Hello!

I work on a multinomial multilevel model of electoral preference with brm (I use the MRP method). With the model I generate a probability prediction on a database with 480 rows (let’s call it **data_est**).

In a first attempt, I made the prediction with the function

“predict(model, ndraws=2500,newdata=data_est,summary=T)”

and I obtain a table with the probabilities for each row of each category (P(Y=partyA) , P(Y=partyB),…). With them, I took the mean as a point estimator and the quantiles (.025 and .975) as the “credibility interval”, but they are very wide.

Seeing this post

I made a prediction now with the “fitted” function in the “data_est” database and the summary gives me the estimate (“Estimate.P(Y=partyA)”, “Estimate.P(Y=partyB)”, . …), the error (“Est.Error.P(Y=partyA)”, “Est.Error.P(Y=partyB)”, …) and the quantiles corresponding to each category (“Q2.5. P(Y=partyA)” and “Q97.5.P(Y=partyA)”, …) per row.

i get probabilities for every category (column) for each “individual” (row), estimates and quantiles, something like…

```
Estimate.P(Y=pA) Est.Error.P(Y=pA) Q2.5.P(Y=pA) Q97.5.P(Y=pA) ...
<dbl> <dbl> <dbl> <dbl>
1 0.0742 0.0337 0.0177 0.153
2 0.0712 0.0320 0.0161 0.149
3 0.0632 0.0284 0.0133 0.128
4 0.120 0.0504 0.0288 0.231
5 0.0851 0.0367 0.0196 0.170
```

…

I have a question, to obtain a “general” estimator, is it correct to only obtain the (weighted) average? Something like

partyA=mean(“Estimate.P(Y=partyA)”)

or is there something else to do? And for the quantiles? (low_partyA=mean(“Q2.5.P(Y=partyA)”))?

or am i doing something wrong?

with “predict” function only obtain the estimated by row (not quantiles or “error”), and with this column i get mean and quantiles (in my first attempt like paul.buerkner said in the post above), i get someting like this:

```
party low (.025) mean up(.975)
A .04 .08 .16
B .26 .40 .58
```

…

but this values are to wide.

For “fitted” function, if i summarise Estimates.P(Y=pA) obtain a similar value like my mean above, but my question is if it´s ok make the same (mean value) for Q2.5.P(Y=pA) and Q97.5.P(Y=pA)? because if i do it i get this:

```
party low (.025) mean up(.975)
A .06 .08 .11
B .34 .40 .46
```

…

and this is more closed.

Thank you all.