Hi, I am trying to run a zero-one-inflated beta mixed model with my study.

Rational for using this model: The DV is a bounded variable signifying the likelihood a person is going to look to location A (0) or location B (1). The response was given on a slider on multiple trials, and most of the responses fell on or around 1 but there is a small (although present) peak on the 0 as well. As this variable is bounded and J shaped, I recoded the extremes to be e-5 and 1-(e-5). The IVs are categorical and represent the membership to two (gender and political) groups and the levels of dishonesty presented in the scenario.

First Question: Do you think it is an appropriate model (I am not a statistician)?

Secondly, when I run the model something weird happens. This is the model I run (I am using a MacBook Air).

```
zoib_model <- bf(
Newscore ~ honesty_number*Political_group_dummy*Gender_group+
(1|number_participant) + (1|Scenario_Number),
phi ~ honesty_number*Political_group_dummy*Gender_group+
(1|number_participant) + (1|Scenario_Number),
zoi ~ honesty_number*Political_group_dummy*Gender_group+
(1|number_participant) + (1|Scenario_Number),
coi ~ honesty_number*Political_group_dummy*Gender_group+
(1|number_participant) + (1|Scenario_Number),
family = zero_one_inflated_beta()
)
fit <- brm(
formula = zoib_model,
data = total_file_HighICS,
cores = 4,
thin = 4,
iter = 4000
)
```

And this is what runs, until its stops.

```
"Compiling Stan program...
Start sampling
starting worker pid=42318 on localhost:11174 at 16:47:59.584
starting worker pid=42333 on localhost:11174 at 16:47:59.885
starting worker pid=42347 on localhost:11174 at 16:48:00.165
starting worker pid=42361 on localhost:11174 at 16:48:00.488
SAMPLING FOR MODEL '884207f16b9ac795e3a747f16199f39e' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.015805 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 158.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration: 1 / 4000 [ 0%] (Warmup)
SAMPLING FOR MODEL '884207f16b9ac795e3a747f16199f39e' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0.015603 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 156.03 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration: 1 / 4000 [ 0%] (Warmup)
SAMPLING FOR MODEL '884207f16b9ac795e3a747f16199f39e' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0.016704 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 167.04 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration: 1 / 4000 [ 0%] (Warmup)
SAMPLING FOR MODEL '884207f16b9ac795e3a747f16199f39e' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0.017622 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 176.22 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration: 1 / 4000 [ 0%] (Warmup)"
```

Am I doing something wrong? Would assigning different priors change something?

Final question:

I wanted two add priors to the interactions, and wanted to model them after the pilot data. They are all beta destributed (as expected). Do you think it is best to use beta distributions (beta(alpha, beta)) to set priors or have them normal or student t?