 2.256 12311 1
eVl.Y 0.32271 95 -1.9900 2.624 12083 1
X.Z-nVe.Y -0.04146 95 -2.4771 2.237 13391 1
X.-nVl.Y -0.39658 95 -2.6858 1.949 13435 1
X.-eVl.Y -0.38375 95 -2.7340 1.986 12792 1
```

I tried changing the contrasts by dropping 3rd comparison of “e vs l” in factor Z and then I ran the model again and got much more sensible values for my Z coefs given what the data acctually look like:

```
Parameter Median CI CI_low CI_high ESS Rhat
Intercept.1. -4.0966 95 -4.4872 -3.7167 1777 1
Intercept.2. -2.9268 95 -3.2950 -2.5698 1632 1
Intercept.3. -2.1144 95 -2.4784 -1.7701 1570 1
Intercept.4. -1.2377 95 -1.5756 -0.8827 1557 1
Intercept.5. 0.0880 95 -0.2465 0.4378 1549 1
X 1.3051 95 0.8484 1.7474 2681 1
Z-nVe 0.5773 95 0.3594 0.8199 8474 1
Z-nVl 0.5813 95 0.3424 0.8128 8339 1
Y -0.2585 95 -0.9052 0.4093 1033 1
X.Z-nVe -0.2037 95 -0.6316 0.2153 9453 1
X.Z-nVl -0.2745 95 -0.6692 0.0964 11942 1
X.Y 0.8777 95 0.0451 1.7566 2069 1
Z-nVe.Y -0.3194 95 -0.7726 0.1039 8596 1
Z-nVl.Y 0.0122 95 -0.4413 0.4510 7940 1
X.Z-nVe.Y -0.0553 95 -0.8372 0.7428 8963 1
X.Z-nVl.Y -0.4382 95 -1.1803 0.2715 10730 1
```

Does anyone have any idea what may be going on here? I’m new to running these kinds of models and I’ve never tried setting these kinds of contrasts before.

- Operating System: Linux Mint 19
- brms Version: 2.10.0