Hi, I’m trying to fit a basic unbalanced multivariate outcomes model in brms that I can then build upon. These data are from a behavioral paradigm (Stroop). The paradigm has two trial events/conditions: congruent and incongruent. However, I only examine responses to correct trials, which means that the data are unbalanced because people commit errors.

Here is an example of how the data might look. There are rows with measurements for one event (e.g., congruent) but not the other event (incongruent). (making up data for example…)

id: participant ID

trial: trial number… 1 = first trial, 2 = second trial, etc.

congruent: response for a congruent trial

incongruent: response for an incongruent trial

NA = empty

id | trial | congruent | incongruent |
---|---|---|---|

100 | 1 | 300 | 350 |

100 | 2 | 310 | 345 |

100 | 3 | 305 | NA |

100 | 4 | 310 | NA |

101 | 1 | 300 | 350 |

101 | 2 | 310 | 345 |

101 | 3 | NA |
340 |

102 | 1 | 300 | 350 |

102 | 2 | 310 | 345 |

102 | 3 | 305 | 350 |

102 | 4 | 306 | 346 |

I have data on about a hundred participants and a few hundred trials of each condition for everyone.

If I had the same number of observations for congruent and incongruent trials, I could model the data using the following…

```
brm_fit <- brm(
bf(congruent ~ 0 + (1+trial|p|id)) +
bf(incongruent ~ 0 + (1+trial|q|id)),
data = df_wide,
chains = 4,
cores = 4,
iter = 1000)
```

But, when I try to model the unbalanced data, brms throws out the rows with NA. How can I go about running this model with unbalanced observations?

Thank you very much for any insight!

- Operating System: OS X 10.14.16
- brms Version: 2.9.0