# How can I found the appropriate family to fit my data using brms?

**URL:** <https://discourse.mc-stan.org/t/how-can-i-found-the-appropriate-family-to-fit-my-data-using-brms/40919>\
**Category:** brms\
**Tags:** brms, rstan\
**Created:** [February 11, 2026, 1:03pm UTC](https://discourse.mc-stan.org/t/how-can-i-found-the-appropriate-family-to-fit-my-data-using-brms/40919 "2026-02-11T13:03:41Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![dmaria](https://avatars.discourse-cdn.com/v4/letter/d/9fc348/32.png) [@dmaria](https://discourse.mc-stan.org/u/dmaria)\
**Post date:** [February 11, 2026, 1:03pm UTC](https://discourse.mc-stan.org/t/how-can-i-found-the-appropriate-family-to-fit-my-data-using-brms/40919/1 "2026-02-11T13:03:42Z")

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**Hello, everyone. I have a set of data from dose-response effect (without saturation point), but I don’t know how to fit it with appropriate family.**

I have values that follows a log distribution, however, there are some negative values, and the Log family doesn’t work.

I’m trying to use 2 set of priors since from data the curves from one group are diff (one group starts reacting at the lowest concentration, while the other only at the highest). I have different bees and whitin each bee I record different glomeruli

```no-highlight
fit3 <- brm(Signal ~ mo(OConc_ord) * Group + (1|Bee:GloTag), 
            data = df,
            prior = c(prior_nurses,
                        prior_foragers),
            chains = 4,
            iter = 2000,
            warmup = 1000,
            cores = 4,
            seed = 123,
            sample_prior = TRUE,
            family = gaussian(log))

```

code\_to\_run\_your\_model(if\_applicable)

Thanks!!!

:D

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<div class="post-metadata">

**Author:** ![jsocolar](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/jsocolar/32/2486_2.png) [@jsocolar](https://discourse.mc-stan.org/u/jsocolar)\
**Post date:** [February 11, 2026, 7:13pm UTC](https://discourse.mc-stan.org/t/how-can-i-found-the-appropriate-family-to-fit-my-data-using-brms/40919/2 "2026-02-11T19:13:13Z")

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Choosing an appropriate response distribution in a regression generally requires one or more of:

- clear domain knowledge that furnishes an empirical or theoretical expectation for the response distribution
- data exploration in the actual data you’ve collected
- iterative model building, criticism, and refinement

To begin, can you describe your prior or theoretical expectations for how the response should be distributed? Is there a theoretical expectation the outcome should be strictly positive? If so, what accounts for negative values in your sample?

And can you provide a plot of the response against the `OConc_ord`, maybe either for one representative `Group` or with separate colors or panels for different `Group` values?