Hello,

I would like to know, how to improve the predictive accuracy of my model for one factor. When I plot the posterior draws against all data, then the model seems to fit quite well:

However, when I take out the levels of one factor, the accuracy of predictions is quite weak:

This is position == “Initial”:

And this is position == “Final”

Any ideas, how to improve my model fit?

I am modeling the absolute duration of the eye gaze with a skew_normal family. I have three two-level factors load, length and position and I would like to examine the impact of the three-way interaction between the factors on the duration of the eye gaze. This was a repeated measures design where a number of participants saw sets of items in between conditions. The trial index track the trials as the participants proceeded with the experiment. This is how my model looks

```
myAbsModel <- brm(GazeDurAbs ~ sentenceLength * position * load +
(1 + sentenceLength * position * load + scaled_trialIndex | participantID) +
(1 + sentenceLength * position * load + scaled_trialIndex | item),
data = test,
family = "skew_normal",
iter = 10000,
thin = 5)
```

Great thanks for your help!